Word problems - Structural effects and defects in folksonomy and linked data approaches to web...

i

Word Problems

Structural effects and defects in folksonomy and

linked data approaches to web document classification.

Dominic Fripp

A dissertation submitted to the University of the West of England, Bristol in accordance with the

requirements of the degree of MSc in Information & Library Management.

Bristol Institute Of Technology May 2010.

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Abstract

The size of the World Wide Web is growing at an incredible pace and many documents online have

little or no structure. Current search and retrieval paradigms focus on sophisticated keyword analysis

on text. An alternative approach is the annotation of documents with descriptive metadata

generated by web users. A further possibility is the automatic annotation of documents using

processes that exploit the semantic web.

This research investigates and compares these two different approaches to web document

classification: Folksonomies and Linked data. The theoretical background of each approach is

discussed in relation to a classification checklist. Three test document groups from Wikipedia are

classified using the techniques and the results analysed.

The two approaches were found to be qualitatively and quantitatively distinct. Folksonomies show

dispersed classification with vague boundaries but exhibit some level of self-organisation that

approaches power law distribution. The relationship between the statistical and semantic properties

of the tags was investigated but no firm conclusions were drawn. There was a suggestion that in co-

occurring tags, there is a linear relationship between pair frequency and semantic distance.

The linked data approach exhibits faceted classification properties and has a highly visual capability

which can be utilised for web navigation.

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Acknowledgements

Many thanks to Tom Tague of Open Calais for the introduction to Semantic Proxy, Kingsley Idehen

for his invaluable insights into the world of linked data and to Paul Matthews for all the advice and

guidance.

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Author’s declaration

I declare that the work in this dissertation was carried out in accordance with the Regulations of

the University of the West of England, Bristol. The work is original except where indicated by

special reference in the text and no part of the dissertation has been submitted for any other

degree.

Any views expressed in the dissertation are those of the author and in no way represent those

of the University.

The dissertation has not been presented to any other University for examination either in the

United Kingdom or overseas.

SIGNED: ............................................................. DATE: ..........................

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Table of Contents

Introduction 1

Background 2

The origins and structure of the research 3

Conceptual Considerations 4

Research process 4

A note on Notation 5

Summary 6

Literature Review 7

Introducing Classification 8

Defining Classification 8

Faceted classification 9

Library origins 9

Current usage 10

A checklist for classification 11

Visualisation 13

Folksonomy 15

Polythetic taxonomies 16

Structure in folksonomies 20

Co-occurrence 20

The problem of homonymy and synonymy 20

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Flatness 21

The Long Tail 22

Power Laws 22

Structural interpretations of power laws 22

Summary 24

Ontologies and linked data 25

A definition of ontology 25

RDF and URI 26

A problem with linked data 28

Summary 29

Research Design 30

Research aims restated 31

Ethical considerations 32

Research instruments for tags 33

Fetching tags 33

Displaying tag hierarchy visually 34

Research tools that use linked data 35

Thomson Reuters Semantic Proxy 35

Visualising metadata using Thinkpedia 37

Keyword generation 38

Document word count 38

Analysing Data 39

Similarity 39

Statistical similarity 39

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Semantic similarity 40

Design walkthrough 42

Document tags obtained from Delicious 42

Keyword analysis 43

Document word count 43

Semantic Proxy document analysis 44

Thinkpedia map 45

Similarity measurement 46

Findings 47

Collection and analysis of test document clusters 48

Tags as subject headings 48

Classification checklist re-examined 50

Analysing the idea plane 51

Differentiation and mutual exclusivity 51

Relevance 51

Homogeneity 54

Analysing the verbal plane 57

Context and currency 57

Analysing the notational plane 59

Synonymy and homonymy 59

Scalability 60

Similarity 61

Comparison of tag frequency and tag semantic distance 64

Interpretation 67

Long Tail 69

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Visualisation of Tag Density in document clusters 69

Power Law Distribution 71

Zipf’s Law 71

80/20 Rule 72

Evidence of power law 73

Conclusion 74

The classification checklist revisited 75

Similarity revisited 76

Structural properties revisited 77

Properties of co-occurrence 77

The long tail 77

Faceted or diffuse classification 78

Visualising the structures 78

Summary 79

References 81

Bibliography 86

Appendices 90

Appendix 1 – 20 document concept maps 90

Appendix 2 – Software research tools 111

Appendix 3 - A notational convention for tags and concepts 114

Word Count: 17, 131

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List of Illustrations

Literature Review

Figure 1.1 (table) – A comparison between three different domains 10

Figure 1.2 – Hierarchical Relationship e.g. Taxonomy 13

Figure 1.3 – Associative relationship e.g. Thesaurus 13

Figure 1.4 – Faceted (multiple hierarchies) 14

Figure 1.5 – Folksonomy (flat and unconnected structure) 15

Figure 1.6 – A top down taxonomy 16

Figure 1.7 – Class relation is logical “part-of” 16

Figure 1.8 – The blurred boundaries of a polythetic taxonomy 18

Figure 1.9 (table) – The classification checklist for folksonomies 24

Figure 1.10 - Ontology (multi-relational structure) 25

Figure 1.11 – Workflow for machine processing text into RDF 27

Figure 1.12 – Calais finds and extracts entities, facts and events 27

Figure 1.13 (table) – The classification checklist for linked data 29

Research Design

Figure 2.1 - Tag cloud for all text in Research Design Section 35

Figure 2.2- Document ontology map for UWE entry in Wikipedia 37

Figure 2.3 – hyponym taxonomy in WordNet 40

Figure 2.4 – Wordnet flexes its homonymic muscles and carves up Turkey 41

Figure 2.5 – Wordle cloud for Delicious tags on Tosca document 42

Figure 2.6 – Wordle cloud of top ten highest frequency words from Tosca document 43

Figure 2.7 – Thinkpedia concept map for http://en.wikipedia.org/wiki/Tosca 45

Findings

Figure 3.1 - Document Cluster EB1 48

Figure 3.2 - Document Cluster GH2 48

Figure 3.3 - Document Cluster IS3 49

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Figure 3.4 (table) – Classification Checklist 50

Figure 3.5 – Relationship between class extraction and document size 52

Figure 3.6 – Relationship between instance extraction and document size 53

Figure 3.7 – Concept map of GH2-20 54

Figure 3.8 - Comparison of Class Occurrence in EB1, GH2 and IS3 55

Figure 3.9 – Tag class occurrence compared to Semantic Proxy class occurrence 56

Figure 3.10 – Top 5 Tag cloud for EB1 cluster 57

Figure 3.11 – Top 5 Tag cloud for GH2 cluster 57





Figure 3.16 - Similarity Comparison EB1 61

Figure 3.17 - Similarity comparison GH2 61

Figure 3.18 - Similarity comparison IS3 62

Figure 3.19 - Tag Pair semantic relation EB1 64

Figure 3.20 – Network map of co-occurring tags of biology and evolution 64

Figure 3.21 - Semantic distance between co-occurring tags in the GH2 cluster 65

Figure 3.22 - Network map of co-occurring tags of greek and history 65

Figure 3.23 - Semantic Distance of co-occurring Tags for IS3 cluster 66

Figure 3.24 - Network map of co-occurring tags of information and science 66

Figure 3.25 - Tag distribution in document clusters EB1, GH2, IS3 69

Figure 3.26 – Tag density for EB1 cluster 70

Figure 3.27 – Tag density for GH2 cluster 70

Figure 3.28 – Tag density for IS3 cluster 70

Figure 3.29 - Term frequency (1st/2nd) compared to Zipf's Law 71

Figure 3.30 - 80 /20 Rule on document clusters 72

Figure 3.31 - Logarithmic Tag Frequency distribution 73

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Conclusion

Figure 4.1 – Checklist successes, failures and inconclusives 80

Appendix 1

Figure 7.1 : EB1-1 - http://en.wikipedia.org/wiki/Apoptosis 91

Figure 7.2 : EB1-2 - http://en.wikipedia.org/wiki/Archaeopteryx 92

Figure 7.3 : EB1-4 - http://en.wikipedia.org/wiki/Bat 93

Figure 7.4 : EB1-8 – http://en.wikipedia.org/wiki/David_Attenborough 94

Figure 7.5 : EB1-11 - http://en.wikipedia.org/wiki/Ernst_Haeckel 95

Figure 7.6 : EB1-17 – http://en.wikipedia.org/wiki/Genetic_algorithm 96

Figure 7.7 : GH2-1 – http://en.wikipedia.org/wiki/Diogenes_of_Sinope 97

Figure 7.8 : GH2-2 – http://en.wikipedia.org/wiki/Ancient_Greece 98

Figure 7.9 : GH2-6 – http://en.wikipedia.org/wiki/Antikythera_mechanism 99

Figure 7.10 : GH2-8 - http://en.wikipedia.org/wiki/Plato 100

Figure 7.11 : GH2-10 – http://en.wikipedia.org/wiki/Alexander_the_Great 101

Figure 7.12 : GH2-15 – http://en.wikipedia.org/wiki/Battle_of_Thermopylae 102

Figure 7.13 : GH2-17 - http://en.wikipedia.org/wiki/Epicurus 103

Figure 7.14 : GH2-20 - http://en.wikipedia.org/wiki/Dionysus 104

Figure 7.15 : IS3-2 - http://en.wikipedia.org/wiki/Nutrition 105

Figure 7.16 : IS3-8 – http://en.wikipedia.org/wiki/Double-slit_experiment 106

Figure 7.17 : IS3-15 - http://en.wikipedia.org/wiki/Akhenaten 107

Figure 7.18 : IS3-17 – http://en.wikipedia.org/wiki/Nuclear_weapon 108

Figure 7.19 : IS3-19 – http://en.wikipedia.org/wiki/Leonhard_Euler 109

Figure 7.20 : IS3-20 – http://en.wikipedia.org/wiki/Salvia_divinorum 110

Appendix 3

Figure 9.1 – The difference between a symbol that represents an object and a 115

{symbol} that represents a [concept]

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Introduction

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In ‘On Exactitude in Science’ Jorge Luis Borges (2000, p.325) writes of an empire in which the art of

cartography achieved perfection. In celebration of such skill and accuracy, a point for point map of

the entire empire was created. Consequently, the map was the size of the empire. For following

generations, who did not hold cartography in such high esteem, the map was useless.

Borges story shows that with more precision comes less pragmatism and this rule is true in the world

of information retrieval. One of the main problems in this branch of study is the indexing and

classification of documents. Reduction of document size is vital for speed of search in a collection

populated with millions of different documents on thousands of subjects. This is also true of web,

where the volume of documents is increasing almost exponentially.

Markov and Larose’s (2007) research shows that the size of the World Wide Web in 1999 was

approximately 150 million pages. In 2007, the size was estimated to be approximately 4 billion

pages with a further 1 million being added each day.

In the early phase of the web, the meaning of the pages was not a required part of the project and

describing the content was left to page developers. Unless they added metadata, the page was

present on the web in an unstructured form.

The expansion rate of documents on the web coupled with this lack of structure has impacted

negatively on the precision and recall of keyword searching techniques.

Background

Keyword searching of web documents is the current paradigm of online information retrieval. There

are a large number of sophisticated search algorithms employed by major branded search engines

that retrieve documents based on the search query. Moreover, the most sophisticated of these

calculate a ranking of the documents, based on which ones it determines to be most relevant. This

weighting is calculated by utilising a variation on the core TDIDF principle (TF = Term Frequency, IDF

= Inverse Document Frequency), a statistical measure based on the query and the size of the

document database. However, as Cheng et al. (2004) notes, the algorithms are not able to identify

cases where different words are being used to describe the same concept (synonymy), or a search

word has more than one meaning (homonymy). As Markov and Larose (2007) observe, these search

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techniques are not concerned with the syntax or the meaning of the words. In essence, the

document is treated as a bag of words where the order has no specific value. In the last few years,

major search engine companies have deployed various strategies to improve recall and relevancy.

Similarity clustering is a technique that utilizes the user’s document selection to improve the

relevancy of the next search. Lux (2008) notes that link analysis is more successful than the simple

term weighting of TFIDF because it looks at the links contained within a document and uses those as

a guide to relevancy by assuming that linked documents are on a similar topic. This small scale

networking of the web drives search models like Page Rank (Lux, 2008).

If metadata could be added to each new web document (a process known as annotation) and added

to all the documents that were already on the web then the potential for the document to be

retrieved would increase. In such an information environment, search engines need not guess as

each document could effectively speak for its own content. To keep pace with the volume of

documents, and for classification to be cost effective, it can be argued that there is a need for these

systems and techniques to be automated. The lesson to draw from Borges cartographers map

parable is deciding what kind of metadata and to what end will it be used.

Specific languages have been developed to help with the discovery and retrieval of web documents.

Dublin Core is a simplified cataloguing language which sets out fifteen main access points to a

document, one of which is related to subject keywords which are equivalent to subject headings in

print classification.

Although designed for easy interoperability between different knowledge domains, metadata

languages like Dublin Core have their heritage in the highly structured document world of print. It is

more likely that most of the documents on the web do not fit this notion of authorship. In addition,

the manual annotation of documents is very expensive (Nagypal, 2005). Even the most structured of

document domains have no formalized access points such as Author or Title. Cooperative document

environments such as Wikipedia challenge the notion of authorship as any user can make a content

contribution. In such an environment user tagging can provide valuable information about what a

document is about.

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The origins and structure of the research

The major motivation for undertaking the research was the lack of connectivity to the library

environment in the literature. Whilst it is understandable that research in the field of information

retrieval would be dominated by studies from computer scientists, the concept of classification is

built into the foundations of library science. With the World Wide Web exerting a burgeoning

influence on information research methods, finding the right information has never been more

important, but never more difficult.

Conceptual Considerations

On the assumption that these technical approaches are beyond a large proportion of library

professionals (including the author), the following design criteria were decided:

1) The research should require no prior specialised knowledge. Any particular knowledge

essential to the mechanics of the research is included within the design.

2) The research should only make use of tools that were freely available on the Web. A list of

software and where to find it is included in Appendix II.

3) The entire project should be reproducible with access to one computer and the internet.

Research process

This research aims to investigate specific aspects of two different approaches to the problem of web

document classification. The first is the co-operative tagging environments of user generated

metadata known as Folksonomies. The second is an automatic annotation process that uses

machine readable languages such as RDF and open information networks known as linked data.

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Initially, the literature review will examine basic properties of classification and argue that concepts

advanced by Ranganathan in the twentieth century have much in common with the modern concept

of classification on the web, particularly to the topic of domain modeling. This initial argument

attempts to explain both the processes in terms of basic classification concepts

Having identified some core notions of classification, a consideration of folksonomies and linked

data tool (Semantic Proxy) will be made, based on the formulation of a classification checklist and a

series of research questions. The checklist predicts the different properties of each approach based

on the core notions already discussed. Three document test groups will be used, based on

Wikipedia documents clustered around the following tags in the Delicious bookmarking site.

(1) Evolutionary biology (referred to as EB1 in the text)

(2) Greek history (referred to as GH2 in the text)

(3) Information Science (referred to IS3 in the text)

The research design looks at the function of the instruments used and the motivation for using

them. The principles supporting data analysis are examined, including a focus on measuring

statistical and semantic similarity. In order to highlight any related weaknesses in the methodology,

the section concludes with a design walkthrough.

The findings will look for statistical and semantic similarities in the classificatory output of the two

approaches. These results will be compared to a keyword indexing of the documents. There will

also be an investigation into what other structural features in the linked data and Folksonomy

metadata can be identified. These structures are then visualized to assess their suitability as a web

navigation aid.

A note on Notation

In the text, all words that denote tags are bracketed by { }. All concepts or classes mentioned within

the text as such are bracketed with [ ]. The need for this notation is discussed in Appendix III.

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Summary

The aims of this research can be summarised as follows.

Research questions

1) Formulate a classification checklist against which the two methods can be evaluated

and compared.

2) How similar are the two approaches?

a) What is the statistical similarity of the metadata?

b) What is the semantic similarity of the metadata?

c) How different are these approaches from a conventional keyword

representation of documents?

3) Do metadata tags show any inherent structure?

a) What are the properties of co-occurring tags?

b) Does Folksonomy metadata exhibit a long tail?

c) Are the classifications faceted or diffuse?

d) How can the structures be visualized?

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Literature Review

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Introducing Classification

Classification lies at the heart of any attempt to organise. Whether it is books on the library shelf,

food along supermarket aisles or genetic sequences in a database, the online world of today

presents fresh challenges for classification schemes. The sheer wealth of data and documents now

available on the World Wide Web are there in unstructured form. The notion of structure in this

example relates to the presence of structural and/or descriptive metadata.

The traditional notion of classification in the library environment arose from concentration on

printed materials and their arrangement within a physical space. However, in the world of digital

documents, there is no shelf (Shirky, 2005). Classification relies on a more descriptive element

(Broughton, 2006), i.e. the subject allocation (Langridge, 1992) that will aid information retrieval.

Recent approaches to document classification employ syntactic rather than statistical approaches to

enable subject analysis. Some techniques have emerged from the burgeoning semantic web project:

the drive to make all web data machine readable. Others have arisen from the boom in web based

social networking. Both of these approaches seem far removed from the classification principles

upon which Ranganathan created his colon classification scheme. However, when some of the

conceptual foundations are considered, there are clear parallels between the classification

programme that Ranganathan laid out and the realm of domain modelling: a core principle in the

design of taxonomies, thesauri, and ontologies.

Defining Classification

To make this relationship more explicit, consider three definitions of classification from Schwarz

(2005):

1. Classifying as a verb is synonymous with domain modelling: the act of grouping together similar or

related concepts and arranging the resulting groups in a logical way.

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2. Classification as a noun is the resulting domain model.

3. A second meaning of classifying as a verb is used in relation to instances. Instances are classified

according to an existing domain model in order to organize them, for example classifying individual

books in a library according to the Dewey Decimal Classification System.

A common example in the literature, (Broughton, (2006), Denton (2003) and Morville & Rosenfeld

(2006) have examples) is classifying wine. Taking that as the domain, the concepts that best

describe wine can be allocated and used as a way of grouping similar properties together. Four

properties that a wine is commonly described by are grape, region, price, year. Instances of wine

are the bottles themselves, which are organized according to the concept schedule. As new

instances of wine occur, the schedule can be extended to ensure that these wines also fall within the

scope of the domain.

Faceted classification

The type of classification scheme being utilized in the wine example is a faceted system. As Denton

(2003) observes, these schemes are used a great deal on large websites to help group products or

services. Anyone familiar with eBay, Amazon, or price comparison websites, is accustomed to

choosing instances from a selection of properties and browsing the results. It is useful to keep these

kinds of examples in mind, as the library equivalent uses vocabulary that makes the whole scheme

sound very different.

Library origins

The chief exponent of this type of scheme in the library context was Ranganathan. In Ranganathan’s

terminology, concepts can be read as facets. It is said that the inspiration for his colon classification

scheme was the toy Meccano (Beghtol, 2008). The implication was that complex objects could be

built up from a finite set of variables, or facets.

Equivalently, for the purposes of classification, Ranganathan argued that any subject, no matter how

complex, could be built from the same set of basic components. His universal system was

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PMEST (Personality, Matter, Energy, Space, Time): a five facet arrangement that focused on the

concepts inherent in the subject matter of documents, rather than the allocation of a single place on

a pre-existing branch of an enumerative structure such as Dewey Decimal Classification (DDC). His

schema was designed to cope with any conceivable document (Ranganathan, 1960).

Current usage

An example can show how these different approaches relate to one another. The FAST (Faceted

Application of Subject Terminology) schema (O’Neill et al., 2001) leverages Library Of Congress (LoC)

subject headings data from bibliographic records and adds them as searchable metadata. Taking

this example, with Ranganathan’s PMEST and the wine domain, the following correlation can be

determined:

FAST facets Ranganathan facets Wine concepts

Topic Personality Grape

Geographical Space Region

Period Time Year

Form Matter Price

Figure 1.1 – A comparison between three different domains

Although the comparison is not perfect, it shows a conceptual similarity to the three approaches and

an attempt to fulfil the criteria of Schwarz’s point 1 regarding domain modelling. Another way of

talking about the number of variables in each domain is to talk of its dimensionality. The PMEST

formula has five dimensions: five degrees of freedom that can change for any description. In the

wine example above, the dimensionality is four.

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A checklist for classification

In his writings, Ranganathan (via Langridge, 1994) outlined a three step approach to document

classification that can be added to Schwarz’s definitions.

Based on the work of the Classification Research Group, Spiteri (1998) has expanded upon each of

these statements. The subdivisions clarify some of the operative processes and criteria through

which the act of classification can be understood. Crucially, it forms a checklist to which the

Folksonomy and linked data classification processes can be compared.

Idea plane - Subject analysis in one’s own words, including form of knowledge, topic, and

any lesser forms that apply.

(a) Differentiation: use characteristics of division (i.e., facets) that will distinguish clearly

among component parts.

(b) Relevance: reflect the purpose, subject, and scope of the classification system.

(e) Homogeneity: each facet must represent only one characteristic of division.

(f) Mutual Exclusivity: the contents of any two facets cannot overlap.

Verbal plane - Examination of the schedules to find the necessary concepts.

(a) Context: the meaning of an individual term is given its context based upon its position

in the classification system.

(b) Currency: terminology used in a classification system should reflect current usage in the

subject field

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Notational plane - Construction of notation for the subject according to the scheme’s rules.

(a) Synonym: each subject can be represented by only one unique class number

(b) Homonym: each class number can represent only one unique subject.

(c) Hospitality: notation should allow for the addition of new subjects, facets, and foci, at

any point in the classification system

Spiteri’s expansion includes many more criteria than shown here. The criteria for inclusion are

based on the relevancy to forthcoming discussion.

Step 1 is the analytical part (performed by person or computer) to Step 3’s synthetic. The analytico-

synthetic (Ranganathan, 1960) is the decon / recon-structive process of facet analysis. Step 2 is the

medium over which this process can take place. The PMEST formula occupies this space in that it

provides the vocabulary by which the classification can be expressed. For wine, Zinfandel, Merlot

and French would be examples of the vocabulary that would be used. In the user generated tag

ecology of the web, English would be a typical domain vocabulary. Step 3 describes the logical

consistency of how the classification is presented. Hospitality is best described as scalability in

modern thinking about the World Wide Web. Scalability is the ability of a system to cope with its

own expansion in size or performance. Interoperability can be understood as a subset issue of

scalability because the ability for systems to work together is an essential part of increasing

performance.

Spiteri’s conclusion is that the language of the classification criteria could be simplified further to

keep classification more accessible and comprehensible. In keeping with this, the steps can be

reduced to the following, more fundamental elements:

1. Document analysis for topics (subjects).

2. Relating topics (subjects) to domain concepts.

3. Expressing the concepts using domain notation.

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term

broader term

Related term

narrower term

prefered term

Figure 1.2

Hierarchical Relationship

e.g. Taxonomy

Visualisation

Broughton (2007) argues that a molecular model is a more suitable modern day analogy for faceted

systems. It is certainly the case that such an analogy does highlight key ingredients of the concept of

facet analysis: the facets themselves, the connections between them, and the notion of laws by

which they combine.

One of the immediate strengths is the pictorial power of the analogy. The multi-directional, three

dimensional plane in which molecules can extend suggests something more free and powerful than

the up/down of taxonomies and additional left/right aspects of thesauri.

The figures above describe visual concepts for taxonomy (figure 1.2) and a thesaurus (figure 1.3). It is

easier to see the notion of dimensionality in these pictorial representations. The taxonomy has a

single, up-down potential for movement whereas the thesaurus has an additional left-right

movement. These are one dimensional and two dimensional respectively.

Figure 1.3

Associative relationship

class

super class

Related term

sub class

prefered term

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Figure 1.4 Faceted (multiple hierarchies)

class

super class

class

sub class

class

A faceted classification scheme can be

visually represented as shown on the left.

Each facet (like the taxonomy in figure 1.3)

has one degree of freedom. As facets can

be combined then n facets have n

dimensions. In figure 1.5, there are three

facets that can move up or down (like the

reels on a fruit machine) and so the

dimensionality is three.

In Broughton’s analogy,

elements can bond along the degrees of

freedom, thereby allowing complex

information structures to be built up.

Extending the analogy still further, if no molecular bonds exist then the concepts or terms are

unrelated and exist in a heterogeneous state. This type of system has no dimensions and no

structure. This is the traditional pictorial representation of the folksonomy.

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Folksonomy

Folksonomy as a term appeared in 2004 (Wikchowksi, 2009), as a result of the increasing popularity

of bookmarking sites such as Delicious. Vander Wal (2007) created this portmanteau word of “folks”

and “taxonomy” to describe the resultant aggregate of tags.

Traditional taxonomies tend to be hierarchical structures, based on a superclass / sub-class

relationship between the units. This type of structure is called top-down and the most well known

library classification schemes Dewey and Library of Congress are both examples of this.

A traditional taxonomy is a top down structure, where each level is subsumed by the one above. The

relationship between levels has to be a logical one. In the diagram below, the logical relation can be

understood as is part of.

Prisoners Dilemma is part of Game Theory / Game Theory is part of Mathematics.

Tag

Tag

Tag

Tag

Tag Tag

Tag

Tag

Figure 1.5 - Folksonomy

(flat and unconnected structure)

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As the pyramid shape indicates, there are typically more subclasses than classes and more classes

than super classes.

Conversely, the act of tagging is a bottom up scheme. A bottom up approach avoids trying to fit

information into a pre-existing schema and looks to classify according to what there is. Floridi (2009)

explains that it is left to the single individual user or producer of the tagged target to choose what to

classify, how to classify it and what appropriate keywords to use in the classification. Crucially, there

need be no logical relationship. This type of classification scheme is called polythetic (Needham,

1975).

Polythetic taxonomies

Polythetic is the criteria of having neither necessary nor sufficient conditions to belong to a

conceptual class. Whether an entity belongs to a class or not is generally based on how many similar

features that entity shares with other group members. It is not a systematic application of a set of

necessary and sufficient conditions of the concept (which is known as monothetic classification).

The argument for this is an extension of Wittgenstein’s family resemblance argument against clear

categorization (Needham, 1975). Take the concept of [sports]: two instances of sports are Luge and

football. It is hard to see what is common between the two instances and, therefore,

Superclass

Class

Sub-Class

Mathematics

Game Theory

Prisoner's Dilemma

Figure 1.6 – A top down taxonomy Figure 1.7 – Class relation is logical “part-of”

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hard to understand the necessary and sufficient criteria by which they both belong to the class (i.e.

are subsumed by) [sports]. Yet it does not seem controversial to refer to both instances as [sports].

The polythetic view is that in a class such as [sports], the number of criteria for belonging is so vast

that there is no reason to believe that all properties would be distributed equally and evenly. As a

result, the class of [sports] would exhibit blurred boundaries.

Beckner (1968, p.22) sets out three criteria to explain how this type of associative classification

schema might work.

1. Each entity possesses a large (but unspecified) number of properties of the class.

2. Each property of the class is possessed by large numbers of these entities.

3. No property of the class is possessed by all of the entities.

Statement 3 is the key, and once this is accepted, then, reductio ad absurdum, the logical necessity

of the relationship between class and sub-class disappears. Sutcliffe (from Gyllenberg and Koski,

1996) argues if that were the case, i.e. there are no necessary conditions for what belongs to a

class, then it makes no logical sense to talk in terms such as class and concept. This argument seems

to miss the point in that there clearly is a problem with the overall concept of classification if it can

support both definitions, but dismissing one of the definitions is only side-stepping the problem. If

Beckner’s polythetic taxonomy is to work then two questions suggest themselves: what constitutes

large? What number of properties is too few to belong to the class? Unfortunately these questions

themselves are subject to the problem of vagueness.

The problem of blurred edges has its origins in Sorites paradox (Hyde, 2005) and the vagueness of

predicates is often blamed. Sorensen (2006) describes vagueness as any concept that exhibits

borderline cases. One perspective is that borderline statements lack a truth-value and are not

classifiable in the traditional sense. Another is that such cases can only be understood if there exists

a range of logical outcomes with values between 0 and 1. As with many philosophical arguments,

neither is conclusive.

any classification technique based

demand) depends on a set of criteria independent of necessary and sufficient conditions.

regards to classification protocols using many valued and fuzzy logic, such work is explored in Zhang

and Song (2006).

The statistical distribution of properties is an imp

classification techniques. Such techniques can also work in the analysis of tags, provided the corpus

is large. Shirky’s mantra - here comes everybody

enterprise is that the numbers count.

more subjective the value of the tags.

folksonomies is this subjectivity and how it necessarily hinders the quest for

system of tags. However, the more people involved in the tagging enterprise, the more aggregated

the folksonomy becomes. The input of many, in theory, produces so many tags that a consensus is

formed. It is true that there is no gua

but it is almost certain that the example she gives, of an image of a black horse being tagged white

horse, would be at the margins of the overall tag spectrum for any entity.

Figure 1.8 – The blurred boundaries of a

polythetic taxonomy

18

Vagueness does give a fresh insight into the

motivation for the principles in the

Ranganathan and CRG clas

checklist. Each part of the recipe seeks to

eliminate vagueness from the classification

process, whether it is the eradication of

homonyms or the correct order for

notation.

Although there is philosophical debate

around these ideas, it is suf

posit the existence of an alternative means

of classification from the traditional

hierarchical design. It could be argued that

any classification technique based on a statistical clustering method (as Beckner’s recipe seems to

ends on a set of criteria independent of necessary and sufficient conditions.


The statistical distribution of properties is an important interpretation for the clustering


here comes everybody - indicates that the value in any crowd sourcing

at the numbers count. The likely reason for this is that the fewer the numbers, the

more subjective the value of the tags. Peterson (2006) claims that the central weakness of

folksonomies is this subjectivity and how it necessarily hinders the quest for consistency within a

However, the more people involved in the tagging enterprise, the more aggregated

The input of many, in theory, produces so many tags that a consensus is

It is true that there is no guarantee to Peterson that this consensus does not carry errors


horse, would be at the margins of the overall tag spectrum for any entity.

The blurred boundaries of a

polythetic taxonomy

Vagueness does give a fresh insight into the

motivation for the principles in the

Ranganathan and CRG classification

checklist. Each part of the recipe seeks to

eliminate vagueness from the classification

process, whether it is the eradication of

homonyms or the correct order for

Although there is philosophical debate

around these ideas, it is sufficient here to

posit the existence of an alternative means

of classification from the traditional

It could be argued that

a statistical clustering method (as Beckner’s recipe seems to

ends on a set of criteria independent of necessary and sufficient conditions. With


ortant interpretation for the clustering


indicates that the value in any crowd sourcing

The likely reason for this is that the fewer the numbers, the

Peterson (2006) claims that the central weakness of

consistency within a

However, the more people involved in the tagging enterprise, the more aggregated

The input of many, in theory, produces so many tags that a consensus is

rantee to Peterson that this consensus does not carry errors


19

Although the complaints by Peterson are valid, they miss the mark because tags aren’t supposed to

be the same as controlled vocabularies and to look at what they’re not is to miss what they are.

Gruber (2005) alludes to the comparison being like comparing an apple and an orange: both are

different examples of a broader descriptive enterprise.

Smith (2008) notes that websites such as LibraryThing are mixing some bottom-up and top-down

structure. Echoing suggestions by Gruber (2005), the TagMash system allows the user community to

weight tags (and thereby increase relevancy) and choose a preferred term from a controlled list of

synonym equivalences. Often, communities are allowed to like / dislike items, creating a self

organised hierarchy for the tags and arriving at a loose but democratically sanctioned controlled

vocabulary.

Tagging is fast paced and ephemeral, which gives it a descriptive advantage over “brittle” (Floridi,

2009) ontologies. Tag language, in this respect is diachronic, i.e. able to change over time. This

change occurs because, arguably, tags represent natural language use (Wichowski, 2009) and

therefore be better suited to natural language search enquiries. Floridi notes that tagging is fully

scalable and that a single tag is adding value as soon as it is created. An example of this would be

tagging documents in an enterprise environment. Specialist fields require specialist vocabularies,

but tags can make an additional semantic layer to aid document retrieval (Smith, 2008). As such,

tagging potentially offers any controlled vocabulary additional granularity. In the example of the

enterprise example given by Holgate (2004), tagging would help assist with the time consuming

process of identifying and classifying key concepts.

Floridi (2009) argues that ontologies have a low degree of resilience. Tagging, when mistaken, does

not cause too much trouble, but ontology is brittle. Ontologies also suffer from a limited degree of

modularity. Every bottom-up tag helps immediately, but systematic, top-down, exhaustive and

reliable descriptions of entities are useless without a large economy of scale.

20

Structure in folksonomies

Co-occurrence

Lalwani and Huhns (2009) formulated and tested three hypotheses that they claim reveals implicit

class-sub class structure in folksonomies. All three make a statement about the properties of co-

occurrence: the phenomenon that some tags appear with other tags more frequently than others.

The threshold of co-occurrence is set by the cardinality, or number of elements in the set (Kunen,

1980).

Lalwani and Huhns found that two of their hypotheses held for their data. The relationship

between co-occurring tags was often a class/sub-class pair. A method to measure this is discussed in

the research design.

Delicious gives a list of tags that co-occur with any given tag. This indicates that whilst it is

theoretically possible for a folksonomy to be completely flat, the reality is that, given a large

community of users, some tags will be more popular than others and some will occur more

frequently than others. From the data Delicious can provide, it is possible investigate these

hypotheses for the Delicious folksonomy.

The problem of homonymy and polysemy

Perhaps the biggest problem that folksonomies face is that of homonymy and polysemy. Without a

set of semantic rules, words such as tank are of indeterminate meaning. Keyword search approaches

will always encounter this problem. Controlled vocabularies, such as a thesaurus tackle this problem

by suggesting preferred terms to avoid confusion.

As tags do not exist in isolation, it is possible that the meaning of bank could be determined by

looking at what other tags are present. A content of a document tagged {bank} can be better

21

assessed by looking at the tags that appear alongside {bank}. For instance, only one further tag

would need to appear (either {finance} or {river}) for {bank} to be disambiguated.

It could be argued that the following tag sequence {film, movie, cinema} are three tags with the

same meaning. Shirky (2005) is adamant that the power of folksonomies is that the three tags are

not equivalent and the value of tags is in their potential to mimic the diversity of language, as

Wichowski mentions.

Flatness

Liu and Gruen (2008) concluded that for the (untrained) human driven ontology development they

measured, the subjects tended to use classes at a common object or instance level. For example,

the class was labelled {chair} rather than {furniture} and sub classes (a specification such as

{armchair}) rarely added. The consequence of this was that the ontologies designed were quite flat.

It could be argued that this flatness is the untrained factor. Speller (2007) refers to this as basic level

variation. Two users, when confronted with a picture of a particular garden bird will tag according to

the depth of their knowledge. Someone with no specialist knowledge may tag {bird} whereas an

ornithologist will tag {robin}.

This is not to say that complex relationships cannot be established between the tags, but that

without the input of some expert domain knowledge, the overall structure tends to remain quite

flat.

Markines et al (2009) propose that the tagging process can be converted into a triple if one takes the

process to involve the subject (tagger) predicate (tagging) object (document). In principle,

folksonomy information converted to this format can be processed like any other triple. By

encoding the subject into the information, there is a potential drawback with regard to privacy. This

will be considered in the research design.

22

The Long Tail

The term “Long Tail” was coined by Anderson (2006). In economic terms, it is the proportion of the

market of items that sells very little individually, but when taken as a whole, sells as much if not

more, than the best selling part of the market.

The music top 40 chart is a good example of this. These are the best sellers of the week, where most

consumer attention is focused and represents a large volume of the overall weekly sales. However,

it is only a small proportion of the total number of titles that will have sold. There could, conceivably,

be 40,000 different titles that had sold at least one unit that week. The top 40 is only 0.1% of that

spectrum. The power of the long tail is in adding all these small sales over a large range of titles

together. Taken en masse, it can amount to a significant proportion of the market value.

Power Laws

An archetype of this type of distribution is often referred to as the “80/20” law, or the Pareto

Principle (which was formulated specifically in terms of economic wealth and ownership). Burrell

(1985) has noted that this heuristic best explains how much of a library’s stock the majority of library

users consult. A corollary of this rule for information seeking behaviour is described by Mann

(1986). Based on the work of Zipf (1965), Mann postulates the principle of least effort: that an

information seeker will stop at the earliest available point at which the information requirement is

thought to have been met. That is to say that an information search is rarely continued to find more

relevant or more accurate information.

If tagging follows a similar pattern of human behaviour then the flatness and density patterns of the

folksonomy could be predicted. This is because the principle of least effort implies that taggers will

apply the same tags that have been previously applied. This could explain the presence of a power

law such as 80/20 since the principle creates a few high frequency tags.

Structural interpretation of power laws

The long tail, in this context, is made up by the tags which only appear once or twice. As such,

within the folksonomy of delicious, they are bookmark tags that have been applied by one

23

person only. Shirky (2005) would argue that the more interesting associations and classifications are

being made in this part of the tagging schema. There is no doubt that this is true in the sense that as

single tags applied by single authors, they represent a purely subjective judgment and the notion of

preferred terms has entirely disappeared. One of the problems with this diffuse vocabulary is that it

limits the document findability as there is no correspondence between search terms and tags. Using

Shirky’s example, {movie} people may not want to hang out with {film} people but a {film} student

might be interested in documents tagged by {movie} people. Morville (2005) highlights the paradox

of such tagging behaviour, perhaps better termed anti-social networking.

This natural language problem is highlighted indirectly by the work of Zipf (1965). His law of natural

language frequency states that while only a few words are used very often, many or most are used

rarely. His law suggests that a second item occurs approximately 1/2 as often as the first and the

third item 1/3 as often as the first, and so on. As tagging comes from natural language, it is possible

to expect that, given no other ordering principle, the tag frequency might fit a similar pattern.

Although power laws seem mathematically simple and easy to spot, Clauset et al (2009) set out a

rigorous set of statistical tests to help verify whether data really is obeying a power law. It is

important to bring such mathematical rigour since the visualization of a long tail often appears to be

a good fit yet may not necessarily be obeying a power law.

Bollen and Halpin (2009) conclude that whether there are tag suggestions or not, the tag frequencies

follow a power law. Social bookmarking sites such as Delicious use a tag suggestion interface to help

users with their bookmarking. Their analysis shows that the distribution was formed more clearly

when the interface was not used. This recent result indicates that a natural language folksonomy

can be predicated to aggregate in this way in spite of vocabulary controls, not because of them.

24

Summary

Following from this discussion, the classification checklist can be reiterated with a prediction about

the properties the folksonomy approach should have.

Classification Plane Criteria Folksonomy

Idea plane Differentiation No

Relevance Yes (by aggregation)

Homogeneity No

Mutual exclusivity No

Verbal plane Context No

Currency Yes (natural language)

Notational plane Synonym Possibly (Statistical)

Homonym Possibly (Statistical)

Scalability Yes (any document can be tagged)

Figure 1.9 – The classification checklist for folksonomies

25

Ontologies and Linked Data

A definition of ontology

There are plenty of definitions of ontology in the literature. Noy and McGuiness (2001, p.1) state

that an ontology defines a common vocabulary for researchers who need to share information in a

domain.

Legg (2007, p. 407) adds a further ingredient: “A formal ontology is a machine-readable theory of the

most fundamental concepts or “categories” required in order to understand information pertaining

to any knowledge domain.”

The readability is a key platform in the semantic web project. Tim Berners-Lee has argued that most

information on the Web is designed for human consumption and that the Semantic Web approach

develops languages for expressing information in machine processable form (Berners-Lee, 1998).

There are many documents on the web that can be read by humans. The semantic layer in which

they operate resides in the interaction between the language of the document and the reader.

Floridi (2009) notes that since semantic content in the Semantic Web is generated by humans,

ontologised by humans, and ultimately consumed by humans, promoting the notion of a machine

intelligible web should come with a caveat.

Figure 1.10 - Ontology (multi-relational structure)

Object

Subject

Object

Object

Object

Subject

Object

26

The problem seems to revolve around the use of the word semantic - especially as its use may

conjure up the image of computers reading and understanding documents in the same way that

humans would. This is a philosophically dangerous area and, according to many thinkers, implies a

type of artificial intelligence that has already been discredited.

Floridi’s objection to this implied artificial intelligence (and the problematic consequences thereof)

can be avoided if the limit of machine comprehension is taken to mean the navigation of a

document using clearly defined concepts in clearly defined machine languages. Avoidance of the

phrase “semantic web” is advisable to avert any unnecessary process implications. The literature

considered below focuses on part of the Semantic Web project: linked data.

RDF and URI

In the linked data environment, concepts are powered by RDF triples and URIs. RDF (Resource

Description Framework) triples are tripartite expressions of relationships between different concepts

based on a subject-predicate-object construction. URIs (Uniform Resource Identifiers) provide a

fixed description of the subjects and objects. Within the whole ontology of the linked data cloud,

the identifiers should be unique to ensure consistency over the whole domain (as per the

prescription of Ranganathan’s notational plane).

The power of linked data is the exposure of the data (anchored to URIs) to other resources that, in

turn, can link to other data. This intermeshing of different data sets creates hyperdata (Idehen,

2009): a direct data correlation to hypertext.

Linked data is also able to deal with the information retrieval problems of polysemy and homonymy.

Polysemy is not an issue providing all equivalent concepts point to the same URI. Thus synonym

rings are infinitely extendable so long as the information target is the predefined entity indicator.

For example, if film is the URI then the following are conceptually equivalent if anchored to the same

URI. Film = cinema = movies = flicks = featurepresentation = fiml = cine = and any other possible

equivalence that can be defined.

Homonymy is handled in much the same way. Bank can be defined by its target URI. The relational

properties by which bank can be defined should distinguish between the side of a

27

river and a financial institution. In the design walkthrough in the following design section, Semantic

Proxy differentiates between Tosca the opera and Tosca the character by examining the context of

use based on suitable triples and URIs.

Semantic Proxy is part of the Calais initiative, powered by Thomson Reuters. The OpenCalais Web

Service automatically creates rich semantic metadata for the content that is submitted. The

unstructured document is investigated using natural language processing (NLP), machine learning

and other methods. Calais analyzes the document and finds the entities. It returns the facts and

events hidden within the text by extracting relations based on data from the Linked Open Data (LOD)

cloud. The relations are then turned into RDF.

Left: Figure 1.11 – Workflow for machine

processing text into RDF (Byrne, 2009)

Right: Figure 1.12 – Calais finds and extracts

entities, facts and events (Calais, 2008)

28

The two images on the left (Byrne, 2009) and the right (Calais, 2008) show the same process in

parallel for converting text into RDF.

The metadata output of Calais can be built into maps that link documents to entities, facts and

events. These links can improve site navigation, and provide contextual detail on the document

content. It is worth noting that these named entities, facts and events can be compared to the

facets of Ranganathan’s PMEST formulation but on a grander scale.

If this kind of output seems far removed from the type of metadata generated by tagging then

Semantic Proxy solves this by providing social tag metadata. This is to enable organisation of the

document based on topic, rather than the entities extracted.

This is a considerable simplification of the linked data cloud, and the processes employed to extract

RDF, but it is sufficient as background in order to explain what Semantic Proxy is doing to a

document and how it arrives at its output. The importance of datasets in the task of classification is

the creation of triples, each one with the potential to connect objects to the structural and

descriptive metadata of the identifiers. From these building blocks, databases can build a large

ontology that will be consistent as the data is interrelated.

A problem with linked data

Byrne (2009) notes that this is no simple task since the arrangement of data within a database is not

necessarily one which lends itself to the creation of RDF. One of the main problems is the legitimacy

and coherency of the data itself. Kelly (2010) has identified a weakness with the datasets (specifically

DBPedia, which is the open database of information contained in Wikipedia) in terms of its capacity

to answer a human entered query.

When one occurs, it is simple enough to discard an inconsistent tag (even if Peterson (2006) does

not believe so) but ontologies are more complex structures with many interlocking pieces. Kelly’s

example highlights how easy it can be to uncover a problem. The conversation in the linked data

community shows how it is not so easy to correct.

Although the implication is that results from the LOD cloud should be treated with caution, the aim

of this research is not to answer specific queries such as Kelly's but to investigate whether

29

the LOD cloud can provide adequate classification of web documents. Currently, the Semantic Web

is work in progress and its capabilities and achievements subject to some hyperbole. This tendency

is identified particularly in criticisms by Floridi (2009) and Kelly (2010).

The reliability of the LOD cloud will only and can only be as good as the data that is made available

to it. For improvements in performance, particularly in the classification process, there need only be

access to the correct schedules. Knowledge architecture such as DDC and LoC is already coherently

structured. If such frameworks were available for relational query, then it would be feasible to

predict the automated subject allocation based on the machine reading of documents. Work in this

area is being conducted by Green and Panzer (2009) and Wang (2009).

Summary

Following from this discussion, the classification checklist can be reiterated, with a prediction about

the properties that the linked data classification approach should have.

Classification Plane Criteria Linked Data

Idea plane Differentiation By URI

Relevance Entity extraction

Homogeneity By ontology

Mutual exclusivity By ontology

Verbal plane Context Yes (by entity extraction)

Currency Yes (at open data level)

Notational plane Synonym Logical by URI

Homonym Logical by URI

Scalability Dependent on coherency and

interoperability of LOD cloud

Figure 1.13 – The classification checklist for linked data

30

Research Design

31

The following chapter looks at the process of selecting the research methods used for the study. An

assessment of the needs of the research aims is made, followed by a critical consideration of what

research methods are appropriate. A selection of methods is examined from research literature to

assess strengths and weaknesses of the approaches. Ethical issues are also evaluated.

The chapter continues with a discussion of why the research tools used were chosen and how these

instruments provided data for analysis.

There follows an evaluation of different data analysis methods and a discussion of what techniques

would be most suitable.

Finally, there is a detailed design walkthrough with step by step analysis to highlight any other issues

not covered in the preceding discussion.

Research aims restated

1) Formulate a classification checklist against which the two methods can be evaluated and

compared.

2) How similar are the two approaches?

a) What is the statistical similarity of the metadata?

b) What is the semantic similarity of the metadata?

c) How different are these approaches from a conventional keyword

representation of documents?

3) Do metadata tags show any inherent structure?

a) What are the properties of co-occurring tags?

b) Does Folksonomy metadata exhibit a long tail?

c) Are the classifications faceted or diffuse?

d) How can the structures be visualised?

32

Ethical Considerations

The nature of tagging is closely connected with the idea of social networking and, hence, online

identity. Social bookmarking has been a web phenomenon in the past ten years and sites such as

Delicious are dependent on participation from web users.

Due to the nature of the online tagging environment, user data is freely available from the

bookmarking websites such as Delicious, Technocrati and Bibsonomy. This raises an ethical question

of using data without consent. This area is not considered at all in the literature reviewed so far. It

would be reasonable to hypothesise that the very nature of a tag means that it is available for use

without need to obtain prior consent. The issue of consent is considered in Denscombe (2007) and

Hewson (2003) but only in the context that information obtained online is data that is identifiable as

personal and traceable back to a data source. This is not necessarily the case in online social

networking, where it is assumed that information is contributed freely. The terms and conditions

made available on Delicious indicate that the tags, once created, are available for public scrutiny.

Equally, there is a concomitant privacy statement that makes a user aware of the protection of their

personal information from other service users (although not the service provider).

The conclusion is that user generated metadata is an ethical grey area. It is likely that there is no

discussion of the issue because, ultimately, tags are processed in research anonymously. The

technical process of collecting tags from social bookmark sites can be automated using a

combination of RSS feeds and Yahoo Pipes modules. This is the approach taken by Oldenburg et al

(2008). A pipe has been designed that extracts only tag information and removes any user specific

data to ensure anonymity.

Equivalently, this research is only interested in the tags as they relate to the documents and to other

tags, not to the users who created them in the first place. This does not have to be the case.

Markines et al (2009) has proposed that tagging data can be converted into a machine readable

form by turning the relationship into the RDF triple format that identifies the subject, the entity and

the tag. Encoding the subject in this way does have ethical ramifications if the subject has not

consented to be included.

Exposing personal data in this way manifests itself on the semantic web as FOAF (Friend Of A

Friend.) This is a machine readable ontology built from relationships between people that point to

URI’s such as personal blogs, email addresses and any other personal information. The ethical

33

impact of these developments is beyond the scope of this research but could provide an interesting

objective for further research.

Research Instruments that use tags

Fetching tags

There is an inherent ethical responsibility in collecting tags because they are user generated.

Delicious is a social bookmarking site and the bookmarking of service users can be explored. In the

context of the research, the demographics of the users themselves are not important: only the tags

themselves are. The tags are collected in a manner that completely devolves them from the

individual.

This research uses three sample document groups from Wikipedia. They were collected via Delicious

by taking the first twenty documents with ten tags or more containing the following tags:

EB1 – Evolution, Biology, Wikipedia (20 documents)

GH2 – Greek, History, Wikipedia (20 documents)

LS3 - Information, Science, Wikipedia (20 documents)

The inclusion of the Wikipedia tag was to assist with the document collection. Delicious allows the

bookmarking of any web page with any single term tag. One of the initial opportunities to test the

classificatory powers of its folksonomy was to measure if the three tag strings returned only

Wikipedia documents on the topics suggested by the first two tags. This process appears to favour

Delicious as a classifier, but the resulting harvest of documents would highlight any weaknesses in

the folksonomy, particularly if a non-Wikipedia document was collected. This would validate

Peterson’s (2006) criticism of folksonomies.

In all but one case, the tags investigated were only those that had been placed on the collected

documents by Delicious users. The exception was the analysis of co-occurring tags. The data was

extracted by first ascertaining the total number of times the base term appeared in the folksonomy.

The co-occurrence frequency of the tag pair was then determined.

34

Displaying tag hierarchy visually

Hearst and Rosner (2008) weigh up two sides of opinion about tag clouds – firstly, that it is an

innovative and informative way of analysing data and secondly, that they are a triumph of form over

function.

On the plus side, a tag cloud is a good way of representing quantity as size and, in tag population

terms, importance.

On the debit side, most tag cloud programs do not cluster terms semantically. This would help in

comparing the frequency of similar concepts in a document. As with Ranganthan’s Meccano or

Broughton’s molecular analogy, a visual representation can show relationships between entities, in

addition to any statistical metric.

This representational technique is employed in the research as a means to show the distribution of

tags in terms of the number of individual tag terms that appear (the amount of words in the cloud)

and the frequency of each tag, or the size of the word in the cloud. The higher the frequency of the

tag, the larger the word in the cloud. Figure 2.1 shows the tag cloud for the full text in this section of

the research (the colour scheme is of no significance). The cloud quantitatively demonstrates clearly

what might only occur as an intuition during the reading of the text – that the words ‘research’

‘tags’, ‘documents’ and ‘data’ occur more often than some others.

The cloud also gives a way of looking at a lot of data at the same time, although the positioning and

distance between the tags is not a representation of any quantitative or qualitative measure.

One of the aims of this research is the calculation of semantic distances between tags, especially

frequently occurring tag pairs. The process of how this is achieved is described below. Here, it can

be hypothesised that calculating the semantic distance between words that appear in the cloud

could, in principle, achieve such an ordering. Semantic clustering is evaluated in the conclusion.

35

Wordle is used in the research for its eye catching qualities, font range and ability to display words at

angles. The essential factor of the tag count, i.e. the weighting is conferred by the size of each word

within the tag cloud.

The weaknesses of Wordle are visible in the example above. The lack of stemming is evident in the

appearance of terms such as ‘tag’ and ‘tags’. A further anomaly is the appearance of ‘research’ and

‘Research’, an occurrence that highlights the severe semantic index limitations of this particular

visual strategy.

Figure 2.1 Tag cloud for all text in Research Design Section

Image generated using Wordle (http://www.wordle.net/)

36

Research tools that use linked data

Thomson Reuters Semantic Proxy

Calais is a project by Thomson Reuters designed to help realise Berners-Lee’s vision of a machine

readable web and is part of the linked data community. Its Semantic Proxy venture aims to translate

the content of any URL on the web to its semantic representation in RDF, HTML or Microformats

(Calais, 2008). Primarily designed to be used by machines, Semantic Proxy does provide the

information in a way that humans can understand too. This makes it the ideal tool to reveal the

processes of entity extraction using linked data. In addition, Semantic Proxy aims to emulate human

metadata creation by adding social tags to any document it processes.

The Calais service is, by its own admission, tailored towards the online media and enterprise

markets. This explains some of the choice of facets, or entities that it extracts from a document.

This angle also explains the inclusion of a top category in the results. Online news environments such

as those provided by the BBC and Yahoo feature such broad classes as ‘Entertainment’. However,

the list of facets should be as scalable as the open data sets permit and it can be argued that

appropriate facets to the library and information environments could be included when the

concomitant datasets are made available for inquiry.

The process by which it works has direct parallels with the three step document classification

program outlined in the literature review. Firstly, Semantic Proxy analyses the document for entities

that belong to particular classes as defined by the supporting ontology. Secondly, based on the

frequency of the appearance of these entities and the words around them, the software makes a

calculation of relevancy of the instance to the concept and the document overall. Thirdly, the

instances are expressed in terms of their related classes available through the ontology. Semantic

Proxy can identify all of the following and more:

Person, Company, Medical condition, Position, Natural feature, Social tag, Province or state,

Organization, City, Continent, Facility, Country

By sorting the entities into facets such as these, Semantic Proxy creates a type of semantic index, a

portable ontology of the document as determined through entity extraction via the LOD cloud. Web

documents can be fed into the program via their URL. Semantic Proxy analyses and presents a

detailed report on the entities it has identified and extracted from the document, plus a set of social

tags it hypothesises as being relevant to the document. It is these tags that are compared to the

Delicious tags in the similarity analysis in the findings.

37

Figure 2.2

Document ontology map for UWE entry in Wikipedia.

Visualising metadata using Thinkpedia

Thinkpedia was developed by Christian Hirsch in 2008 and uses the semantic information leveraged

by Semantic Proxy and fed through Thinkmap software to create a visual graph of the document

metadata. The thickness of the line between the main classes and the instances indicate the

relevancy of each piece of metadata to the document. Each instance in each class is interactive,

making Thinkpedia a navigational tool, not just between document metadata arrangements, but

between the instances and classes themselves. This permits document clustering around a

particular instance or class, in addition to the topic.

The main facets of the document are the dots closest to the central dot. Rather than the five of

Ranganathan’s PMEST formula, there are thirteen classes identified in the data output. The size of

the dot is the statement of its relevancy to the document. It is easy to spot the more relevant facets,

although lesser facets are also included. This keeps the dimensionality of the structure high but the

metadata rich. Thinkpedia queries Semantic Proxy and Thinkmap by entering any Wikipedia URL.

38

Keyword generation

A list of the top ten keywords for each document is calculated for each document using Tag Crowd

(http://tagcrowd.com/) and used as a bench mark for the two other processes.

The reasoning is that if there is a high similarity between a simple term frequency analysis of the

document and the analysis by human generated tags and linked data, then it can be argued that the

metadata the techniques generate do not add much value to a document in terms of search and

retrieval.

Conversely, a low correlation of similarity suggests that the processes under investigation provide

more content richness than a simple statistical assessment of the words in each document.

Tag Crowd has a stemming option and provides a list of the 100 most frequently occurring words in

English.

Document word count

Polaris word count software was used to calculate the size of each Wikipedia document. The

software worked from an input of the document URL.

It was not ascertained in the course of the research if the word count included the words within the

navigation bar that appears to the left of the screen when viewing Wikipedia entries.

39

Analysing the data

Similarity

One of the research questions makes explicit the need to compare two sets of classification data and

establish how similar they are. Similarity is, therefore, the main feature of the analysis and requires

formalisation here. What is tacit in the research aim is to find a criterion for similarity to be judged.

For similarity to be measured, it has to be quantified. Lin (1998) outlines three underlying intuitions

he feels are present in our perceptions of similarity.

Intuition 1: The similarity between A and B is related to their commonality. The more commonality

they share, the more similar they are.

Intuition 2: The similarity between A and B is related to the difference between them. The more

differences they have, the less similar they are.

Intuition 3: The maximum similarity between A and B is reached when A and B are identical, no

matter how much commonality they share.

Although the statements seem trivial, they are important for the task of formalising the concept of

similarity in the two senses that are required for the research.

Statistical similarity

To analyse the similarity of two sets of tags the Dice co-efficient was selected.

� ��, �� 2 |�� |

|�| � |�|

The value on the top of the right hand side (RHS) is twice the intersection of sets A and B. The co-

efficient is then calculated by dividing the intersection by the sum of the set cardinalities. In this

case, that value is numerical. The equation permits S (A, B) to be a maximum of 1 and a minimum of

0. From intuition 3, S (A, B) = 1 is identical and S (A, B) = 0 is totally dissimilar.

An example can best make the Dice index clear. Consider the following sets of tags:

40

A = {history, architecture, Greece, mythology, Turkey} – cardinality = 5

B = {ruins, greek, history, politics, turkey} – cardinality = 5

The intersection of A and B is 3, i.e. {history, history}, {Greece, greek}, {turkey, Turkey}

Greece and Greek are taken to be a similar tag pair, on the stemming principle of indexing.

The Dice coefficient for A and B, S (A, B) = 2 x (3) / (5+5) = 0.6

Semantic similarity

The package used was written by Pederson and Michelizzi, and performs twelve semantic

relatedness measures including those set out by Resnik (1995) and Lin (1998). The software queries

the Wordnet English lexical ontology, which divides word associations into synsets. This includes the

linking words and word senses that help resolve problems of hypernomy, synonymy and homonymy

(Blanken et al, 2004). The metric used in this research is node counting. Nodes are the word blocks

themselves, rather than the links between them which are commonly referred to as edges (see

figure 2.3). This ensures that any equivalent (or synonymic) words have a similarity of 1, as the block

they are in counts as one. The graph from Simpson and Dao (2005) makes the concept of node

measurement clear.

Figure 2.3 - hyponym taxonomy in WordNet

Node length between car and auto is 1, car and truck is 3, car and bicycle is 4, car and fork is 12.

41

The driving force for creating a measurable value for semantic relatedness is to enable that value to

be machine readable. Without such, it would be unrealistic to expect relationships between

different domains to be calculated. This is a matter of interoperability: envisaging a process by

which formal language and structure in one domain can be compared to another domain and the

semantic similarity calculated. Similarly, in information retrieval, retrieving documents containing

highly related concepts are more likely to have higher precision and recall values.

The caveat that must accompany this comparison of concepts is exemplified well by the capabilities

of this software. What does it mean to have six or seven different methods of measuring semantic

relatedness and how do we interpret seven different values for the comparison of the same two

words? This problem is evaluated in the conclusion.

A concomitant issue is underlined in Legg (2007). She notes that, as in the example above, a tag pair

such as {Turkey, turkey} has homonymic ambiguity that cannot be resolved by semantic distance if

the two words are the sole data input. It is likely that humans employ an association heuristic in

such a case by looking at the words that occur with {turkey} and {Turkey}. This heuristic can be

machine deployed with the assistance of the Wordnet ontology.

Figure 2.4 – Wordnet flexes its homonymic muscles and carves up Turkey

Disambiguation performed on Javascript Visual Wordnet

42

Design walkthrough

In order to make the mechanics and outputs of the design more explicit, this section concludes with

a design walkthrough. The idea is to show the measurement and analysis of data provided by a trial

document. With any luck, trying the methodology with one document will flag up any problems

with the overall design before undertaking the main data capture and analysis.

Any further design considerations that have not appeared above are indented into the main text.

The walkthrough is also an opportunity to utilise tag clouds as a representational method. The

walkthrough concludes with a similarity calculation between the metadata sets of the document.

Test document – Tosca entry on Wikipedia, available at: http://en.wikipedia.org/wiki/Tosca

Document tags obtained from Delicious

Tag List Tag Cloud

Tag Uses Rel. %

opera 7 29

wikipedia 4 17

tosca 2 8

Puccini 2 8

music 2 8

theatre 1 4

wiki 1 4

teater 1 4

composers 1 4

music_opera 1 4

ooper 1 4

operas 1 4


Design Problem 1: Self-reflexive Tags

Is {Wikipedia} a valid tag to analyse? The nature of Delicious – a bookmarking service –

means that some tags will be solely applied for navigation purposes. In one sense all

tags fulfil this function though. It would seem extreme to remove the second most

featured tag in this instance, as it would also distort the quantitative balance between

the top tags and the tail. One of the founding principles of folksonomies is the

democracy of the tags so they will be included in the analysis and any potential skewing

of the data can be identified in the findings.

Figure 2.5 – Wordle cloud for Delicious tags on Tosca document

43

Keyword Analysis

Top ten words that appear in the document (with their frequency) are:

act (16)

angelotti (19)

cavaradossi (33)

edit (12)

mario (15)

opera (25)

puccini (14)

sacristan (12)

scarpia (40)

tosca (59)

Document Word Count

Polaris word count: 3,432

Design Problem2: Typos or Non-English

The methodology will evaluate tags such as {teater, ooper} as they appear. Analysis

indicates that {teater} could be a Scandinavian spelling of the symbol for the concept

[theatre]. Likewise, translation software suggests that {Ooper} is the Estonian language

symbol for the concept [opera]. As this research is concerned with English language

tags only, these two tags could be discarded. Equally, the tags could be misspellings of

{theatre} and {opera} and could be conflated with their English language counterparts.

As it is not possible to establish the correct reason for their presence, they should be

taken as separate tags. Folksonomies depend on this type of tagging for their semantic

richness and their inclusion may prove important when assessing the syntactic quality

and semantic value of the long tail.


Figure 2.6 – Wordle cloud of top ten highest frequency words from Tosca document

44

Semantic Proxy document analysis

Tags

Entertainment Culture (socialTag)

Tosca (socialTag)

Operas (socialTag)

Instances and Classes

Rome (City), Italy (Country), Roman prison (Facility), food (IndustryTerm), pain (MedicalCondition),

Alps (NaturalFeature), Mario falls (NaturalFeature), Austrian-Russian army (Organization), Floria

Tosca (Person)

Note the limitations of this approach in the NaturalFeature class, which has included the instance of

“Mario falls” (a [Person]GenericRelation term). It is also worth noting that the relevancy score was

7%, which does not correct the mistake in itself, but does discount it from appearing within the

visual metadata record shown below.

45

Thinkpedia Map

Class count: eight

([Person], [industry term], [position], [social tag], [province/state], [city], [facility], [country]}

Instance count: 33

[person] – 13, [industry term] – 1,[position] – 3,[social tag] – 3

[province/state] – 1, [city] – 7, [facility] – 1, [country] – 4

Figure 2.7 – Thinkpedia concept map for http://en.wikipedia.org/wiki/Tosca

46

Similarity measurement

Social Tags: {operas}, {Tosca}, {Entertainment_Culture}

Delicious Tags: {opera}, {wikipedia}, {tosca}, {Puccini}, {music}, {theatre}, {wiki}, {teater},

{composers}, {music_opera}, {ooper}, {operas}

Keywords: act, angelotti, cavaradossi, edit, mario , opera, puccini, sacristan, scarpia, tosca

Tags common to Social tags and Delicious tags = 2 i.e. {operas, Tosca}

S (Delicious tags, Social tags) = 2 x (2) / (3 + 12) = 4 / 15 = 0.27

S (Delicious tags, keywords) = 2 x (1) / (12 + 10) = 2 / 22 = 0.09

S (Social tags, keywords) = 2 x (1) / (3 + 10) = 2 / 13 = 0.15

Result: Social tags and Delicious tags most similar for this document.

47

Findings

48

Figure 3.1 - Document Cluster EB1

Figure 3.2 - Document Cluster GH2

Collection and analysis of test document clusters

The first challenge of the Folksonomy or distributed classification system was in the document

selection. The documents were selected from Delicious by searching for the first twenty URLs that

contained the three different tripartite tags.

Tags as Subject Headings

As with the FAST programme discussed by O’Neill et al (2001), the choice of tags acts as a set of

subject headings. This is not to say that the tags are faceted. They are not predefined to belong to a

particular concept (like [topic] or [location]). However, if the documents collected through the three

tags are of a similar subject then it can be argued that the folksonomy can effectively cluster

documents around three terms.

1) Diogenes_of_Sinope, 2) Ancient_Greece, 3) Ovid, 4) Heraclitus, 5) Democritus, 6)

Antikythera_mechanism, 7) Aristotle, 8) Plato, 9) The_Hedgehog_and_the_Fox, 10)

Alexander_the_Great, 11) Greco-Buddhism, 12) The_Myth_of_Sisyphus, 13)

The_Birth_of_Tragedy, 14) Greek_gods, 15) Battle_of_Thermopylae, 16) Greek_words_for_love,

17) Epicurus, 18) Ptolemy, 19) Sophocles, 20) Dionysus

1) Apoptosis, 2) Archaeopteryx, 3) Atavism, 4) Bat, 5) Bioinformatics, 6) Clade, 7)

Convergent_evolution, 8) David_Attenborough,9) E._coli_long-term_evolution_experiment, 10)

Epigenetics, 11) Ernst_Haeckel, 12) Ethology, 13) Eusociality, 14) Evolution, 15)

Evolutionary_biology, 16) Evolutionary_psychology, 17) Genetic_algorithm, 18) Island_gigantism,

19) Lamarckism, 20) Lazarus_taxon

49

1) Schmidt_Sting_Pain_Index, 2) Nutrition, 3) Apophenia, 4) Information_theory,5) Psychokinesis,

6)Entropy, 7) Game_theory, 8) Double-slit_experiment, 9 ) Quantum_entanglement, 10) Cymatics,

11) Simulated_annealing, 12) Kolmogorov_complexity, 13) Scale-free_network, 14)

Psychoacoustics, 15) Akhenaten, 16) Ultimate_fate_of_the_Universe, 17) Nuclear_weapon, 18)

Sense, 19) Leonhard_Euler, 20) Salvia_divinorum

Figure 3.3 - Document Cluster IS3

The initial point to be made is that all documents retrieved with this method were Wikipedia

documents. Although this was expected, it was not a stipulation within the methodology, i.e. to

retrieve the first 20 documents with over ten tags that fitted the criteria of containing all three tags.

This is a small but clear success and offers a crumb of comfort to Peterson (2006) and any others

wary of malicious tagging. The consistency and accuracy of a bookmarking system should be

expected otherwise it is of no use, even at the subjectivity level of a single user.

An initial qualitative overview of the document topics suggests that the EB1 and GH2 document

clusters are more successful than IS3. One of the problems with tags in the context of the

classification checklist is that they are non-commutative. Unlike the facet analysis of a schema such

as PMEST, the order in which the tags appear does not offer any further syntax. It is likely that this

lack of commutativity is a main cause of the borderline categorization of some of the Wikipedia

documents retrieved. {Information} and {science} is not the same as {information, science} or

{information_science}. The latter is a case that Kuropka (2005, p. 4)) terms a word group – a special

group of words that has a different compound meaning than is indicated by the meaning of the

parts. Prime examples of word groups are proper names (such as New York) and nicknames (the Big

Apple). Although {information, science} is not so extreme an example, there is an argument that the

word group has a particular meaning, related to but substantively narrower than the meaning of the

terms independently. In figure 3.3, documents IS3-1, IS3-2, IS3-16 and IS3-17 are examples of

borderline cases on the topic of information science.

Conversely, this is the type of diffusion for which Shirky champions folksonomies. It is positive

evidence that tagging does produce a type of distributed classification (Speller, 2007) that exhibits

the type of vague properties outlined in the literature review. In other words, the tags on the

boundary (of lowest rank) are not classified as narrowly as those at the top. The exact structural

nature of the tags will be discussed below. Word groups will feature in the discussion on tag co-

occurrence in a later section.

50

Classification checklist re-examined

The literature review outlined and argued for the existence of a classification checklist. The list was

composed of key concepts involved in the process of classification. It is reproduced in figure 3.4

below.

Classification Plane Criteria Linked Data Folksonomy

Idea plane Differentiation By URI No

Relevance Entity extraction Yes (by aggregation)

Homogeneity By ontology No

Mutual exclusivity By ontology No

Verbal plane Context Yes (by entity

extraction)

No

Currency Yes (at open data level) Yes (natural language)

Notational plane Synonym Logical by URI Possibly (Statistical)

Homonym Logical by URI Possibly (Statistical)

Scalability Dependent on

coherency and

interoperability of LOD

cloud

Yes (any document

can be tagged)

Figure 3.4 – Classification Checklist

51

Analysing the Idea Plane

Differentiation and mutual exclusivity

These two concepts are important for any faceted classification system as the rules forbid the

overlapping of any concepts. Taking the wine example, a description of a wine would not make

reference to location and region as this might involve duplication of information, making one of the

concepts redundant. However, region and country are distinct and follow a classic ‘part of’ logical

relational hierarchy.

Semantic Proxy entity extraction is based on the querying of RDF triples to discover semantic

information about words in a document. Therefore, because of the logical nature of these triples,

Semantic Proxy will sort document contents into clear and distinct classes.

In the three document clusters, Semantic Proxy extracted all entities into the following classes:

city, company, continent, country, currency, electronics, entertainment / award / event, facility,

industry term, market index, medical condition, medical treatment, music album, music group,

natural feature, operating system, organization, person, position, product, programming language,

published medium, region, sports event, state / province, technology, social tag, movie, radio

station, TV show.

Relevance

It could be argued from the concept list above that Semantic Proxy extracts entities into classes that

may not seem specifically relevant to the overall document topic. The class headings tend to betray

the modus operandi of Semantic Proxy as an automatic generator of semantic metadata about

people, companies, events and relationships. The product is geared towards enterprise and this

might go some way to explaining the choice of classes that it extracts into. However, as the highest

possible dimensionality for any document being processed by Semantic Proxy is 30 (six times greater

than Ranganathan’s PMEST formulation) then the chance of relevant entities being extracted are

higher.

This expectation is also supported by looking at the relationship between the number of classes and

instances detected when compared to the document size, or word count.

The class / document size relationship is quite diffuse, as can be seen in figure 3.5.

52

The scatter of the values does not suggest a trend in terms of how many classes are found. The

result is encouraging, as it suggests that Semantic Proxy always uses a finite set of classes for

analysis. This is useful if the technique is deployed as a faceted classification system and shows that

most documents contain at least one or two of these classes. This corresponds with Ranganathan’s

condition that the classes for a faceted scheme must be as fundamental as possible.

0

2

4

6

8

10

12

14

16

18

0 5000 10000 15000 20000 25000

Nu

mb

er

of

Cla

sse

s e

xtr

act

ed

fro

m D

ocu

me

nt

Number of words in Document

EB1 Corpus

GH2 Corpus

IS3 Corpus

Linear (EB1 Corpus)

Linear (GH2 Corpus)

Linear (IS3 Corpus)

Figure 3.5 – Relationship between class extraction and document size

53

Instance extraction to document size offers a slightly better correlation and this would match the

intuition that there would be more entities in a larger document than a smaller one.

The weakness of Semantic

Proxy approach can be

understood by looking at the

concept map for GH2-20

(figure 3.7). The

predisposition for extracting

entities relating to enterprise

collapses in the identification

of companies. It is not clear

how companies whose name

contains Dionysus are

connected to the topic of the

document itself. This could be

because the process itself is

not context sensitive at the extraction level. Normally the conext would be provided by the set of

0

20

40

60

80

100

120

140

0 5000 10000 15000 20000 25000

Nu

mb

er

of

inst

an

ces

ex

tra

cte

d f

rom

do

cum

en

t

Number of words in Document

EB1 Cluster

GH2 Cluster

IS3 Cluster

Linear (EB1 Cluster)

Linear (GH2 Cluster)

Linear (IS3 Cluster)

Figure 3.7 – Concept map of GH2-20

Figure 3.6 – Relationship between instance extraction and document size

54

social tags generated by Semantic Proxy. However, no social tags were generated in GH2-20. The

most logical explanation is that Semantic Proxy is a beta testing product and comes without

guarantees in terms of output.

For the Delicious folksonomy, it could be argued that the more a tag appears, the more relevant it is

to the topic. The data suggests that the top terms are often relevant to the topic of the document;

moreso than some of the lower appearing terms that tend to reflect the individual tastes and

motivations of the tagger. One example that supports this comes, once again, from GH2-20. The top

five tags are {mythology}, {Dionysus}, {religion}, {wikipedia}, {bacchus}, and it would be hard to argue

that these was not pertinent to a document about Dionysus. The bottom five tags are {atheism},

{booze}, {dad}, {classics}, {debra}. The tag relevancy is often lower the lower the frequency but not

exclusively so. {Classics} and {atheism} look like important tags, certainly as important as anything

else in the top five, especially as both tags represent distinct concepts at a precise class level. It

should also be stated that content relevancy is not the same as navigational relevancy. A tag

guarantees relevancy to the tagger at the very least. The same guarantee cannot be assumed for

automatic annotation.

Homogeneity

It is not totally clear from the work of Broughton (2006), Spiteri (1998) or Ranganathan (1960)

himself exactly what homogeneity means in the classificatory sense.

In order to analyse the data, homogeneity has been taken to mean that all the components are the

same. From the terms expressed in the literature review, one might expect a homogenous system

to be made up of a narrow set of classes only or instances only. It could be argued that this includes

groupings such as nouns only. An ontology driven classification system would be expected to

display orderly characteristics such as these. A converse situation would be predicted for

folksonomies or heterogeneous systems. The opposite of uniformity and order would be expected

here.

Homogeneity was assessed by measuring the number of classes recognised in each document

cluster. If the classes were of similar quantity there would be argument for uniformity of the

Semantic Proxy approach.

55

From figure 3.8, there is some grouping around key classes identified in the text. Although there are

smaller bars evident (especially from the IS3 document cluster), the y-axis scale highlights the low

value, particularly when compared to the most commonly occurring classes. The combined class

count for the three clusters is 30.

The tags data from Delicious could not be compared in the same way. This highlights the non-

faceted nature of the Delicious folksonomy. One quantitative measure utilised to show the distinct

nature of the classification techniques was to analyse the document cluster tags through Semantic

Proxy: in order to find correlations between classes identified in figure 3.8 and the tags. The results

are shown below in figure 3.9.

0

50

100

150

200

250O

ccu

rre

nce

Fre

qu

en

cy

Class

EB1

GH2

IS3

Figure 3.8 - Comparison of Class Occurrence in EB1, GH2 and IS3

56

EB1

Tag

Occurrence

Class

Occurrence

Ratio

Overall

technology 6 47 0.13 Occurrence Ratio 8.5%

medical condition 2 38 0.05

industry term 4 52 0.08

position 1 62 0.02

GH2

country 2 114 0.02 Occurrence Ratio 5.6%

position 5 93 0.05

industry term 2 9 0.22

IS3

industry term 9 150 0.06 Occurrence Ratio 9.9%

medical

condition

1 32 0.03

technology 3 30 0.10

position 1 56 0.02

The data supports the research by Liu and Gruen (2008) – that humans do not create highly

structured ontologies with their tags. The likely outcome is a set of classes all at the same level. The

fact that these terms do not fit comfortably with the approach adopted by Semantic Proxy is

indicative of how diffuse the class range is. The evidence in table 3.9 shows that tags are mapped

onto only five classes, although there are over 400 distinct tags in each document cluster.

This lack of similarity between class nouns found by Semantic Proxy and those used by taggers does

not rule out that a folksonomy cannot be faceted. It does suggest that the motivation between class

identification in the two approaches is different. It is possible that this distinction allows the two

vocabularies to be complementary as they are identifying different aspects of the document. This

possibility relies on neither approach being exhaustive.

Figure 3.9 – Tag class occurrence compared to Semantic Proxy class occurrence.

57

Image generated using Wordle (http://www.wordle.net/) Image generated using Wordle (http://www.wordle.net/)


Analysing the verbal plane

Context & currency

These two terms are inter-dependent. By using the most up-to-date language that a system permits

(the currency) then the context with a given knowledge domain should follow.

If the folksonomy exhibits such characteristics then the tags should be relevant to the content of the

document and represent concepts appropriate to the content of the document. As discussed in the

relevancy section, the frequency of tag terms may say something about the relevancy although it is

by no means the case that all infrequently occurring tags are not relevant.

Top five tags for each document cluster

Figure 3.10 – Top 5 Tag cloud for EB1 cluster Figure 3.11 – Top 5 Tag cloud for GH2 cluster

Figure 3.12 – Top 5 Tag cloud for GH2 cluster

58

Image generated using Wordle (http://www.wordle.net/) Image generated using Wordle (http://www.wordle.net/)


Bottom five tags for each document cluster

The visualisations confirm that in the high rankings, some terms occur with noticeably higher

frequencies. In the lower ranks, the tag occurrence is more evenly distributed and hence more

appear in the tag cloud.

It is likely that tag frequency is used as a rule of thumb, a heuristic to suggest relevancy and context.

The data does not suggest that any stronger conclusion can be drawn. An interesting result in the

top five tags is that {Wikipedia} is a dominant tag in each group. The frequency does suggest that a

bookmark folksonomy has its foundations in web navigation rather than any deeper classification

strategy. This should be compared with the presence of {WIKIPEDIA} in figure 3.13. It appears

because it is in capital letters whereas {wikipedia} in the top tags are lower case. This shows the

synonymic weaknesses of the folksonomy.

Figure 3.13 – Top 5 Tag cloud for GH2 cluster Figure 3.14 – Top 5 Tag cloud for GH2 cluster

Figure 3.15 – Top 5 Tag cloud for GH2 cluster

59

A caveat should be stated that the information displayed here is true in snapshot in time. The

diachronic nature of folksonomies means that popularity of terms can go up and down and

structural weaknesses can potentially be corrected. This constant flux should also reflect the

democracy of language (as with Zipf’s law) and so, given time, the currency of a folksonomy should

include the latest terminology.

Before discussion turns to the investigation of the long tail later on in the section, it is worth

observing that frequency only suggests context on the statistical scale. This is to ignore the unique

tags that exhibit context to the individual tagger. This one-to-one relationship cannot be dismissed

but it is difficult to see how such singular contextual relationships might help an information seeker

such as the film student discussed in the literature review. The one-to-one tags are unlikely to form

part of the language used in a search query.

Analysing the notational plane

Synonymy and homonymy

There is evidence in the data that the Delicious folksonomy does have the capability of dealing with

synonymy on a statistical level. If it assumed that human taggers are, as Floridi states, the only

known semantic engines in existence, then the decision to disambiguate terms is part of the

reasoning process of tagging. There is no evidence in the tag data that points to any problems in

disambiguating terms, although it should be stated that the documents under investigation are not

on topics that are clearly subject to homonymic problems. In terms of synonymy, the scalability of

tags ensures that any equivalent word can be used as a tag and therefore will be active as a search

term in a query. The drawback is that the Delicious Folksonomy does not aggregate synonymic

terms, thereby not increasing the core (or preferred) term frequency. As has been stated already,

there is nothing more than a slight correlation between frequency and relevancy in the documents

analysed so this is not a big problem.

Semantic Proxy individuates all instances according to the class list discussed earlier. There were no

cases in which homonymy or synonymy was evident, although the documents analysed were not on

topics where this was likely to happen. Taking an example from EB1-14, Semantic Proxy can easily

identify and extract China to the class [country] by examining the text around which the term

appears: in this case “Great Wall of China”.

60

Scalability

The prediction is that folksonomies are scalable because of what Floridi describes as the instant

value that they can convey to any document to which they might be attached. Thus what is

considered one of the main weaknesses of Folksonomies - their heterogeneous nature, discussed

above – is a major strength in terms of scalability. The sheer range of different tags presented in

figures 3.13, 3.14 and 3.15 indicate the diversity of the tag ecology. This diversity is helped by the

fact that the top ranked tags for any document appear more frequently. This phenomenon is

explored in further detail in the power law distribution findings.

For the Semantic Proxy, scalability is limited by the number of subject-object-association

relationships that lie within the document text. From the data regarding class and instance

occurrence as compared to document size, there are encouraging results that point towards the

scalability of Semantic Proxy. Although it is clear that the amount of instances is generally

proportional to the amount of words, the amount of classes is consistent and carefully limited. The

drawback is the relevance of the entities extracted from the document. Although the amount of

classes is manageable, they might not always be appropriately linked to the subject matter.

Similarity

The research question asked for a comparison between three different data groups.

with the method laid out in the research design, three groups of data were selected and analysed:

the data that was outputted as Social Tags from Semantic Proxy, the document tag list extracted

from Delicious and the top ten most frequently

0.00

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61

The research question asked for a comparison between three different data groups.



from Delicious and the top ten most frequently occurring keywords (calculated using Tagcrowd).

8 9 10 11 12 13 14 15 16 17 18 19 20

Document NumberFigure 3.16 - Similarity Comparison EB1

Sim (Tags, Social Tags)

Sim (tags, keywords)

Sim (Social Tags, Keywords)

8 9 10 11 12 13 14 15 16 17 18 19 20

Document NumberFigure 3.17 - Similarity comparison GH2


Sim (Tags, Keywords)


The research question asked for a comparison between three different data groups. In accordance



occurring keywords (calculated using Tagcrowd).

Similarity Comparison EB1


Sim (tags, keywords)


Similarity comparison GH2


Sim (Tags, Keywords)


62

The results shown above in figures 3.16, 3.17 and 3.18 suggest that in terms of statistical similarity

measurements the tags and keywords from each document group show no high similarity trends.

Although there are single examples where there is a higher similarity between tags and keywords

(such as EB-15 and IS3-14), there are more occasions when the tags are totally dissimilar, i.e. with a

value of zero. There is no Dice measurement over 0.4. The isolated green lines in the GH2 chart

indicate that on some documents there was a generally higher correlation between social tags and

keywords.

This result was not unexpected when some of the conditions of the research tools are considered.

There was often no statistical match between Semantic Proxy’s social tags and the tags in Delicious

because of the preponderance of syntactic tags created by Semantic Proxy. These are multi-word

descriptive phrases (often referred to in the literature as n-grams (Markov and Larose, 2007). There

is no such capability within Delicious where only a single word can be entered. This limitation occurs

in both statistical and semantic analysis methods because the resultant comparison value is zero.

This is a corollary of Kuropka’s Word Group problem. In IS3-8, there are the Delicious tags

{double_slit} (ranked 18) and {experiment} (ranked three) compared to Semantic Proxy outputs

{double slit experiment}. Due to the limitations of the research tools, these tags cannot be treated

as the same. Even a Folksonomy evangelist such as Shirky may feel that the intention of the taggers

is clear: that is, to tag it as {double slit experiment}. The restrictions of the tag input do not permit it

however.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Sim

ila

rity

Document NumberFigure 3.18 - Similarity comparison IS3

Sim (Tag, Social Tag)

Sim (Tag, Keyword)

Sim (Social Tag, Keyword)

63

The semantic comparison is redundant since the software cannot handle anything other than single

words, typically nouns and some verbs. It cannot handle adjectives or tag noise (tags in figures 3.

like @toread, @mentat and BLC09 are examples of noise). This non measurability has implications

for the tag co-occurrence analysis outlined below. This section contains a more detailed

consideration of the n-gram / Word Group problem.

The discrepancies between Semantic Proxy social tags and Delicious tags also highlight the problem

of folksonomy flatness. Taking the results of GH2-19 as an example, the social tags are specific

names, entities extracted from the text {Esmene}, {Creone}. The tags added by users maintain

general terms such as {person} and {people}. This is the same effect as predicted by Speller (2007).

However, this lack of specialist knowledge is not reflected in all the tags. The technical language

present in many of the IS3 social tags is reflected in the content of the user tags.

64

Comparison of tag frequency and tag semantic distance

Using the first two tags of the document clusters, ten co-occurring tags were extracted from

Delicious. The semantic distance (node count) was expressed in relation to the frequency of the co-

occurrence.

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0 5 10 15 20

Ta

g c

o-o

ccu

rre

nce

fre

qu

en

cy r

ati

o

Semantic Distance (node count)Figure 3.19 - Tag Pair semantic relation EB1

Co-occurrence with Biology tag

Co-occurrence with Evolution tag

Figure 3.20 – Network map of co-occurring tags of biology and evolution

65

0

0.02

0.04

0.06

0.08

0.1

0.12

0 5 10 15

Ta

g c

o-o

ccu

ren

ce f

req

ue

ncy

ra

tio

Semantic distance (node count) Figure 3.21 - Semantic distance between

co-occuring tags in the GH2 cluster

Co-occurrence with Greek tag

Co-occurrence with History tag

Figure 3.22 - Network map of co-occurring tags of greek and history

66

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0 5 10 15

Ta

g c

o-o

ccu

ren

ce f

req

ue

ncy

ra

tio

Semantic distance (node count) Figure 3.23 - Semantic Distance of

co-occurring Tags for IS3 cluster

Co-occurrence with Information

tag

Co-occurrence with Science tag

Figure 3.24 - Network map of co-occurring tags of information and science

67

All verbs that appeared in the tag pairs were converted into nouns so that the similarity

measurement would work. The majority of synsets defined on Wordnet are nouns (ten times more

defined than verbs (Wordnet, 2010). Due to the limitation of “is-a” hierarchies, Wordnet

predominately works with "noun-noun", and "verb-verb" parts of speech (Simpson and Dao, 2005).

Therefore tags were stemmed or reformulated to ensure that a successful measurement could be

taken.

Compound nouns like "travel agent" will be treated as two single words via tokenization. With the

techniques available, further tokenization beyond n=1 cannot be performed. In this way it is

possible to anticipate certain problems with some data. This, in general is one of the main problems

with the type of measurement being performed.

All semantic measurements that returned a nil value (impossible under the terms of node counting)

are represented by any point on the y-axis.

Interpretation

Lawali and Huns (2009) predict that commonly occurring terms are semantically close. This is not

particularly borne out by the data analysed here. Closely related tags (i.e. those that share a node

count of 2 or 3) occur more in GH2 and IS3 than EB1.

GH2 data set is the least scattered result and hints at a broader hypothesis: semantic distance is

inversely proportional to co-occurrence frequency. Therefore, tag classes are more closely related

the more that they occur together.

Although this is an appealing conclusion, an important caveat must be stressed. The problems with

taking any measurements at all indicate that any results provided by this technique should be

treated with caution. The lack of precision, coupled with the enforced changes to the terms

measured, creates an artificial environment where the results are altered to fit the method.

Each cluster shows results on the axes, indicating a bad result where either the semantic distance

could not be calculated (zero value) and/or the co-occurrence frequency was negligible (approaching

zero).

Kuropka’s (2005) thesis concerning the uselessness of co-occurring terms is not overturned or

supplanted in this analysis. The idea of the co-occurrence and semantic relatedness is appealing in

that it provides a stable metric for deciding how semantically similar terms are based on a (machine

readable) statistical measurement. Unfortunately, this is not supported by the data.

68

In the light of discussion in the research design about clustering tag clouds around semantic

distance, it must be concluded that unless a technique is capable of handling the semantic value of

any tag, the capability is going to be limited. However, this isn’t to say that it would have some

success with certain terms. An application of this in social networking is considered in the

conclusion.

69

Long Tail

The distribution of tags in each of the data sets was analysed. Along the x-axis is tag rank. Rank is

dependent on number of uses. Delicious provides the top thirty tags and this data was extrapolated

for each of the documents. The data is already ranked in frequency order with the most commonly

appearing tags ranked top. First impressions convey a promising match to a long tail structure.

Visualisation of Tag Density in document clusters

By assigning an incremental shade to the tag frequency at each rank and using the tag rank as the

radius of the circle, the density pattern in each document cluster can be displayed. In the following

illustrations, the graph lines in figure 3.25 can be best understood as colour intensity.

0

10

20

30

40

50

60

70

80

1 5 9 13 17 21 25 29

Av

erg

ae

Ta

g F

req

ue

ncy

EB1

GH2

IS3

Figure 3.25 - Tag distribution in document clusters EB1, GH2, IS3

Tag rank

70

This method of analysis was chosen

to highlight some of the structural

claims made in the literature review

about folksonomies.

The technique aims to extract

distribution data from the long tail

measurements and express this as a

series of concentric circles in order

to show the relative tag density in

the document cluster.

A visual representation for tag

distribution is useful in the same way

that Ranganathan’s Meccano or

Broughton’s molecular analogy is

useful: it shows structural

information of how metadata is

arranged. It also corresponds with

intuitions about vagueness and fuzzy

boundaries that were discussed in

the literature review.

It suffers from the same drawbacks.

As with a word cloud (Hearst and

Rosner, 2008), there is no clear

explanation of why the pattern is the

way it is. Aside from indicating the

predominance of relatively few

terms, there is no structural syntax

concerning how these groups of

terms might combine. They are

interesting but isolated schematics

of tag distribution, although the outer

layers may be a good representation of

Campbell and Fast’s (2006) pace layers.

Tag

density

Tag

density

Tag

density

Figure 3.26 – Tag density for EB1 cluster

Figure 3.27 – Tag density for GH2 cluster

Figure 3.28 – Tag density for IS3 cluster

71

Power Law Distribution

Zipf’s Law

In their simplest forms, both Zipf’s Law and the 80/20 rule are simple to test. Zipf’s Law states that

in a natural language, the second most commonly occurring word will appear half as much as the

most commonly appearing. To see if the tag distribution of the three document clusters

corresponds to this law, the term with the highest frequency is compared to that with the second

highest frequency.

From fig 3.29 it can be concluded that tags do not follow Zipf’s Law. Apart from on a couple of

occasions, the frequency fall off rate is lower than 0.5. The wider implication is that a folksonomy is

not a precise microcosm of a natural language. One way of interpreting that result is that tags

represent something more than the statistical average of standard natural language and contain a

more focused distribution of terms.

0.00

0.20

0.40

0.60

0.80

1.00

1.20

0 5 10 15 20 25

Ra

tio

of

fre

qu

en

cy (

term

1:

term

2)

Document Number

EB1

GH2

IS3

Zipf's

Ratio

Figure 3.29 - Term frequency (1st/2nd) compared to Zipf's Law

72

80/20 Rule

The 80/20 rule can be checked in the same way. For each document, the number of tags that

represented 20% of the total count of tags applied to each document was calculated. For example,

for a document that was labelled with 30 different tags, the first six tags represented 20%. From

these tags only, the number of their occurrences was added together and compared to the total

number of all tag occurrences. If the data obeys the rule, then this comparison value should be 0.8.

From fig 3.30, it is clear that none of the document clusters obey the 80/20 rule. In fact, there are

no instances in any of the clusters where the rule holds. However, as discussed in the literature

review, the 80/20 or Pareto Law has its origins in economics. It is an example of a power law but not

the only one that might hold. It is important to use one of the laws to make a best guess in this

research but it does not rule out the presence of a power law.

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Ta

g C

ou

nt

/ F

req

ue

ncy

Ra

tio

EB1

GH2

IS3

80/20

Rule

Document Number

Figure 3.30 - 80 /20 Rule on document clusters

73

Evidence of Power Law

Noting the concerns and strictures outlined by Clauset et al (2009) the research uses a simple

logarithmic (base 10) derivative from the tag frequencies. The power law relationship would reveal

this derivative as a straight line.

Figure 3.31 - Logarithmic Tag Frequency distribution

The graph indicates that no strict power law is in place. The makings of such a relationship are

there, and it should be noted that, in support of the work by Bollen and Halpin (2009), tag

distribution in a cluster the size of only 20 documents shows a significant trend towards a power

law. In this case, there is a potentially interesting principle by which a folksonomy can avoid

synonymic and homonymic problems. If tag frequencies always tend to this type of distribution, then

the tags will impose their own democratic hierarchy. Under these circumstances, preferred terms

will always appear.

0

0.5

1

1.5

2

2.5

3

3.5

1 6 11 16 21 26

Log

10

Ta

g F

req

ue

ncy

EB1

GH2

IS3

Tag Rank

74

Conclusion

75

This concluding chapter will revisit the research questions. The strengths and weaknesses of the

answers will be assessed from a theoretical and practical perspective. Opportunities for further

study will be suggested.

The classification checklist revisited

The aim of proposing the checklist was twofold. Firstly, it was an important object on which to hang

a great deal of the theoretical discussion. Secondly, it was conjectured that the processes could be

investigated within the research design.

One of the major problems with this approach was the selectivity issue. It is one thing to argue that

modern classification techniques share something of the original prescribed method of classification;

it is another thing altogether to assert the similarities in a precise language.

Terms such as homogeneity and currency (as Spiteri (1998) herself notes) are more likely to cloud

thinking on the subject than clarify it. This research has set out to demystify some of the more

arcane terminology of classification that arose from Ranganathan’s thinking over sixty years ago and

which, according to Beghtol (2008), wasn’t properly understood by his most brilliant

contemporaries.

Further (and necessarily deeper) research into the history and evolution of the terminology used by

Ranganathan and then the CRG may have helped explain the peculiarities of some of the

terminology. Unfortunately, this would also distance the project from the core reason for

introducing the terminology in the first place: that of showing that the key concepts were alive and

well in the thinking of modern practices.

In terms of the classification concepts that were selected (i.e. the ones that made the most sense)

the selection bias was towards faceted classification. It is demonstrated in the findings that the

Delicious folksonomy is not a good fit for many of the processes described. Although this is a valid

conclusion of the research and a prediction from the literature review, it says more about what a

folksonomy isn’t and therefore lacks penetration as a research tool.

A summary of what was discussed in the findings about the checklist is as follows:

The folksonomy functions successfully as a clustering classification strategy, showing only a small

proportion of borderline cases in the initial document harvest.

76

Semantic Proxy behaves like a faceted classification scheme, creating a total dimensionality of all

documents of 30. The number of instances that the software detects and extracts is in proportional

agreement with the number of words that the document contains. Semantic Proxy doesn’t add

classes in the same way but limits the number (in the same way that PMEST keeps the descriptive

variables limited to five). The drawback is an artificial classification of some instances, often into the

classes [company names] and [positions]. It is hypothesised that this is due to the way that open

data is queried in the LOD cloud and that many of the RDF relations and URIs have an enterprise and

news flavour. The implication is that classification is always a subjective process, despite first

appearances.

Similarity revisited

The similarity measurement part of the research design should be classified as an honourable failure.

Much of the research into ontology development with tags including work by Markines et al (2009),

Mase (2009), Nagypal (2005) and Van Damme et al (2007), all argue that tags can augment and go

some way to constructing new ontologies. This is true, particularly in the approach seeded by

Gruber (2007) concerning this amalgamation, neologised as folksontologies. The core idea is that

tags and taggers become tripled, between themselves, the tag they create and the target of the tag.

This work is cutting edge, in the sense that utilising distributed classification supports the thesis that

typical ontology design is time consuming and expensive.

This area of practical research is beyond the technical capabilities of this work, although the findings

do pick up on some potential problem areas that such research has to consider. The major hurdle is

the difficulty in calculating semantic similarity and, if obtained, interpreting the results. As

demonstrated by the software used, there are many possible similarity measures and this research

concludes that the metric to pick is the one that best suits the purpose.

Despite the difficulties involved in the process, the research did make some findings.

Statistically and semantically, there is no better and sustained similarity between any two tag

outputs. One reason for the low scores is the presence of n-grams (or word groups) in the Semantic

Proxy output. This is a problem since neither Delicious tags nor keywords appear in any larger size

than a 1-gram. The research tools are not able to compare tags of varying syntax.

77

Structural properties revisited

This part of the research was more successful than the previous. Although routed in quantitative

analysis, it was also an opportunity to explore the visual properties of both classification techniques.

The major drawback of the research design is that the structural properties of the techniques are

taken in isolation. This is always a danger if, as the research predicts, the two techniques have

completely different structural properties.

Properties of co-occurrence

In light of the research by Lawlani and Huhns (2009) and the problems identified by Kuropka (2005),

the main objective was to investigate whether there was a clear relationship between tag co-

occurrence frequency in the Delicious folksonomy and the semantic similarity of the tags. From the

data analysed, it was concluded that no such relationship exists. Parts of the data did show that

frequently co-occurring tags do exhibit a close class / sub-class relationship (a semantic distance

between one and four), particularly in the GH2 and IS3 document clusters. Further to this, GH2 did

exhibit a more focused set of results that hinted at, in that document cluster at least, a proportional

and linear relationship between semantic distance and tag pair frequency.

This is an important result for the idea of semantic clustering. An example from Twitter can make its

potential clear. To aggregate tweets on a similar topic, users can include a hash tag (#), which gives

the tweet machine readable metadata. This is important in finding content according to topic and

extends the parameters of keyword searching on the social network by creating information streams

around categorisations. Once a user is locked onto a topic in this way, they might want to look for

similar topic streams. This could be solved by aggregating different hash tags and measuring the

semantic distance. Semantically close hash tags are likely to be on a similar topic and of potential

interest to the user on the social network. The possibility of reorganising social network information

in this way is a possible, topical, and interesting area for further study.

The long tail

The research concluded that, according to even the small (but dispersed) data samples used, tags on

documents are organised into a long tail structure. Two specific power law paradigms were applied -

Zipf’s Law and the 80/20 (Pareto’s) Rule – but found not to apply to the data. Although there was a

suggestion that the data was close to being organised according to a power law, the strict conditions

78

set out by Clauset et al (2009) prohibit any suggestion of such without further rigorous mathematical

analysis.

Faceted or diffuse classification

The focus on faceted classification is due to its predominance on the web. The research aim was to

make this explicit in the literature review and draw on common examples from the web to back up

the claim. What was lacking from the research design was a clearer focus on how the concept maps

generated in Thinkpedia are an extension of online retail paradigms such as those on eBay and

Amazon. The linked maps of classes and instances gives a new means of document navigation that

exploits the same principle as hyperlinks in web documents. The mimicking of hyperlinks between

semantically similar information spaces is exploited by Mase (2009) with topic maps.

Visualising the structures

Appendix 1 contains the 20 most ‘interesting’ concept maps from the sixty documents available. The

selection criteria were maps that featured the most classes and instances. The output of Thinkpedia

is compelling in viewing faceted classification as a molecular system, as Broughton (2007) proposes.

The other elements of the molecular metaphor such as the notion of bonds as syntax are also

present. In terms of Broughton’s final analogy, it seems correct to state that the RDF triples, and the

associations provide the rules by which the bonds occur. To temper the success of the analogy it

should be qualified that Semantic Proxy did produce some generic relation errors and did not

produce social tags for some documents. This is wholly permissible from software at beta testing

stage.

It is worth adding here that the concentric ring graphs for the tag distribution in a document cluster

also helps explain key concepts, such as the pace layering of Campbell and Fast (2006) and the

philosophically complex idea of vague boundaries. The qualification, as with the faceted concept

maps, is that visualisation is a useful explanatory device, and although grounded in the data, does

not give any fresh quantitative insight.

79

Summary

1. The classification checklist

The research found that a checklist was a useful lens under which the two classification

techniques could be scrutinised. The strategy was successful in helping draw theoretical

strands of classification together to make some predictions about what features each

technique would display.

The main drawback was the issue of selectivity. Only a small portion of the possible

properties of classification were explored. The checklist was a weak lens for examining the

folksonomy and is better suited to faceted schemes.

2. Similarity

This was a difficult concept to define and measure. Due to the large populace of competing

metrics in the field, it was a complex task to choose the best way of measuring similarities

between document tags. The measurements suggested that there was no pattern of

similarity between social tags, tags and keywords that indicated one approach was better

than the other. The research design was not able to provide a suitable measurement for n-

grams (or word groups).

3. Structure

Analysis on co-occurring pairs suggested that there is a correlation between frequency and

semantic distance on some pairs. There was not enough evidence to suggest the correlation

was a rule.

Graphical interpretations on tag frequency and density suggested that the tags in the

Delicious folksonomy obeyed a power law distribution. It was established that neither Zipf’s

nor Pareto’s law held for the data studied. Further rigorous mathematical analysis would be

required to discover whether there was a genuine underlying power law.

80

Visualisation techniques found that the Delicious folksonomy exhibited a vague edge and a

dense core for all three document clusters studied. This showed that documents were

frequently tagged with the same few tags. The tags at the edges were very infrequent and

more diverse than the core tags.

The arrangement of entities in the Thinkpedia concept maps correlates strongly with

Broughton’s (2007) molecular interpretation of classification. This indicates that the

Semantic Proxy process is a high dimension, faceted classification scheme. It also provides a

novel way of navigating documents. It also provides an automated method of metadata

annotation and hyperdata markup.

Below is the classification checklist one final time. The colour scheme indicates how

successful the research was at analysing each property.

Criteria Linked Data Folksonomy

Differentiation By URI No

Relevance Entity extraction Yes (by aggregation)

Homogeneity By ontology No

Mutual exclusivity By ontology No

Context Yes (by entity extraction) No

Currency Yes (at open data level) Yes (natural

language)

Synonym Logical by URI Possibly (Statistical)

Homonym Logical by URI Possibly (Statistical)

Scalability Dependent on coherency

and interoperability of

LOD cloud

Yes (any document

can be tagged)

Figure 4.1 – Checklist successes failures and inconclusive

81

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90

Appendix I

20 document concept maps

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Figure 7.1 : EB1-1 - http://en.wikipedia.org/wiki/Apoptosis

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Figure 7.2 : EB1-2 - http://en.wikipedia.org/wiki/Archaeopteryx

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Figure 7.3 : EB1-4 - http://en.wikipedia.org/wiki/Bat

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Figure 7.4 : EB1-8 – http://en.wikipedia.org/wiki/David_Attenborough

95

Figure 7.5 : EB1-11 - http://en.wikipedia.org/wiki/Ernst_Haeckel

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Figure 7.6 : EB1-17 – http://en.wikipedia.org/wiki/Genetic_algorithm

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Figure 7.7 : GH2-1 – http://en.wikipedia.org/wiki/Diogenes_of_Sinope

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Figure 7.8 : GH2-2 – http://en.wikipedia.org/wiki/Ancient_Greece

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Figure 7.9 : GH2-6 – http://en.wikipedia.org/wiki/Antikythera_mechanism

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Figure 7.10 : GH2-8 - http://en.wikipedia.org/wiki/Plato

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Figure 7.11 : GH2-10 – http://en.wikipedia.org/wiki/Alexander_the_Great

102

Figure 7.12 : GH2-15 – http://en.wikipedia.org/wiki/Battle_of_Thermopylae

103

Figure 7.13 : GH2-17 - http://en.wikipedia.org/wiki/Epicurus

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Figure 7.14 : GH2-20 - http://en.wikipedia.org/wiki/Dionysus

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Figure 7.15 : IS3-2 - http://en.wikipedia.org/wiki/Nutrition

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Figure 7.16 : IS3-8 – http://en.wikipedia.org/wiki/Double-slit_experiment

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Figure 7.17 : IS3-15 - http://en.wikipedia.org/wiki/Akhenaten

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Figure 7.18 : IS3-17 – http://en.wikipedia.org/wiki/Nuclear_weapon

109

Figure 7.19 : IS3-19 – http://en.wikipedia.org/wiki/Leonhard_Euler

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Figure 7.20 : IS3-20 – http://en.wikipedia.org/wiki/Salvia_divinorum

111

Appendix II

Software research tools

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All software and tools used in the research are available to use free of charge (at the time of writing)

from the World Wide Web.

Delicious – Social bookmarking website. All the bookmarks, notes, and tags for any webpage can be

revealed by entering its URL at http://delicious.com/url/

Google Translate is a web based translation service available from http://translate.google.co.uk/#

Ontopia (version 5.0.2) – Java based software tools for building, maintaining, and deploying Topic

Maps-based applications. Available from: http://www.ontopia.net/download/freedownload.html

Protégé – Ontology builder used to expose the main class fields and class relations in the DBpedia

database. Available from: http://protege.stanford.edu/

Semantic Proxy – part of the Thomson Reuters Calais initiative. Currently at Beta testing stage.

Available from: www.semanticproxy.com

Thinkpedia – Thinkpedia is developed by Christian Hirsch, PhD student at the University of Auckland,

under the supervision of John Hosking and John Grundy. Requires Java. Available from:

www.thinkpedia.cs.auckland.ac.nz

Thinkmap - is available from: http://www.thinkmap.com/ - requires Java. Trial version allows three

tries before purchase. Standard Edition download with a limited term license for research and

development purposes only is available through application only.

Wikipedia – a “web-based, collaborative, multilingual encyclopaedia project.” English language

version available from: http://en.wikipedia.org/wiki/Main_Page

Wordle - tool for generating “word clouds” from text. The clouds give greater prominence to words

that appear more frequently in the source text. Available from: http://www.wordle.net/

Wordnet 3.0 – Wordnet is a large lexical database of English developed under the direction of

George A. Miller. Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms

(synsets), each expressing a distinct concept. Synsets are interlinked by means of conceptual-

semantic and lexical relations. Version 3.0 is online and available from:

http://wordnetweb.princeton.edu/perl/webwn

Javascript Visual Wordnet – a visual realisation of the Wordnet lexical database, particularly clear at

showing disambiguation of terms. Less powerful than Thinkmap but available free of charge under

Creative Commons license. Available from: http://kylescholz.com/projects/wordnet/

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Polaris Word Count – calculates keyword frequency and overall web page word count from a URL.

Available to download from: http://www.polariscomputing.com/plcount.htm

Semantic Similarity - created by Ted Pedersen and Jason Michelizzi. Eleven semantic measures

available on the Wordnet dictionary. Available from: http://marimba.d.umn.edu/cgi-

bin/similarity/similarity.cgi

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Appendix III

A notational convention for tags and concepts

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Sowa (2000a) provides the following diagram (an adaption of the Aristotelian concept triangle) to

highlight the care needed to distinguish between a symbol for the object itself and the concept of

that object.

Yojo the cat is different from the tag {Yojo} because the word Yojo identifies the object Yojo whereas

{Yojo} identifies the tag applied to Yojo. The distinction seems trivial but is important because {Yojo}

represents the concept [Yojo], not the instance, which is denoted by Yojo.

In some steps of this argument, the distinction between the two is a valuable part of the reasoning

process and, therefore, necessary to differentiate. As Sowa’s triangles show, it is possible to become

entangled in a concept regression and the clarity of terms is the only means of signposting which

point of which triangle is being discussed.

The convention reveals what is problematic with a Folksonomy such as Delicious only allowing a

single string tag. The object may be better symbolised with a multi-term string (such as {double slit

experiment} in IS3-8 document). If this is the case, then Sowa’s diagram shows that the symbol for

the concept [double slit experiment] must also be a multi-term string. Similarity between tags is

dependent on their capacity to represent the appointed (or most used) symbol for the object.

Figure 9.1 – The difference between a symbol that

represents an object and a {symbol} that represents a

[concept]

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