Knowledge structuring to support facet-based ontology visualization

Knowledge Structuring to SupportFacet-Based Ontology VisualizationCarmen De Maio,∗ Giuseppe Fenza,† Vincenzo Loia,‡ Sabrina Senatore§

Dipartimento di Matematica e Informatica, Universita degli Studi di Salerno,via ponte don Melillo 84084, Fisciano (SA), Italy

The huge growth of data on the Web and the requirement of semantic content analysis makethe knowledge management and data mining very difficult activities. The knowledge elicitation,codification, and storage need not trivial techniques to improve formal information structuringon the Internet. Ontologies provide conceptualization and processing knowledge, sharing ofconsolidate understanding, reusing of domain knowledge codification for many Web applications.Manual construction of a domain-specific ontology is an intensive and time-consuming process,which requires an accurate domain expertise, because of structural and logical difficulties inthe definition of concepts, as well as conceivable relationships. At the same time, the ontologyvisualization process requires similar endeavors to support ontology management, exploration, andbrowsing. This work describes an automatic method for ontology design from the content analysisof Web resources. The approach exploits a fuzzy extension of formal concept analysis model forstructuring the elicited knowledge, viz. concepts and relations embedded in the resources content.Final result is an effective ontology visualization through a navigable, facet-based view of thebuilt ontology across the extracted concepts and their own population. Furthermore, the approachproposes a simple labeling of ontology concepts through a sketched and intuitive process. C© 2010Wiley Periodicals, Inc.

1. INTRODUCTION

Because of the explosive increase in information published on the Web, manag-ing digital information becomes a challenge that requires formalisms and knowledgestructuring for comprehensive and transportable machine understanding. In fact,without the help of efficient data management tools, it is a hard task to extract theknowledge, viz. the “semantic” content from the available Web information beyondits textual presentation. Unstructured or semistructured data are often characterized

∗Author to whom all correspondence should be addressed: e-mail: [email protected].†e-mail: [email protected].‡e-mail: [email protected].§e-mail: [email protected].

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, VOL. 25, 1249–1264 (2010)C© 2010 Wiley Periodicals, Inc.View this article online at wileyonlinelibrary.com. • DOI 10.1002/int.20451

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by lack of any fixed schema, without strict typing, and with a wide heterogeneity intext organization.

In the current era, semantic aspects of data have acquired a strategic valuefor applications interoperability, for sharing of common understanding of subjectsamong humans and computers.

Ontologies supply an adequate solution in knowledge representation, sharing,and reusing for a variety of applications domain. Although there is no universalconsensus about the definition of ontology, it is generally accepted that an ontol-ogy is a specification of conceptualization.1 That means an ontology is a formalrepresentation of concepts and their interrelationships.

Ontologies range from a simple form of taxonomy (i.e., a hierarchical structure)or vocabulary with standardized machine interpretable terminology supported bynatural language definitions, until to be enriched with formal expressions describedby a logical domain theory with very expressive and complex information. Theexponential information growth on the Web and the strict request of semantics andknowledge structuring have potentiated the diffusion of ontologies. They vary insize, semantics, application domain, often are redundant, sometimes covering just aspecialized domain. Anyway, there is a clear exigency of integration of the hetero-geneity aimed at the knowledge sharing, updating, and communication. Standardsemantic markup languages such as OWL, RDF, and so on provide a declarativeform for ontology communications and interchanges, yet a shared domains concep-tualization is still a challenging goal.

Recent studies on ontology design methodologies evidence concrete difficultiesin developing accurate and consistent ontologies. Some approaches propose toimprove ontology construction by using automatic discovery of taxonomic andnontaxonomic relationships from domain data or domain-specific texts.2

Also, ontology building presents structural and logical difficulties in the def-inition of concepts, properties, and relations. On the other hand, developing anontology from scratch is a time-consuming, labor-intensive task which requires anaccurate understanding of the reference domain.

In addition, ontologies do not foreseen to manipulate uncertain information ofthe real world. To handle uncertainty of information and knowledge, one possiblesolution is to incorporate fuzzy theory into ontology; in fact, the use of fuzzy tech-niques in the ontology modeling can provide the right add-on to capture uncertaintyand/or approximation in the relationship and conceptual information.

On the other hand, working with ontologies coded through semantic languagessuch as OWL can be overwhelming at the first time, especially for the beginners.Ontology visualization can also be the answer to better understanding of the datacontained by ontologies. A visual version of an ontology helps users to visuallyexplore concepts and its nearest neighbors, or analyze the conceivable individualsassociated with those concepts.

This paper presents an automatic method for ontology extraction from a col-lection of textual resources and then the design of a fuzzy OWL-based ontologyschema. The work is based on a fuzzy extension of formal concept analysis (FCA),a mathematical model3 for knowledge structuring and emphasizes the ontologynavigation across its own population, by using a friendly, multifacet Web interface.

International Journal of Intelligent Systems DOI 10.1002/int

FACET-BASED ONTOLOGY VISUALIZATION 1251

Figure 1. The whole process of the system.

The rest of this paper is structured as follows. Section 2 describes formalmodel beyond the ontology construction and the mapping into an effective OWL-based ontology. Section 3 presents the visualization of the generated OWL ontology,the exploration of the ontology population, and finally, the labeling of the ontologyconcepts. Section 4 introduces a few considerations about some initial experimentalresults. Related works and conclusions are at the end of paper.

2. THEORETICAL BACKGROUND OF THE SYSTEM MODEL

Figure 1 sketches the whole process of the system through some macrophases.Essentially, there are two main activities: the (fuzzy) OWL-based ontology genera-tion and the GUI-based ontology navigation and ontology concept labeling.

A collection of digital resources represents the input to the first phase: A text-based parsing of these documents produces a meaningful set of keywords associatedwith each resource. In general, preprocessing activities such as normalization, POStagging, lemmatize, and stop-word removal processing produce a matrix composedof vector-based representations of each analyzed resource. Thanks to fuzzy formalconcept analysis (FFCA) theory, the fuzzy formal context (i.e., the computed ma-trix) and the fuzzy formal lattice are generated. The process continues through thetranslation of the lattice into an OWL-based ontology. In fact, output of this phaseis an OWL ontology codification.

The second phase takes as input the OWL representation of the ontology and,through the multifacet-based GUI, produces a graphical representation of it, allowingexploration among concepts and instances.

The process finishes by providing an automatic, simple naming of the ontologyconcepts.

The first phase of this process (OWL-based ontology generation) is detailed inthe following two subsections, which present, respectively, the mathematical model(FFCA) and the mapping to translate the lattice into the ontology OWL-code.


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2.1. The Formal Model beyond the Ontology Building

The core of the first phase of this system is the theory based on FCA.4 FCAis a technique of data analysis, which exploits the formal context and the orderedlattice theory5 to enable the representation of the relationships between objects andattributes in a given domain.

Formal concepts can be interpreted from the concept lattice using FCA. Theconcept lattice represents a mathematical modeling of knowledge, which is moreinformative than traditional tree-like conceptual structures.4 Moreover, it can alsosupport multiple inheritances. It is based on the definition of formal context forrepresenting these relationships.

Recently, FCA has been exploited in many applications in which uncertain andvague information occur in the representation of the domain. A possible approachis to incorporate the fuzziness (i.e., uncertain values in the range [0,1]) into FCA.

This extension is known as fuzzy formal concept analysis (FFCA).6 Thus, toexploit the FCA theory, the real-world domain must be “translated” into a cross-table, where each cell of table (i, j ) describes an existing “fuzzy” relation betweenthe object i and the attribute j by means of a membership value.

More specifically, in the traditional FCA method, the formal context is repre-sented by a dichotomy matrix: Each cell of table can have a value between 0 and1, true and false, to indicate the existence or nonexistence of the relation betweenobjects and attributes. In the corresponding “fuzzy” table, the value in the range[0,1] provides the existence of a relation and in addition, gives a valuation about thestrength of the relation.

Each fuzzy formal concept is a couple (objects and attributes), with an asso-ciated membership function. The resulting lattice is constrained by a confidencethreshold T , which is applied to the context for pruning all the relations (i, j ) whosemembership function assumes values less than T .5

In the sequel, some definitions that incorporate fuzzy logic into FCA are given.

DEFINITION 1 . A fuzzy formal context is a triple K = (G, M , I = ϕ(G × M)),where G is a set of objects, M is a set of attributes, and I is a fuzzy set on domainG × M . Each relation (g, m) ∈ I has a membership value μ(g, m) in [0, 1].

DEFINITION 2 (Fuzzy Representation of Object). Each object O in a fuzzy for-mal context K can be represented by a fuzzy set �(O) as �(O) = {A1(μ1),A2(μ2), . . . , Am(μm)}, where {A1, A2, . . . , Am} is the set of attributes in K and μi

is the membership of O with attribute Ai in K . �(O) is called the fuzzy represen-tation of O.

Figure 2a shows a fuzzy version of the formal context by means of a cross-table. According to the fuzzy theory, the definition of fuzzy formal concept is givenas follows.

DEFINITION 3 (Fuzzy Formal Concept). Given a fuzzy formal context K = (G, M ,I ) and a confidence threshold T , we define A∗ = {m ∈ M|∀g ∈ A: μ(g, m) ≥ T }



Figure 2. An example of a portion of fuzzy formal context: (a) and corresponding fuzzy conceptlattice (b) (with threshold T = 0.6).

for A ⊆ G and B∗ = {g ∈ G|∀m ∈ B: μ(g, m) ≥ T } for B ⊆ M . A fuzzy formalconcept (or fuzzy concept) of a fuzzy formal context K with a confidence thresholdT is a pair (Af = ϕ (A), B), where A ⊆ G, B ⊆ M , A∗ = B and B∗ = A. Eachobject g ∈ ϕ(A) has a membership μg defined as

μg = minm∈B

μ(g, m)

where μ(g, m) is the membership value between object g and attribute m, which isdefined in I . Note that if B = {} then μg = 1 for every g. A and B are the extentand intent of the formal concept (ϕ(A), B), respectively.

Let us observe the fuzzy formal context shown in Figure 2a has a confidencethreshold T = 0.6.

DEFINITION 4. Let (A1, B1) and (A2, B2) be two fuzzy concepts of a fuzzy formalcontext (G, M , I ). (ϕ(A1), B1) is the subconcept of (ϕ(A2),B2), denoted as (ϕ(A1),B1) ≤ (ϕ(A2),B2), if and only if ϕ(A1) ⊆ ϕ(A2) (⇔ B2 ⊆ B1). Equivalently, (A2,B2) is the superconcept of (A1, B1).

DEFINITION 5. A fuzzy concept lattice of a fuzzy formal context K with a confidencethreshold T is a set F (K) of all fuzzy concepts of K with the partial order ≤ withthe confidence threshold T .

The FCA theory proposes a hierarchical model, where the concepts (objectsand their attributes) are arranged in a subsumption relations (known as “hyponym–hypernym” or “is–a” relationship too).


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The fuzzy formal lattice evidences the membership associated with the objectsand the relationship class–subclass. More formally:

DEFINITION 6. The fuzzy formal concept similarity between concept K1 = (ϕ(A1),B1) and its subconcept K2 = (ϕ(A2), B2) is defined as

E(K1, K2) = |ϕ(A1) ∩ ϕ(A2)||ϕ(A1) ∪ ϕ(A2)|

where ∩ and ∪ refer intersection and union operators on fuzzy sets, respectively.Figure 2b shows the portion of lattice related to the formal context of FFCA. In

particular, the fuzzy lattice provides information about the knowledge structuring,the relationships, and similarities between fuzzy formal concepts.

2.2. Step-Based Process for Building of Fuzzy Ontology from Fuzzy FCA

A sequence of steps describes the mapping to transform a fuzzy formal con-text using the fuzzy lattice into an OWL-based fuzzy ontology. The building ofa fuzzy ontology requires a mapping of both intentional and extensional informa-tion (see Definition 3) into the corresponding classes and relations of the ontology.In other words, the process enables the elicitation of the knowledge embedded inthe resources collection, structuring it in a fuzzy ontology conceptualization andpopulation coming from the mapping of intentional and extensional information,respectively.

In the fuzzy formal context, Web resources (objects of the formal context) aredescribed by keywords extracted by parsing their contents. The building of fuzzyontology takes into account the formal concept object-attribute relationships of theformal context.

For instance, given the fuzzy concept lattice shown in Figure 2b, let us consider ahierarchical connection between two concepts Concept 1 (described by urls URL 1,URL 2, and URL 3, the last two are inherited and the unique attribute image) andConcept 2 (described by urls URL 2 and URL 3 and the inherited attribute imageand two its own, colors, paint). The membership value associated with this relationis 0.61. The translation of a lattice into a fuzzy ontology can be described by thefollowing mapping steps:

Step 1: Class Mapping

This step translates each formal concept of the fuzzy lattice into an ontologyclass. The analysis of extent and intent of the fuzzy context guarantees the appro-priate characterization of the ontology class (as described in the next steps). Letus note the approach automatically produces a class name, identified by a specificprogressive number (Concept 1, Concept 2, zoomed in Figure 3).



Figure 3. Zoom in of two fuzzy formal concepts.

<owl:Class rdf:ID="Concept 1"></owl:Class>

<owl:Class rdf:ID="Concept 2"></owl:Class>

Step 2: Hierarchy Mapping

This step focuses on the subsumption connections among concepts in the latticeas shown in Figure 2. The mapping of this relation is described by the predicate“rdfs:subClassOf ” in the resulting OWL ontology. In other words, in the OWL classdefinition, there is an assertion that defines Concept 2 a specialization of Concept 1.

Through the reification, the membership values can be assigned to the state-ments subClassOf C2 C1. The example shown in Figure 3 evidences the member-ship value 0.61, associated with the hierarchical connection between the Concept 1and Concept 2.

The datatype property “Membership” describes, by the OWL code, the mem-bership degree associated with relation.

<rdf:Statement rdf:ID="subClassOf C2 C1"><rdf:subject resource="#Concept 2"/><rdf:predicate resource="&rdfs;subClassOf"/><rdf:object resource="#Concept 1"/>

</rdf:Statement>

<rdf:Statement><rdf:subject resource="#subClassOf C2 C1"/>


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<rdf:predicate resource="#Membership" /><rdf:object>0.61</rdf:object>

</rdf:Statement>

<owl:DatatypeProperty rdf:ID="Membership"><rdfs:domain resource="&rdfs;Statement"/><rdfs:range resource="&xsd;float"/>

</owl:DatatypeProperty>

Step 3: Relation Mapping

This step translates the intent (attributes in the fuzzy formal context) of the conceptsof the lattice into a set of properties in the resulting OWL ontology. Figure 3 showsas example, the intent of the formal concept Concept 2, composed of the wordscolors, paint, and image.

The mapping step defines an OWL DatatypeProperty whose domain is thereference concept and the range is a float datatype. This way, a membership value inrange [0,1] can be associated with the actual individuals, when the datatype propertyis applied on them.

<owl:DatatypeProperty rdf:ID="image"><rdfs:domain rdf:resource="Concept 1"/><rdfs:range rdf:resource="&xmlns;#float"/>


<owl:DatatypeProperty rdf:ID="colors"><rdfs:domain rdf:resource="Concept 2"/><rdfs:range rdf:resource="&xmlns;#float"/>


<owl:DatatypeProperty rdf:ID="paint"><rdfs:domain rdf:resource="Concept 2"/><rdfs:range rdf:resource="&xmlns;#float"/>


Step 4: Individuals Generation

In this step, the mapping process translates the extent of the concepts, i.e., Webresources that compose the fuzzy formal context. For each Web resources in theextent of a concept, an instance of the corresponding ontology class is generated. Inthe example of Figure 3, the individuals associated with Concept 1 and Concept 2are described by the relative OWL code. In particular, the class relative to Concept 2is composed of the extent URL 2 and URL 3 and has associated an inherited attributeimage and two own attributes colors and paint. The membership values in the cross-table of the fuzzy formal context are mapped too. For each datatype property, whosedomain is the ontology concept Concept 2 (in this case), a predicate instance isgenerated. The range of each predicate instance is a float value, corresponding to amembership given in the fuzzy formal context matrix. The OWL code describingthe instances URL 1, URL 2, and URL 3 is shown as follows:



<Concept 1 rdf:Id="URL 1"><image> 1.00 </image>

</Concept 1>

<Concept 2 rdf:Id="URL 2"><colors> 1.00 </colors><image> 0.80 </image><paint> 0.94 </paint>

</Concept 2>

<Concept 2 rdf:Id="URL 3"><colors> 1.00 </colors><paint> 0.98 </paint><image> 0.78 </image>

</Concept 2>

3. ONTOLOGY VISUALIZATION AND CONCEPT LABELING

Several ontology visualization approaches have been delivered by researchcommunity; most of them are based on graph although they are not capable ofeffectively visualizing every kind of data. In general, the visualization techniqueshould always be appropriate to support the user’s goal in viewing the representationand should allow the user to interact with the visualization.

As said, the second phase of the whole process (GUI-based ontology navigationand ontology concept labeling; see Figure 1) consists of the generation of a visualrepresentation of our built OWL ontology. Let us note the OWL language is quitecomplex and looking at an OWL ontology for the first time can be overwhelming.

For these reasons, to facilitate the exploration and the navigation of an ontology,a friendly user interface has been developed, as shown in Figure 4. It is mainly splitin two main parts: the ontology tree, on the left and the “zoomed” details associatedwith the selected concept, on the right.

In the next subsection, the facet-based visualization of the generated on-tology is described. Then, an intuitive, automatic ontology labeling process ispresented too.

3.1. Multifacet View-Based Ontology Exploration

The user interface shown in Figure 4 presents a facets-based navigation of thehierarchy of concept classes and the relative individuals. The faceted navigation isan efficacy technique for accessing a collection of information (or resource content)through a navigation based on filtering. Let us note the ontology tree visualization,that is created according to the described mapping in preceding section.

The formal concept lattice, translated into an OWL taxonomy evidences anew view of knowledge intrinsically coded into Web resources information. Thefacet-based representation reveals new ensembles into the data by providing multi-ple concept-based abstractions of the knowledge and implicit relationships amongattributes or objects.


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Figure 4. Ontology representation through facet-based Web interface.

In Figure 4, a simple example of an ontology tree visualization is shown. Inparticular, the ontology concept hierarchy corresponds to the OWL-code, computedby the portion of formal lattice analyzed in Figure 3. Let us note the simple graphicaltree-based representation of ontology fully traces the lattice concepts and relations.In fact, in the example in Figure 4, Concept 1 has been selected, during the navigationof the ontology tree.

Let us observe each concept is generically named by a progressive identifi-cation and has associated the number of individual belonging to that class (in theparenthesis). In this version of the GUI, no renaming of concepts has been defined.The selected Concept 1 in appears on the right-hand side of interface in the conceptfacet view, evidencing that the Concept 1 has a subconcept, Concept 2 and its ownindividual URL 1 (compare with the example of Figure 3).

Furthermore, on the right-hand side, on the bottom, all the individuals comingfrom the Concept 1 and its descendents are listed too. These individuals representthe instances, i.e., the URLs of the Web resources that are described by the concept.It is possible to continue the exploration by clicking on the subconcepts of Concept 1(in the figure, Concept 2) to visualize only the relative individuals.

The user can navigate the ontology population easily, exploring each concept,discovering conceivable concept–subconcept relationships and analyzing associatedindividuals.

3.2. Naming of the Ontology Concepts

As said, the system achieves a textual content analysis of Web resources byproviding a characterization of the concepts that compound the ontology. As seen,the ontology generation process does not produce meaningful labels for the concepts,



but only an identification of each concept (through an increasing number univocallyassociated) to which the resources/attributes are related. Usually, human interventionis required to assign a proper naming to the generated ontology concepts, accordingto the meaning of the resources content.

The ontology labeling is based on the lattice representation. In particular, dif-ferently by the approach presented in Ref. 2 the redundancy of concepts is overcome,thanks to the given graphical visualization of the lattice and, the elimination of redun-dant elements in formal concepts intents and redundant objects in formal conceptsextents is an implicit activity. In fact, looking at the portion of the lattice in Figure 2,for instance, Concept 1 has the extent composed of one its own url, URL 1 andtwo other ones (i.e., URL 2, URL 3) that are included in the specialized subconceptConcept 2 too and hence, they are locally redundant. Similar considerations holdfor the intent of Concept 2 composed of two its own attributes, paint and color andone is inherited, image.

According to Ref. 7, eliminating these two kinds of redundancy from conceptlattice does not produce loss of information.

On the other hand, let us note the formal lattice and OWL-based translationinto ontology reproduces similar structuring in the concepts representation andhierarchical relations and population instances; consequently, the naming of formalconcepts has a direct correspondence with the OWL concepts.

At the end, a simple and automatic labeling of formal concept (or the corre-sponding ontology concept) can be accomplished as follows:

• Each concept takes the name of the most representative attribute, viz. the label of conceptis the name of attribute whose membership is the highest one. If the concept has morethan one attributes with the same membership, the label is composed by the concatenationof all the names of these attributes.

• During this naming procedure, there might be nodes, or concepts with no own attributes.In this case, a possible solution is to require help to domain expert or ontology designer.On the other hand, the process generates automatically a name as a concatenation of thelabel of the proper parents.

To select the most representative name attribute as candidate label of a concept, asketched pseudocode is shown as follows:

In other words, according to Definition 3, we compute μm (dually to thedefinition of μg) and then for all the attributes, we assign as a label, viz. the attributename, whose membership is the maximum among all μm.

In Figure 5, a renaming of the concepts previously analyzed is shown. Herein,more emphasis is given to Concept 2, which has been renamed in the ontology tree


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Figure 5. An example of ontology concept labeling.

by “Color, Paint,” according to the described ontology naming process. The concepthas associated its own resources URL 2 and URL 3, which represent two Web pagesdealing with inherent topics, as evidenced in Figure 5.

Similarly, Concept 1, appearing in background is a direct superconcept in thetree, has as a label “Image.”

4. EXPERIMENTAL RESULTS

Some initial experiments have been conducted on the built ontology codedin OWL language. The test has been applied on a collection-sample of 443 ob-jects, which represent Web pages or just portions of them. As seen previously, wehave named them generically by URL X, where Xrepresents an increasing numberassociated univocally to each objects.

Through the building of formal context, a fuzzy lattice composed of 193 con-cepts was generated. The assessment of the results is based on the comparisons ofthe categories automatically generated by the fuzzy FCA technique with the ex-pected ones. Let us emphasize this is just an initial study to validate whether thedesired concepts arise in the building of the lattice, thus, an ad hoc sample of theWeb resources has been selected.

The generated ontology can be analyzed by two different abstraction levels:on the one hand, by studying the meaning of concepts by the interpretation ofthe attributes collected together in each concept (this point is strictly related tothe ontology concept naming: next study will focus on it); on the other hand, byanalyzing the categorization coming from the ontology structuring.



Focusing on the last aspect, the resulting lattice evidences the most of Webresources are grouped consistently in the appropriate categories. More specifically,87% of the whole collection of Web resources is classified coherently with ourexpectations. The generated ontology reveals that just the remaining 13% of re-sources appears as misclassified; upon analysis , some of them reveal ambiguity inthe content because present different topics with respect to the associated category.In particular, our approach emphasizes in some cases, an accurate classification:indeed, some resources have been placed in specific categories that represent aspecialization of the original defined ones.

5. RELATED WORKS

The success of Semantic Web depends on the explosive diffusion of ontologies,which confirm their effectiveness, efficiency, and accuracy in knowledge represen-tation and data sharing.

Ontologies provide many benefits in representing and processing data, sharingof common knowledge, and reusing it for different applications. Yet, Web ontologiestend to grow bigger or be redundant to cover an application domain. This highlights aclear exigency to “tailor” a simple process for designing ad hoc ontologies. Ontologybuilding is a complex process that evidences structural and logical difficulties in theidentification and definition of concepts, properties, and relations.

Yet, building a good, ad hoc ontology for specific or general-purpose domainsis not an exhaustive activity. Additional difficulties often are related to the graphicalrepresentation of a generated ontology. Visualization of ontologies is not an easytask. An ontology is not only a hierarchy of concepts: It is enriched with rolerelations among concepts, and each concept can have various properties. Also, eachconcept could have many individuals, instances of it. Consequently, designing avisualization that enables the display of all this information and allows the user toperform various operations on the ontology through a friendly interface is not aneasy task.8

5.1. Ontology Building

Traditional approaches are based on the acquisition of knowledge from theavailable Web resources manually and require labor-intensive and time-consumingactivities. Automated or semiautomated tools are designed for knowledge structuringin specific domains, for instance, through automatic discovery of taxonomic and non-taxonomic relationships from domain data. Well-known tools such as OntoLearn9

and OntoEdit10 enable knowledge structuring and ontology engineering activities;ASIUM11 supports the task of knowledge semantic acquisition from textual re-sources and automatically ontology building, for instance. Machine learning andtext mining methods have been introduced to assist users in this process, for in-stance, Text2Onto12 is a framework for ontology learning from textual resources toautomatically create ontologies from a corpus of documents within a certain domain.

As in our approach, in Ref. 13 a technique based on FCA theory has beenexploited for data analysis. The method has been adapted to maintain a concept map


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for the reuse of knowledge. The focus of this approach is more related to informationretrieval domain: It aims at improving the capabilities of the search engine to improvethe user’s query. Other approach combining FCA and fuzzy techniques are definedin the literature.3,14

Lee et al.15 present a fuzzy ontology exploited in the news summarizationcontext. Pollandt16 introduces the L-Fuzzy Context, as an attempt to combine fuzzylogic with FCA, where linguistic variables are used to represent ambiguities in thecontext. An approach similar to ours is FOGA,17 which exploits the FCA theory forthe automatic fuzzy ontology generation.

5.2. Ontology Visualization

There exist a number of ontology editing tools and ontology visualization thatare being used either in the context of ontology management tools or as informationretrieval aids in applications that exploit ontologies.8 According to Ref. 8, thereare some different methods for ontology visualization, some of them are eitherspecifically created to display ontologies or others for general uses related to atree or graph representation. Most of the ontology visualization systems, such asProtege,18 OntoEdit,10 Kaon,19 and OntoRama20 offer a Windows Explorer like treeview of the ontology, where concepts and relations are placed in an indented list.

One of the recurrent ontology visualization schemas is a network representa-tion, through a set of interconnected nodes, presenting the taxonomy with a top–down or left to right layout. OntoViz21 is a Protege18 visualization plug-in, whichexploits the GraphViz22 library and produces a two-dimensional (2D) graph visu-alization, showing concepts, properties, relations, and colored instances. IsaViz23

is a visual environment for browsing RDF ontologies, representing graphs such asellipses, boxes, and arcs between them. OntoTrack24 is a browsing and editing “in-one-view” tool with a hierarchical layout, which provides, as additional feature, aninterface with an external OWL reasoner. GoSurfer25 is a data-mining tool for visu-alizing biological data and comparing genes in the GO (Gene Ontology Consortiumhttp://www.geneontology.org/) ontology.

Other approaches converge on the 2D and three-dimensional (3D) hyperbolicschemes: A node is on focus, and the rest of the nodes are presented around it withreduced size.

In Refs. 26 and 27, a 2D hyperbolic tree for describing an ontology is presented;the hyperbolic tree technique considers, initially, the root of the tree in the center of acircular area with the child nodes around it. OntoRama20 presents a hyperbolic-typevisualization too, apart from the windows explorer–like tree view.

6. CONCLUSIONS

This work describes the entire process to translate informal knowledge ofWeb resources into a structured, ontology-based conceptualization. Final result is afriendly user interface for the ontology visualization. The approach starts describingthe automatic process for the OWL ontology building, through the analysis of raw



knowledge elicited from Web resources collections, and then a graphical tree-basedinterface allows the exploration and the navigation across the concepts. The tree-likerepresentation enables a new viewpoint of initial “plain” information, emphasizingthe inner relationships with other data and providing a navigable, structured model.

The tree-based view of the ontology is improved by the automatic labeling ofontological concepts. As said, the relevant role is played by the formal concepts ofthe lattice, particularly by the attributes that are included into each formal concept.Indeed, although the attributes are coded as data property in the OWL-language, theirsemantics is crucial to discriminate the meaning and the words’ sense associatedwith each ontology concept.

Future extensions foresee the improving of the concept labeling mechanism toproduce a better automatic and meaningful naming for the concept tree. Anyway,the initial implementation outcomes demonstrate that our theoretical approach isadmissible and this software application can meet the real users’ expectations.

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Knowledge structuring to support facet-based ontology visualization

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