A Survey on Ontology Creation Methodologies

48 Int’l Journal on Semantic Web & Information Systems, 1(2), 48-68, April-June 2005

Copyright © 2005, Idea Group Inc. Copying or distributing in print or electronic forms without writtenpermission of Idea Group Inc. is prohibited.

A Survey onOntology Creation Methodologies

Matteo Cristani, Università di Verona, ItalyRoberta Cuel, Università di Verona, Italy

ABSTRACT

In the current literature of knowledge management and artificial intelligence, severaldifferent approaches have been tried to the problem of developing domainontologies from scratch. All these approaches deal fundamentally with threeproblems: (1) providing a collection of general terms describing classes andrelations to be employed in the description of the domain itself; (2) organizing theterms into a taxonomy of the classes by the ISA relation; and (3) expressing in anexplicit way the constraints that make the ISA pairs meaningful. Though a numberof such approaches can be found, no systematic analysis of them exists which canbe used to understand the inspiring motivation, the applicability context, and thestructure of the approaches. In this chapter, we provide a framework for analyzingthe existing methodologies that compare them to a set of general criteria. Inparticular, we obtain a classification based upon the direction of ontologyconstruction; bottom-up are those methodologies that start with some descriptionsof the domain and obtain a classification, while top-down ones start with an abstractview of the domain itself, which is given a priori. The resulting classification isuseful not only for theoretical purposes but also in the practice of deployment ofontologies in Information Systems, since it provides a framework for choosing theright methodology to be applied in the specific context, depending also on theneeds of the application itself.

Keywords: Please provide

INTRODUCTION

In the recent past, complex marketshave been characterized by a huge spe-cialization of work, a high level ofoutsourcing processes, and a more openPorter’s chain that develop and increase

the needs of intra- and interorganizationalnetworks. Both intraorganizational net-works among strategic units, divisions,groups, and other even smaller substruc-tures and interorganizational networks,such as industrial districts and knowledgenetworks (Hamel & Prahalad, 1990) are

https://www.researchgate.net/publication/201382098_The_Core_Competences_of_the_Corporation?el=1_x_8&enrichId=rgreq-41f34b87b31382cad16ff8c61298e8e8-XXX&enrichSource=Y292ZXJQYWdlOzIyMDEyMzg1NDtBUzoxODgxMDg4MDA3OTg3MjJAMTQyMTg2MDA0MTQ0Nw==


Int’l Journal on Semantic Web & Information Systems, 1(2), 48-68, April-June 2005 49

composed of a constellation of special-ized units (Ashby; 1956; Numagami, Ohta& Nonaka, 1989), which might not becontrolled totally by a unique subject andmight grow and differentiate their activi-ties, their system of artifacts, and their viewof the world in an autonomous way. Al-though every unit uses a different view ofthe world (i.e., differentconceptualizations), they should coexist asin a biofunctional system (Maturana &Varela, 1980) and communicate, coordi-nate, and share knowledge in a networkedenvironment. Furthermore, this continuousand unpredictable encountering of differ-ent views might enable the creation of anunexpected and innovative combination ofprocesses and products (Chandler, 1962).

Most of these processes are basednowadays on the Web, and in this sce-nario, tools and technologies that sustainknowledge telecommunication, coordina-tion, and sharing are increasing their im-portance. Therefore, both the scientificcommunity on computer science and in-dustries are interested in what is called theSemantic Web. The Semantic Web com-munity has grown in terms of dimensionand specialization, and different viewpointsof creating and managing semantic toolshave been taken on the nature of the Web.In studies, an excellent role is being as-sumed by discipline about developmentof systems and methodology that allow thecombination of several different views (ex-pressed through taxonomies, classifica-tions, contexts, and ontologies) of theworld. In particular, ontology can be con-sidered the boundary topic between theSemantic Web research and InformationSystems that mostly deserves special at-

tention to the methodological aspects. Thisis the argument that urged researchers allover the world to create outstandingprojects that involve methodological re-search, and that urged industries to createontology-based applications that expressthe units’ points of view and, at the sametime, allow knowledge exchange amongdifferent units and their perspectives(Bonifacio, Bouquet & Cuel, 2002). Inthis work, we are interested especially inontology, because that it is one of the mostimportant systems and methods that al-lows an explicit representation of knowl-edge and expresses itself a view of theworld. Even if implicitly, each organiza-tion, unit, or employee interconnected ina networked system uses a personal con-ceptual schema that could be shared or atleast understood among them. Therefore,developing a good ontology seems to beone of the critical issues n this area. Forthis reason, this chapter focuses on themethodologies in literature that provideframeworks for developing Formal On-tologies. Frameworks are provided byontology creation tools, which explicitlyor implicitly endow a series of steps thatthe developer should follow. For instance,some ontology development tools makeavailable documentation that describes thebetter way in which an ontology devel-oper might create an ontology; other on-tology development tools implicitly forcethe developer to follow some specific stepsduring the process of ontology creation.

BACKGROUND THEORIES

It is quite well established in recentinvestigation on Information Systems, that

https://www.researchgate.net/publication/243771381_The_Role_of_Classifications_in_Distributed_Knowledge_Management?el=1_x_8&enrichId=rgreq-41f34b87b31382cad16ff8c61298e8e8-XXX&enrichSource=Y292ZXJQYWdlOzIyMDEyMzg1NDtBUzoxODgxMDg4MDA3OTg3MjJAMTQyMTg2MDA0MTQ0Nw==

https://www.researchgate.net/publication/200025838_An_Introduction_To_Cybernetics?el=1_x_8&enrichId=rgreq-41f34b87b31382cad16ff8c61298e8e8-XXX&enrichSource=Y292ZXJQYWdlOzIyMDEyMzg1NDtBUzoxODgxMDg4MDA3OTg3MjJAMTQyMTg2MDA0MTQ0Nw==




formal ontologies are a crucial problem todeal with, and, in fact, they received a lotof attention in several different communi-ties (e.g., knowledge management, knowl-edge engineering, natural language pro-cessing, intelligent information integration,etc.) (Fensel, 2000). Ontologies havebeen developed in Artificial Intelligence tofacilitate knowledge sharing and reuse, andnow are applied and intended to be usedin expert systems of almost all types ofindustries. The concept of ontology that isadopted in this chapter is taken from thegeneral considerations on the use of philo-sophical issues in Artificial Intelligence:

“the systematic, formal, axiomaticdevelopment of the logic of all forms andmodes of being.” (Cocchiarella, 1991, p.000)

Another commonly accepted defi-nition is that an ontology is an explicitspecification of a shared conceptualizationthat holds in a particular context. The ac-tual topic of ontology is one of thosethemes that epistemology dealt with inphilosophical studies of Parmenides,Heraclitus, Plato, Aristotle, Kant, Leibnitz,Wittgenstein, and others. Ontologies de-fine the kind of things that exist in the worldand, possibly, in an application domain.In other words, an ontology provides anexplicit conceptualization that describessemantics of data, providing a shared andcommon understanding of a domain. Froman AI perspective we can say that:

“... ontology is a formal explicitspecification of a sharedconceptualization. Conceptualization

refers to an abstract model ofphenomena in the world by havingidentified the relevant concepts of thosephenomena. Explicit means that the typeof concepts used, and the constraints ontheir use are explicitly defined. Formalrefers to the fact that the ontology shouldbe machine readable. Shared reflect thatontology should capture consensualknowledge accepted by thecommunities.” (Gruber, 1993, p. 000 )

“… an ontology may take a variety offorms, but necessarily it will include avocabulary of terms, and somespecification of their meaning. Thisincludes definition and an indication ofhow concepts are inter-related whichcollectively impose a structure on thedomain and constrain the possibleinterpretation of terms.” (Jasper &Ushold, 1999, p. 000)

The main idea is to develop an un-derstandable, complete, and sharable sys-tem of categories, labels, and relations thatrepresent the real world in an objectiveway. For instance, one of the interestingresults achieved by Aristotle is the defini-tion of general categories used to describethe main features of events, situations, andobjects in the world: quality, quantity, ac-tivity, passivity, having, situated, spatial,temporal. Kant figured out only fourmacro categories used to describe theworld: quantity, quality, relation, and mo-dality. Nowadays, ontologies are not onlyan explicit and theoretical specification ofa shared conceptualization or a commonand abstract understanding of a domain,but they also are implied in projects asconceptual models that allow:

https://www.researchgate.net/publication/2322607_Ontologies_A_Silver_Bullet_for_Knowledge_Management_and_Electronic_Commerce?el=1_x_8&enrichId=rgreq-41f34b87b31382cad16ff8c61298e8e8-XXX&enrichSource=Y292ZXJQYWdlOzIyMDEyMzg1NDtBUzoxODgxMDg4MDA3OTg3MjJAMTQyMTg2MDA0MTQ0Nw==



• communication among people and het-erogeneous and widely spread appli-cation systems;

• content-bases access on corporateknowledge memories, knowledgebases, archives;

• agent understanding through interaction,communication, and negotiation ofmeanings; and

• understanding and agreement upon apiece of information structure.

In other words, ontologies providequalitatively new levels of services in sev-eral application domains such as the Se-mantic Web (Ding & Foo, 2002) or fed-erated databases. Moreover, they enablereuse of domain knowledge, make domainassumption explicit, and separate domainknowledge from the operational knowl-edge.

Unfortunately, in the real world or inpractical applications (i.e., informationsystems, knowledge management systems,portals, and other ICT applications), thegeneral and universal categories (studiedby philosophers for along time) are notbeing used widely. In particular, two mainproblems come up in modern applications:

• it is difficult to implement a general on-tology within specific domains;

• it is too expensive to create very com-plex, complete, and general ontologies.

Therefore, less complete, correct,and consistent ontologies have been used,and problems have arisen due to the factthat in the same project or domain, peoplemight use different ontologies composed

by various combinations of categories. Thismeans that different ontologies might usedifferent categories or systems of catego-ries to describe the same kinds of entities;or even worse, they may use the samenames or systems of category for differ-ent kinds of entities. Indeed, it might bethat two entities with different definitionsare intended to be the same, but the taskof proving that they are, indeed, the samemay be difficult, if not impossible (Sowa,2000). The basic assumption of this be-havior is that what we know cannot beviewed simply as a picture of the world,as it always presupposes some degree ofinterpretation. Different categories repre-sent different perspectives, aims, and de-grees of world interpretation. Indeed, de-pending on different interpretationschemas, people may use the same cat-egories with different meanings or differ-ent words to mean the same thing. Forexample, two groups of people may ob-serves the same phenomenon but still seedifferent problems, different opportunities,and different challenges. This essential fea-ture of knowledge was studied from dif-ferent perspectives, and the interpretationschemas were given various names (e.g.,paradigms [Kuhn, 1979], frames[Goffman, 1974], thought worlds[Dougherty, 1992], context [Ghidini &Giunchiglia, 2001], mental spaces[Fauconnier, 1985], cognitive path[Weick, 1979]). This view in which theexplicit part of what we know gets itsmeaning from a typically implicit or taken-for-granted interpretation schema leads tosome important consequences regardingthe adoption and use of categories andontologies.

https://www.researchgate.net/publication/200465509_Intepretive_Barriers_to_Successful_Product_Innovation_in_Large_Firms?el=1_x_8&enrichId=rgreq-41f34b87b31382cad16ff8c61298e8e8-XXX&enrichSource=Y292ZXJQYWdlOzIyMDEyMzg1NDtBUzoxODgxMDg4MDA3OTg3MjJAMTQyMTg2MDA0MTQ0Nw==

https://www.researchgate.net/publication/238681220_Mental_Spaces_Aspects_of_Meaning_Construction_in_Natural_Language?el=1_x_8&enrichId=rgreq-41f34b87b31382cad16ff8c61298e8e8-XXX&enrichSource=Y292ZXJQYWdlOzIyMDEyMzg1NDtBUzoxODgxMDg4MDA3OTg3MjJAMTQyMTg2MDA0MTQ0Nw==

https://www.researchgate.net/publication/270162336_In_The_Structure_of_Scientific_Revolutions?el=1_x_8&enrichId=rgreq-41f34b87b31382cad16ff8c61298e8e8-XXX&enrichSource=Y292ZXJQYWdlOzIyMDEyMzg1NDtBUzoxODgxMDg4MDA3OTg3MjJAMTQyMTg2MDA0MTQ0Nw==

https://www.researchgate.net/publication/243597864_The_Social_Psychology_Organizing?el=1_x_8&enrichId=rgreq-41f34b87b31382cad16ff8c61298e8e8-XXX&enrichSource=Y292ZXJQYWdlOzIyMDEyMzg1NDtBUzoxODgxMDg4MDA3OTg3MjJAMTQyMTg2MDA0MTQ0Nw==

https://www.researchgate.net/publication/222528003_Local_Models_Semantics_or_contextual_reasoninglocalitycompatibility?el=1_x_8&enrichId=rgreq-41f34b87b31382cad16ff8c61298e8e8-XXX&enrichSource=Y292ZXJQYWdlOzIyMDEyMzg1NDtBUzoxODgxMDg4MDA3OTg3MjJAMTQyMTg2MDA0MTQ0Nw==


https://www.researchgate.net/publication/242536446_Interpretive_barriers_to_successful_product_innovation_in_firms?el=1_x_8&enrichId=rgreq-41f34b87b31382cad16ff8c61298e8e8-XXX&enrichSource=Y292ZXJQYWdlOzIyMDEyMzg1NDtBUzoxODgxMDg4MDA3OTg3MjJAMTQyMTg2MDA0MTQ0Nw==



Therefore, an ontology is not a neu-tral organization of categories, but ratherit is the emergence of some interpretationschema, according to which it makessense to organize and define things in thatway. In short, an ontology is often the re-sult of a sense-making process and alwaysrepresents the point of view of those whotook part in that process (see Benerecetti,Bouquet, & Ghidini [2000] for a in-depthdiscussion of the dimensions along whichany representation—including an ontol-ogy—can vary, depending on contextualfactors). The person who takes part inontology creation and management can bean expert in ontology creation or a personwho, through daily work, creates andmanages his or her point of view repre-sented by an ontology.

In computer science, several lan-guages and tools exist for helping final us-ers and system developers in creatinggood and effective ontologies. In particu-lar, various tools help people create, ei-ther manually or semi-automatically, cat-egories, partonomies, taxonomies, andother organization levels of ontologies.Some of the most important ontology edi-tors and ontology manager are:

• Protégé-2000: http://protege.stanford.edu;

• SWOOP: http://www.mindswap.org/2004/SWOOP;

• KAON: http://kaon.semanticweb.org;• WSMX: Web Services Execution En-

vironment is a dynamic discovery thatallows selection, mediation, invocation,and interoperation of Semantic Webservices. More information can be

found at http://sourceforge.net/projects/wsmx;

• OWL-S Editor: A plug-in developedon Protégé. Its goal is to allow easy,intuitive OWL-S service developmentfor users who are not experts in OWL-S. It’s an open source software at http:// o w l s e d i t o r . p r o j e c t s .semwebcentral.org/;

• OntoManager: A workbench envi-ronment that facilitates the managementof ontologies with respect to a user’sneeds. http://ontoware.org/details/ontomanager.

Behind these tools and techniques,different domain-independent languageshave been developed and spread usingRDF(S), DAML+OIL, OWL and itsspecifications, KIF, and so forth.DAML+OIL has been developed as alanguage for specifying descriptions, inparticular to serialize in XML descriptionlogics. The expressive power ofDAML+OIL is, therefore, at syntacticlevel, equivalent to T-box of, for instance,SHIQ, the last evolution in KL-ONE fam-ily. Finally OWL (Web ontology language)is able to integrate the expressive powerof DAML+OIL along with the specifica-tion of ISA relations in an explicit way.

A FRAMEWORK FORMETHODOLOGYANALYSIS

In this work, we do not argue aboutcorrectness, completeness, and consis-tency of ontologies, and we do not pointout special characteristics that specific



development languages and systemsshould sustain. Moreover, in this chapter,we will not focus upon the ontologies lan-guages, even if these would be an inter-esting research topic. Although the evolu-tion of languages is, indeed, a single streamand focuses upon expressive power andcomputational expressive, it is out of thescope of this chapter.

The first phase of the investigationdocumented in this chapter had the ob-jective of reviewing the existing method-ologies for the creation of a domain on-tology from scratch. We discovered thatin the existing methodologies, there is acore structure that is independent of bothlanguages and tools; therefore, we ana-lyze ways in which ontology developerscan create their own ontology and prac-tices that should be adopted by develop-ers to create the most effective ontology.In particular, we want to measure whatare the common elements that each meth-odology take into account, in which waya methodology sustains the user in theontology creation processes, and hownon-expert people can adopt a method-ology and create their own representa-tions. Therefore, in the following section,some important methodologies will bedescribed and analyzed. We tried to de-fine:

• names of the phases;• elements and knowledge that should be

in input in each phase;• description of the processes and op-

erations that are developed in eachphase; and

• expected output.

Unfortunately, we do not have all theframework’s elements for all of the fol-lowing methodologies, because some ofthem are more research oriented and arenot focused on the description of how us-ers and developers can utilize them in theprocess of ontologies creation. Althoughthese methodologies are not well de-scribed in existing papers, we decided toanalyze them due to their importance inthe Semantic Web community.

In Section 4, we survey the mostrelevant methodologies of recent investi-gations. We cannot be exhaustive for atleast three reasons: (1) in the practice ofSemantic Web applications and in severalreal cases of intelligent information sys-tems, people developed their own meth-odologies, which makes almost impossiblea complete review; (2) several method-ologies have been developed as an illus-tration of the principles that inspired toolsand languages but are essentially equiva-lent to other methodologies; and (3) sev-eral cases of implicit methodologies exist;in particular, in the specific context of lan-guage or tools.

SOME RELEVANTMETHODOLOGIES

In computer science, knowledgemanagement, knowledge representation,and other fields, a lot of languages andtools are developed with the aim of help-ing people and system developers creategood and effective ontologies. In particu-lar, a lot of tools help people create, ei-ther manually or semi-automatically, cat-egories, partonomies, taxonomies, and so



on. Indeed, behind these tools and tech-niques, different approaches, methods,and techniques are used to develop a bigquantity of heterogeneous ontologies. Inthis section, we will describe some of them,and we will try to compare the more sig-nificant principles that are sustaining theseapproaches and that are all described inexisting scientific papers.

DOLCE: Descriptive Ontologyfor Linguistic and

Cognitive Engineering

The main authors’ idea is to developnot a monolithic module, but rather a li-brary of ontologies (WonderWeb Foun-dation Ontologies Library) that allowsagents to understand one another despiteforcing them to interoperate by adoptionof a single ontology (Masolo et al., 2002).The authors intend the library to be:

• minimal: the library is as general aspossible, including only the most reus-able and widely applicable upper-levelcategories;

• rigorous: ontologies are characterizedby means of rich axiomatizations;

• extensively researched: modules inlibraries are added only after carefulevaluation by experts and after consul-tation with canonical works.

One of the first modules of their foun-dational ontologies library is a Descrip-tive Ontology for Linguistic Cognitive En-gineering (DOLCE). DOLCE is an on-tology of particulars and refers to cogni-tive artifacts that depend on human per-ception, cultural imprints, and social con-

ventions. Their ontology derives from arm-chair research in particular (in other terms,it is the result of an intellectual specula-tion), referring to enduring and perduringentities from philosophical literature.

Finally, basic functions and relations(according to the methodology introducedby Gangemi, Pisanelli & Steve [1998])should be:

• general enough to apply to multipledomains;

• sufficiently intuitive and well studied inthe philosophical literature;

• held as soon as their relata are given,without mediating additional entities.

The methodology that sustain thistype of ontologies is based on a few start-ing points for building new ontologies:

1. Determine what things there are in thedomain to be modeled.

2. Develop easy and rigorous compari-sons among different ontological ap-proaches.

3. Analyze, harmonize, and integrate ex-isting ontologies and meta-data stan-dards.

4. Describe a foundational ontology onpaper, using a full first-order logic withmodality.

5. Isolate the part of the axiomatization thatcan be expressed in OWL and imple-ment it.

6. Add the remaining part in the form ofKIF comments attached to OWL con-cepts.

This methodology doesn’t describephases that a developer should follow in



ontology creation, but rather focuses moreon expressivity problems, on partial align-ment to lower levels of WordNet, and onmuch more philosophical aspects of theontology creation. Although it’s not pos-sible to complete our framework, we de-cided to add this methodology becausewe consider DOLCE as one of the mostrepresentative methodologies in the field.

Ontology Development 101

This methodology has been devel-oped by the authors involved in ontologyediting environment such as Protégé-2000, Ontolingua, and Chimaera (Noy &McGuinnes, 2001). They propose a verysimple guide based on iterative design thathelps developers, even the non-expertdeveloper, to create an ontology usingthese tools. According to our framework,the sequence of steps to develop an on-tology is described well in the Table 1.

OTK Methodology

The methodology developed withinthe On-To-Knowledge project is calledOTK Methodology, and it is focused onapplication-driven development of ontol-ogy during the introduction of ontology-based knowledge management systems(Fensel et al., 2000; Lau & Sure, 2002;Sure et al., 2002; Sure, 2003). The mainaim of this methodology is to sustain a newprocess based on human issues, in whichexperts in industrial contexts are capableof creating and managing their own ontol-ogy, and ontology engineers are allowedto create effective ontologies. In otherwords, according to this methodology,

strong efforts should be focused on physi-cal presence of engineering and industrialexperts, brainstorming processes (in par-ticular, during the early stages of ontologyengineering, especially for domain expertsnot familiar with modelling), and advancedtools for supporting ontology creation.According to our framework analysis, thesequence of the steps to develop an on-tology is described well in the Table 2.

Methontology

One of the most famous ontologydesign methodologies (supported by on-tology engineering environment WebODE)is “Methontology.” It tries to define theactivities that people need to carry outwhen building an ontology (Fernández,Gòmez-Pérez, & Juristo, 1997). In otherwords, a flow of ontology developmentprocesses for three different processes:management, technical, and supporting.

The ontology development processis composed of the following steps:

• project management activities include:• planning: it identifies which tasks

are to be performed, how they willbe arranged, how much time andwhat resources are needed fortheir completion;

• control: it guarantees that plannedtasks are completed in the man-ner in which they were intendedto be performed;

• quality assurance: it assures thatthe quality of each and every prod-uct output is satisfactory.

• development: oriented activities in-clude:

https://www.researchgate.net/publication/2412399_On-To-Knowledge_Ontology-based_Tools_for_Knowledge_Management?el=1_x_8&enrichId=rgreq-41f34b87b31382cad16ff8c61298e8e8-XXX&enrichSource=Y292ZXJQYWdlOzIyMDEyMzg1NDtBUzoxODgxMDg4MDA3OTg3MjJAMTQyMTg2MDA0MTQ0Nw==

https://www.researchgate.net/publication/221430324_Introducing_Ontology-based_Skills_Management_at_a_Language_Insurance_Company?el=1_x_8&enrichId=rgreq-41f34b87b31382cad16ff8c61298e8e8-XXX&enrichSource=Y292ZXJQYWdlOzIyMDEyMzg1NDtBUzoxODgxMDg4MDA3OTg3MjJAMTQyMTg2MDA0MTQ0Nw==

https://www.researchgate.net/publication/243772462_Ontology_Development_101_A_Guide_to_Creating_Your_First_Ontology?el=1_x_8&enrichId=rgreq-41f34b87b31382cad16ff8c61298e8e8-XXX&enrichSource=Y292ZXJQYWdlOzIyMDEyMzg1NDtBUzoxODgxMDg4MDA3OTg3MjJAMTQyMTg2MDA0MTQ0Nw==


https://www.researchgate.net/publication/36451372_Methodology_tools_case_studies_for_ontology_based_knowledge_management?el=1_x_8&enrichId=rgreq-41f34b87b31382cad16ff8c61298e8e8-XXX&enrichSource=Y292ZXJQYWdlOzIyMDEyMzg1NDtBUzoxODgxMDg4MDA3OTg3MjJAMTQyMTg2MDA0MTQ0Nw==



• specification: it states why theontology is being built, what its in-tended uses are, and who are theend-users;

• conceptualization: structures thedomain knowledge as meaningfulmodels at the knowledge level;

• formalization: transforms theconceptual model into a formal orsemi-computable model;

• implementation: builds comput-able models in a computational lan-guage.

• support activities: include a series ofactivities performed at the same timeas development-oriented activities:• knowledge acquisition;• evaluation: it makes a technical

judgment of the ontologies, theirassociated software environment,

Table 1: Ontology development 101 methodology

Name of the Phase Input Phase Description Output Determine domain and scope of the ontology

Nothing. It is the first step

Definition of - what is the domain that the ontology will cover, - what ontology will be used, - what types of question the ontology should provide

answers to (competency questions are very important in this domain; they allow the designer to understand when ontology contains enough information and when it achieves the right level of detail or representation),

- who will use and maintain the ontology.

The resulting document may change during the whole process, but at any time, this documentation helps to limit the scope of the model.

Consider reusing existing ontologies

Documents with the domain and the scope of the ontology

Looking for other ontologies that are defining the domain. There are libraries of reusable ontologies on the Web and in literature (e.g., Ontolingua ontology library, DAML ontology library, UNSPSC, RosettaNet, and DMOZ)

One or more domain ontologies, or part of them with their description

Enumerate important terms in the ontology

Documents with the domain, the scope of the ontology, and libraries on the domain

Write a list of all terms used within the ontology, and describe the terms, their meanings, and their properties

Terms and important aspects to model in the ontology

Define the classes and the class hierarchy

Important terms in the ontology, domain, and scope description

There are several possible approaches in developing a class hierarchy:

- top-down development process starts with the definition of the most general concepts in the domain and subsequent specialization of the concepts;

- bottom-up development process goes in the opposite direction;

- a combination development process is a combination of the top-down and bottom-up approaches

Classes and class hierarchy

Define the properties of classes-slot

The taxonomy, and the domain and scope description

Add all the necessary properties and information that allow the ontology to answer the competency questions

Classes, class hierarchy, and properties

Define the facets of the slots

Slots and classes

There are different facets describing the value type, allowed values, the number of the values, and other features of the values the slot can take: slot cardinality, slot-value type, domain, and range

Ontology

Create instances

The ontology

Create individual instances of classes in the hierarchy, which means choosing a class, creating an individual instance of that class, and filling in the slot values.

Ontology and the modeled domain



Table 2. OTK methodology

Name of the Phase Input Phase Description Output Feasibility study: identify problem/opportunity areas and potential solutions, and put them into a wider organizational perspective. In general, a feasibility study serves as a decision support for economical, technical, and project feasibility in order to select the most promising focus area and target solution

Nothing. It is the first step

The process of studying the feasibility of the organization, the task, and the agent model proceeds in the following steps: - carry out a scoping and problem analysis study,

consisting of two parts: o identifying problem/opportunity areas and

potential solutions and putting them into a wider organizational perspective;

o deciding about economic, technical, and project feasibility in order to select the most promising focus area and target solution;

- carry out an impacts and improvements study for the selected target solution, again consisting of two parts: o gathering insights into the interrelationships

among the business task, actors involved, and use of knowledge for successful performance, and what improvements may be achieved here;

o deciding about organizational measures and task changes in order to ensure organizational acceptance and integration of a knowledge system solution.

The resulting document may change during the whole process. Indication of the most promising area and target solutions and of measures and task changes in order to ensure organizational acceptance and integration of knowledge systems solutions. An ontology requirements specification document (ORDS) should be formed. The ORSD describes what an ontology should support, sketching the planned area of the ontology application and listing, for example, valuable knowledge sources for the gathering of the semi-formal description of the ontology.

Kick-off phase

It starts with an ORSD

The ORSD should guide an ontology engineer to decide about inclusion and exclusion of concepts and relations and the hierarchical structure of the ontology. In this early stage, one should look for already developed and potentially reusable ontologies.

The result will be a document containing: - goal, domain, and scope of the

ontology; - design guidelines; - knowledge sources; - (potential) users and usage

scenarios; - competency questions; supported

applications. Refinement phase

Specification given by the kick-off phase

The goal of the refinement phase is to produce a mature and application-oriented “target ontology” according to the specification given by the kick-off phase. This phase is divided into different subphases: - a knowledge elicitation process with domain experts

based on the initial input from the kickoff phase. - a formalization phase to transfer the ontology into

the target ontology expressed in formal representation language like DAML+OIL.

- The knowledge about the domain

is captured from domain experts in the previously mentioned competency questions or by using brainstorming techniques;

- To formalize the initial semi-formal description of the ontology, we first form a taxonomy out of the semi-formal description of the ontology and add relations other than the “is-a” relation, which forms the taxonomical structure.

Evaluation phase

The refinement results

The refinement phase is closely linked to the evaluation phase. If the analysis of the ontology in the evaluation phase shows gaps or misconceptions, the ontology engineer takes these results as an input for the refinement phase. It might be necessary to perform several (possibly tiny) iterative steps to reach a sufficient level of granularity and quality

It serves as a proof for the usefulness of developed ontologies and their associated software environment.

Application and evolution phases

Define strict rules for the update, insert and delete processes within ontologies

User guide for future application and evolution phases



Table 3. Methontology through our framework

Name of the Phase Input Description Output Planning

Nothing: first step

Plan the main tasks to be done, the way in which they will be arranged, the time and resources that are necessary to perform these tasks

A project plan

Specification

A series of question

such as: “Why is this ontology being built and what are its intended uses and end-users?”

Identify ontology’s goals

Ontology requirements specification document, specifying purposes and scopes. Its goal is to produce either an informal, semi-formal, or formal ontology specification document written in natural language, using a set of intermediate representations or using competency questions, respectively. The document has to provide at least the following information: the purpose of the ontology (including its intended uses, scenarios of use, end-users, etc.); the level of formality used to codify terms and meanings (highly informal, semi-informal, semi-formal, rigorously formal ontologies); the scope; its characteristics and granularity. Properties of this document are: concision, partial completeness, coverage of terms, the stopover problem and level of granularity of ache and every term, and consistency of all terms and their meanings.

Conceptualization

A good specification document

Conceptualize in a model that describes the problem and its solution. To identify and gather all the useful and potentially usable domain knowledge and its meanings

A complete glossary of terms (including concepts, instances, verbs, and properties). Then, a set of intermediate representations such as concepts classification trees, verb diagram, table of formulas, and table of rules. The aim is to allow the final user to ascertain whether or not an ontology is useful and to compare the scope and completeness of several ontologies, their reusability, and shareability.

Formalization

Conceptual model

Transform conceptual model into a formal or semi-compatible model, using frame-oriented or description logic representation systems

Formal conceptualization

Integration

Existing ontologies and the formal model

Processes of inclusion, polymorphic refinement, circular dependencies, and restriction. For example, select meta ontologies that better fit the conceptualisation

Implementation

Formal model

Select target language

Create a computable ontology

Maintenance

Including, modifying definition in the ontology

Guidelines for maintaining ontologies

Acquisition

Searching and listing knowledge sources through non-structured interviews with experts, informal text analysis, formal text analysis, structured interviews with experts to have detailed information on concepts, terms, meanings, and so on

A list of the sources of knowledge and a rough description of how the process will be carried out and what techniques will be used.

Evaluation

Computable ontology

Technical judgment with respect to a frame of reference

A formal and correct ontology

Documentation

Specification document must have the property of concision



and documentation with respect toa frame of reference;

• integration;• documentation.

In Table 3, we provide a listing ofthe single phases of the previously men-tioned methodology. It is also worth de-picting one fundamental fact aboutmethontology: its specific explicit avoid-ance to commit to the user’s interest inthe development of tools that automate allthe phases.

A Comprehensive Framework forMultilingual Domain Ontologies

The authors (Lauser et al., 2002) usethe Methontology methodology defined byFernández, Gòmez-Pérez, & Juristo(1997) and stress the specific actions forsupporting the creation process for ontol-ogy-driven conceptual analysis.

The domain ontology is built usingtwo different knowledge acquisition ap-proaches (see Table 4):

• acquisition approach 1: creation ofthe core ontology. A small core ontol-ogy with the most important domainconcepts and their relationships is cre-ated from scratch. This stage is com-prised basically of the first three stepsof Methontology development activi-ties: requirement specification,conceptualization of domain knowl-edge, and formalization of the concep-tual model in a formal language;

• acquisition approach 2: deriving a do-main ontology from thesaurus;

• ontology merging: merging the manu-ally created core ontology and the de-rived ontology using thesaurus terms;

• ontology refinements and exten-sion: the frequent domain terms areused as possible candidate concepts orrelationships for extending the ontology.These terms have to be assessed bysubject specialists and checked for rel-evance to the ontology.

TOVE Methodology

Toronto Virtual Enterprise (TOVE)is a methodology for ontological engineer-ing that allows the developer to build on-tology following these steps:

• motivating scenarios: the startingpoint is the definition of a set of prob-lems encountered in a particular enter-prise;

• informal competency questions:based on the motivating scenario, it isthe definition of ontology requirementsdescribed as informal questions that anontology must be able to answer;

• terminology specification: the ob-jects, attributes, and relations of the on-tology are formally specified (usuallyfirst-order logic);

• formal competency question: the re-quirements of the ontology are formal-ized in terms of the formally defined ter-minology

• axiom specification: axioms thatspecify the definition of terms and con-straints on their interpretations are givenin first-order logic;



• completeness theorems: an evalua-tion stage that assesses the competencyof the ontology by defining the condi-tions under which the solutions to thecompetency question are complete.

The most distinctive aspect of TOVEis the focus on maintenance using formaltechniques to address a limited number ofmaintenance issues (Gruninger, Atefi, &Fox, 2000; Kim, Fox, & Gruninger,1999). According to our framework, thismethodology can be summarized as shownin Table 5.

DILIGENT Methodology

The DILIGENT methodology(Tempich et al., 2004) aims at creating a

set of effective ontologies that the user canshare and, at the same time, expand forlocal use at their will and individual needs.The aim of this methodology is to over-come the misunderstanding of ontologiesthat are created by a very small group ofpeople (the ontology engineers and thedomain experts who represent the users)but are utilized by a huge number of us-ers. The authors postulate that:

Ontology engineering must takeplace in a Distributed evolvInG andLoosely-controlled setting.

(Tempich et al., 2004, p. )With DILIGENT, the authors pro-

vide a process template for distributedengineering of knowledge structures andintend to extend it toward a fully workedout and multiply-tested methodology. Ana-

Table 4. Multilingua domain methodology

Name of the Phase Input Description Output Acquisition approach 1

Nothing: first step

The creation of the core ontology. A small core ontology with the most important domain concepts and their relationships is created from scratch.

The goal of this step is to define a list of frequent terms and a list of domain specific documents to analyze.

Acquisition approach 2

Nothing: first step

Deriving a domain ontology from thesaurus. A thesaurus consists of descriptive keywords linked by a basic set of relationships. The keywords are descriptive in terms of the domain in which they are used. The relationships either may describe a hierarchical relation or an interhierarchical relation.

The goal of this step is to refine an RDFS ontology model to develop a pruned ontology and a list of frequent terms.

Ontology merging

Manually created core ontology and thesaurus terms

Merging the manually created core ontology and the derived ontology using thesaurus terms.

A first version of the ontology

Ontology refinements and extension

The first version of the ontology

The frequent domain terms are used as possible candidate concepts or relationships for extending the ontology. These terms have to be assessed by subject specialists and checked for relevance to the ontology

The final ontology version

https://www.researchgate.net/publication/220556867_Ontologies_to_Support_Process_Integration_in_Enterprise_Engineering?el=1_x_8&enrichId=rgreq-41f34b87b31382cad16ff8c61298e8e8-XXX&enrichSource=Y292ZXJQYWdlOzIyMDEyMzg1NDtBUzoxODgxMDg4MDA3OTg3MjJAMTQyMTg2MDA0MTQ0Nw==


https://www.researchgate.net/publication/244167097_A_case_study_in_supporting_DIstributed_Loosely-controlled_and_evolvInG_Engineering_of_oNTologies_DILIGENT?el=1_x_8&enrichId=rgreq-41f34b87b31382cad16ff8c61298e8e8-XXX&enrichSource=Y292ZXJQYWdlOzIyMDEyMzg1NDtBUzoxODgxMDg4MDA3OTg3MjJAMTQyMTg2MDA0MTQ0Nw==

https://www.researchgate.net/publication/241026540_An_Ontology_for_Quality_Management_-_Enabling_Quality_Problem_Identification_and_Tracing?el=1_x_8&enrichId=rgreq-41f34b87b31382cad16ff8c61298e8e8-XXX&enrichSource=Y292ZXJQYWdlOzIyMDEyMzg1NDtBUzoxODgxMDg4MDA3OTg3MjJAMTQyMTg2MDA0MTQ0Nw==




lyzing DILIGENT, we can say that it isbased on dominating roles and five high-level phases that are described in Table 6.The key roles are several experts with dif-ferent and complementary competencies,involved in collaboratively building thesame ontology.

Business Object Ontology

The authors Izumy and Yamaguchi(2002) have used this methodology todevelop an ontology for business coordi-nation. They constructed the business ac-tivity repository by employing WordNetas a general lexical repository. They haveconstructed the business object ontologyin the following way:

• concentrating the case-study models ofe-business and extracting the taxonomy;

• counting the number of appearances ofeach noun concept;

• comparing the noun hierarchy ofWordNEt and the taxonomy obtainedand adding the number counted for thesimilar concepts;

• choosing the main concept with highscores as upper concepts and buildingupper ontologies by giving all the nounsthe formal is-a relation;

• merging all the noun hierarchies ex-tracted from the whole process.

Although this methodology is quitegeneral, it is important to notice that thecontinuous interdependencies among busi-

Table 5. TOVE methodology

Name of the Phase Input Description Output Motivating scenario

Nothing: first step

It’s the starting point, the definition of a set of problems encountered in a particular enterprise

Identify intuitively possible applications and solutions.

Informal competency questions

The motivating scenario

It is the definition of ontology requirements described as informal questions that an ontology must be able to answer

Questions: terminology Answers: axioms and formal definitions

Terminology specification

Informal questions

The objects, attributes, and relations of the ontology are formally specified (usually first-order logic)

Objects: constants and variables Attributes and relations: functions and predicates

Formal competency question

Objects, attributes, and relations

The requirements of the ontology are formalized in terms of the formally defined terminology

Formally defined terminology

Axiom specification

Formally defined terminology

Axioms that specify the definition of terms and constraints on their interpretations are given in first-order logic;

First-order sentences, using predicates of ontology

Completeness theorems

First-order sentences

An evaluation stage that assesses the competency of the ontology by defining the conditions under which the solutions to the competency question are complete.

Conditions under which the solutions to the competency question are complete



ness people, industries, and case studiesare the key factors in an effective ontol-ogy.

A Natural Language InterfaceGenerator (GISE)

In Gatius and Rodríguez (1996), theauthors developed three process steps tobuild a domain ontology:

• first step: building and maintenance of:• the general linguistic knowl-

edge: It includes linguistic ontol-ogy that covers the syntactic and

semantic information needed togenerate the specific grammarsand a general lexicon that includesfunctional, domain, and applica-tion independent lexical entries;

• the general conceptual knowl-edge: it includes the domain andapplication-independent concep-tual information as well as themeta-knowledge that will beneeded in the following steps;

• second step: definition of the applica-tion in terms of the conceptual ontol-ogy. Both the domain description andthe task structure description must be

Table 6. DILIGENT methodology

Name of the Phase Input Description Output Build

Domain experts, users, knowledge engineers, and ontology engineers

The definition of an initial ontology. The team involved in building the initial ontology should be relatively small in order to find more easily a small and consensual first version of the shared ontology

Built an initial ontology. It is not required completeness of the initial shared ontology with respect to the domain.

Local adaptation

Core ontology

Users work in the core ontology and adapt it to their local needs, business, and system of artefacts.

Logging local adaptation, the control board collects change requests to the shared ontology

Analysis

Local ontologies and the local requests

The board analyzes the local ontologies and tries to identify similarities in users’ ontologies. One of the crucial activities of the board is to decide which changes should be introduced in the shared ontology

A refined version of the shared ontology, users’ changes and board evaluations.

Revision

Shared ontology and local ontologies. The board should have well-balanced and representative participation of the different kinds of participants involved in the process.

The board should regularly revise the shared and local ontologies. The board should evaluate the ontology, mostly from a technical and domain point of view. The board should assure some compatibility with previous versions.

Final revision of the shared ontology, which entails its evolution

Local update

New version of the shared ontology

Users can update their own local ontologies to better suit the new version of the shared ontology for their local needs.

New locally defined ontologies represent new concepts and criteria with new meaning. The locally defined ontologies constitute new versions of the locally shared ontology.



built and linked to the appropriate com-ponents of the conceptual ontology;

• third step: definition of the controlstructure. It includes:

• the meta-rules for mapping ob-jects in the domain ontology withthose in the task ontology;

• the meta-rules for mapping theconceptual ontology onto the lin-guistic ontology and those forallowing the generation of the spe-cific interface knowledge sources,mainly the grammar and the lexicon.

This methodology seems to be notspecific enough to analyze it according toour framework. In any case, the most im-portant point of this methodology is that itbuilds and maintains the general linguisticknowledge and conceptual ontology. Thiswork is devoted to providing a mecha-nism based on the performance of a set ofcontrol rules relating general linguisticknowledge to the applications specifica-tions in order to obtain automatically thenatural language best suited for each ap-plication. The representation of the rel-evant knowledge in the process of gener-ating an interface in separate data struc-tures (conceptual ontology, linguistic on-tology, general lexicon, and control rules)gives great flexibility in adapting linguistic re-sources to different applications and users.

SOME CONSIDERATIONCOMPARING THESEMETHODOLOGIES

Although there are considerable dif-ferences between the methodologies pre-

viously described, a number of pointsclearly emerge:

• many of the methodologies take a spe-cific task as a starting point: choosingdomains and categories that allow thecorrect representation. From one pointof view, it focuses on the acquisition,provides the potential for evaluation,and provides a useful description of thecapabilities of the ontology, expressedas the ability to answer well-definedcompetency questions. On the otherhand, it seems to provide limitations tothe reuse of the ontology and to thepossible interactions among ontologies;

• there are two different types of meth-odology models: the stage-based mod-els (represented, for example, byTOVE) and evolving prototype mod-els (represented by Methontology).Both approaches have advantages anddisadvantages: the first one seems moreappropriate when purposes and re-quirements of the ontology are clear;the second one is more useful when theenvironment is dynamic and difficult tounderstand;

• most of the time, there are both infor-mal descriptions of the ontology andformal embodiment in an ontology lan-guage. These often are developed inseparate stages, and this separation in-creases the gap between real worldmodels and executable systems.

The common point in these method-ologies is the starting point for creating anontology that could arise from differentsituations (Ushold, 2000; Ushold &Gruninger, 1996):



• from scratch;• from existing ontologies (whether glo-

bal or local);• from corpus of information sources

only;• a combination of the latter two ap-

proaches.

Normally, methods to generate on-tology could be summarized (Ding & Foo,2002):

• bottom-up: from specification to gen-eralization;

• op-down: from generalization to speci-fication such as KACTUS ontology;

• middle-out: from the most importantconcepts to generalization and special-ization such as Enterprise ontology andMethondology.

Picture 1 shows how different on-tologies deal with both the top-down ap-proach and the bottom-up approach.

As depicted in Picture 1, on oneside, the Naïve approaches present a lowlevel of quality in both approaches and arenot taken into account in this analysis. Al-though different technology-driven meth-odologies are used, new methodologiesand tools are needed for creating andmanaging local schemata. These method-ologies and tools should allow both thecreation of a schemata from scratch(analyzing documents, repeated occur-rences within databases, etc.) and themanagement of sense-making processeson concepts. This framework is called inthe picture “more sophisticated ap-proaches.” These should provide guide-lines to assist the knowledge owner in

Picture 1. Methodologies according the top down and bottom up approach

Bottom-Up Approach

Top-Down Approach

Low

High

High

Low

DOLCE

GISE

Methontology

Ushold’s methodology

TOVE

Multilingual methontology

OTK

101

Business object ontology

Naïve approaches

More sophisticated approaches

DILIGENT



making choices at different levels, from thehigh-level structure of the ontology to thefine detail of whether or not to include dis-tinctions. These frameworks should aimat satisfying all the knowledge the ownerneeds and merging two different require-ments, a more adequate representation ofa local domain and a very effective devel-opment of a top-level ontology. In thisstudy, the authors would consider thatmanually constructed ontologies are time-consuming, labor intensive, and error-prone (Ding & Foo, 2002) but are nec-essary to define a domain in which thequality and general comprehension of theontology are good.

There are also a number of generalontology design principles that are pro-posed:

• Guarino, Borgo, Masolo, Gangemi, andOltramari (2002) proposed a method-ology to design a formal ontology, inparticular, defining domain, identifyinga basic taxonomic structure, and point-ing to a role;

• Uschold and Gruninger (1996) pro-posed a skeletal methodology for build-ing ontologies via a purely manual pro-cess: identify purpose and scope, on-tology capture (identification of keyconcepts and relationships and provi-sion of definitions), ontology coding(committing to the basic terms for on-tology), language, integrating existingontologies, evaluation, documentation,guidelines;

• Other researchers separate the con-struction and definition of complex ex-pression from its representation;

Other authors proposed a numberof desirable criteria for the final generatedontology to be open and dynamic, scal-able and interoperable, easily maintained,and context independent.

Finally, there is no one correct wayto model a domain; there are always vi-able alternatives. Most of the time, the bestsolution depends on the application thatthe developer has in mind and the toolsthat the developer uses to develop theontology. In particular, we can notice someemerging problems (Blázquez et al., 1998;Gómez-Pérez, 2001):

• the correspondence between existingmethodologies for building ontologiesand environments for building ontolo-gies causes this consequences:• conceptual models are implicit in

the implementation codes, and areengineering process usually is re-quired to make the conceptualmodels explicit;

• ontological commitments and de-sign criteria are implicit in the on-tology code;

• ontology developer preferences ina given language condition theimplementation of the acquiredknowledge. So when people codeontologies directly in a target lan-guage, they are omitting the mini-mal encoding bias criterion definedby Gruber (1993);

• most of the tools only give support fordesigning and implementing the ontolo-gies, but they do not support all theactivities of the ontology life-cycle;

https://www.researchgate.net/publication/50426325_Building_Ontologies_at_the_Knowledge_Level_using_the_Ontology_Design_Environment?el=1_x_8&enrichId=rgreq-41f34b87b31382cad16ff8c61298e8e8-XXX&enrichSource=Y292ZXJQYWdlOzIyMDEyMzg1NDtBUzoxODgxMDg4MDA3OTg3MjJAMTQyMTg2MDA0MTQ0Nw==

https://www.researchgate.net/publication/244437488_A_Translation_Approach_to_Portable_Ontology_Specifications?el=1_x_8&enrichId=rgreq-41f34b87b31382cad16ff8c61298e8e8-XXX&enrichSource=Y292ZXJQYWdlOzIyMDEyMzg1NDtBUzoxODgxMDg4MDA3OTg3MjJAMTQyMTg2MDA0MTQ0Nw==



• ontology developers may find it diffi-cult to understand implemented ontolo-gies or even to build a new ontology.

ACKNOWLEDGMENTS

The first author gratefully thanks theEuropean Union under Grant GRD1-2001-40739 (the UPTUN Project) forsupporting the investigation that we havedocumented in this chapter.

The UPTUN-project, “Cost-effec-tive, sustainable and innovative upgradingmethods for fire safety in existing tunnels”is being carried out in the framework ofthe “Competitive and Sustainable GrowthProgramme” (project GRD1-2001-40739, Contract G1RD-CT-2002-0766), with a financial contribution of theEuropean Community.

We also both gratefully thank Cre-ative Consulting s.r.l. for the grant OASI(Ontologies of Artefacts and Services toIndustry).

REFERENCES

Ashby, W.R. (1956). An introduction tocybernetics. New York: John Wiley &Sons.

Benerecetti, M., Bouquet, P., & Ghidini,C. (2000). Contextual reasoning dis-tilled. Journal of Theoretical and Ex-perimental Artificial Intelligence,12(3), 279-305.

Blázquez, M., Fernández, M., García-Pinar, J.M., & Gómez-Pérez, A.(1998). Building ontologies at theknowledge level using the ontology de-sign environment. Proceedings of theKAW’98, Banff, Canada.

Bonifacio, M., Bouquet, P., & Cuel, R.(2002). The role of classification(s) indistributed knowledge management.Proceedings of the 6th InternationalConference on Knowledge-Based In-telligent Information EngineeringSystems & Allied Technologies (KES2002). Podere d’Ombriano, Crema,Italy.

Chandler, A.D. (1962). Strategy andstructure: Chapters in the history ofthe industrial enterprise. Cambridge,MA: MIT Press.

Cocchiarella, N.B. (1991). Formal ontol-ogy. In H. Burkhardt, & B. Smith(Eds.), Handbook of metaphysics andontology. Munich, Germany:Philosophia Verlag.

Ding, Y., & Foo, S. (2002). Ontology re-search and development, Part 1—Areview of ontology generation. Jour-nal of Information Science, 3(28),123-136.

Dougherty, D. (1992). Interpretative bar-riers to successful product innovationin large firms. Organization Science,3(2).

Fauconnier, G. (1985). Mental spaces:Aspects of meaning construction innatural language. MIT Press.

Fensel, D. (2000). Ontologies: A silverbullet for knowledge managementand electronic commerce. Springer.

Fensel, D., van Harmelen, M., Klein, &Akkermans, H. (2000). On-to-knowl-edge: Ontology-based tools for knowl-edge management. Proceedings ofeBusiness and eWork, Madrid, Spain.

Fernández, M., Gòmez-Pérez, A., &Juristo, N. (1997). METHON-TOLOGY: From ontological art to-

https://www.researchgate.net/publication/220079943_Contextual_reasoning_distilled?el=1_x_8&enrichId=rgreq-41f34b87b31382cad16ff8c61298e8e8-XXX&enrichSource=Y292ZXJQYWdlOzIyMDEyMzg1NDtBUzoxODgxMDg4MDA3OTg3MjJAMTQyMTg2MDA0MTQ0Nw==











































wards ontological engineering. Pro-ceedings of the AAAI Spring Sympo-sium on Ontological Engineering,Stanford, California.

Gangemi, A., Pisanelli, D.M. & Steve, G.(1998). Ontology integration: Experi-ences with medical terminologies. In N.Guarino (Ed.), Formal ontology in in-formation systems (pp. 163-178).IOS Press.

Gatius, M., & Rodríguez, H. (1996). Adomain-restricted task-guided naturallanguage interface generator. Proceed-ings of the Second Edition of theWorkshop Flexible Query AnsweringSystems (FQAS’96), Denmark.

Ghidini, C., & Giunchiglia, F. (2001).Local models semantics, or contextualreasoning = locality + compatibility. Ar-tificial Intelligence, 127(2), 221-259.

Goffman, I. (1974). Frame analysis.New York: Harper & Row.

Gómez-Pérez, A. (2001). A proposal ofinfrastructural needs on the frameworkof the semantic Web for ontology con-struction and use. Proceedings of theFP6 Programme Consultation Meet-ing 9.

Gruber, T.R. (1993). A translation ap-proach to portable ontology specifica-tions. Knowledge Acquisition, 5(2),199-220.

Gruninger, M., Atefi, K., & Fox, M.S.(2000). Ontologies to support processintegration in enterprise engineering.Computational and MathematicalOrganization Theory, 6(4), 381-394.

Hamel, C., & Prahalad, G. (1990, May-June). The core competences of thecorporation. Harvard Business Re-view.

Isumi, N., & Yamaguchi, T. (2002). Se-mantic coordination of Web servicesbased on multi-layered repository.PRICAI, 597.

Jasper, R., & Ushold, M. (1999). Aframework for understanding and clas-sifying ontology applications. Proceed-ings of the 12th Workshop on Knowl-edge Acquisition, Modelling, andManagement, Ban®, Canada.

Kim, H.M., Fox, M.S., & Gruninger, M.(1999). An ontology for quality man-agement—Enabling quality problemidentification and tracing. BT Technol-ogy Journal, 17(4), 131-140.

Kuhn, T. (1979). The structure of scien-tific revolutions. Chicago, IL: Univer-sity of Chicago Press.

Lau, T., & Sure, Y. (2002). Introducingontology-based skills management at alanguage insurance company.Modellierung, 123-134.

Lauser, B., et al. (2002). A comprehen-sive framework for building multilingualdomain ontologies: Creating a proto-type biosecurity ontology. Proceedingsof the Internatiional Conference onDublin Core and Metadata for e-Communities.

Masolo, C., Borgo, S., Gangemi, A.,Guarino, N., & Oltramari, A. (2002).Wonderweb deliverable d17. Interme-diate Report 2.0, ISTC-CNR.

Maturana, H.R., & Varala, F.J. (1980).Autopoiesis and cognition: The real-ization of the living. Dordrecht: D.Reidel.

Noy, N.F., & McGuinnes, D.L. (2001).Ontology development 101: A guideto creating your first ontology.

https://www.researchgate.net/publication/2351138_Ontology_Integration_Experiences_with_Medical_Terminologies?el=1_x_8&enrichId=rgreq-41f34b87b31382cad16ff8c61298e8e8-XXX&enrichSource=Y292ZXJQYWdlOzIyMDEyMzg1NDtBUzoxODgxMDg4MDA3OTg3MjJAMTQyMTg2MDA0MTQ0Nw==






https://www.researchgate.net/publication/221465819_Horacio_Rodriguez_A_Domain-Restricted_Task-Guided_Natural_Language_Interface_Generator?el=1_x_8&enrichId=rgreq-41f34b87b31382cad16ff8c61298e8e8-XXX&enrichSource=Y292ZXJQYWdlOzIyMDEyMzg1NDtBUzoxODgxMDg4MDA3OTg3MjJAMTQyMTg2MDA0MTQ0Nw==























https://www.researchgate.net/publication/272161456_Autopoiesis_and_Cognition_The_Realization_of_the_Living?el=1_x_8&enrichId=rgreq-41f34b87b31382cad16ff8c61298e8e8-XXX&enrichSource=Y292ZXJQYWdlOzIyMDEyMzg1NDtBUzoxODgxMDg4MDA3OTg3MjJAMTQyMTg2MDA0MTQ0Nw==



















https://www.researchgate.net/publication/2511701_A_Framework_for_Understanding_and_Classifying_Ontology_Applications?el=1_x_8&enrichId=rgreq-41f34b87b31382cad16ff8c61298e8e8-XXX&enrichSource=Y292ZXJQYWdlOzIyMDEyMzg1NDtBUzoxODgxMDg4MDA3OTg3MjJAMTQyMTg2MDA0MTQ0Nw==








Numagami, T., Ohta, T., & Nonaka, I.(1989). Self-renewal of corporate or-ganizations: Equilibrium, self-sus-taining, and self-renewing models[working paper]. Berkeley, CA: Uni-versity of California at Berkeley.

Sowa, J.F. (2000). Knowledge represen-tation. Logical, philosophical andcomputational foundations. Brooks/Cole.

Sure, Y. (2003). Methodology, tools andcase studies for ontology basedknowledge management [doctoralthesis]. University of Karlsruhe, De-partment of Economics and BusinessEngineering.

Sure, Y., Staab, S., & Angele, J. (2002).OntoEdit: Guiding ontology develop-ment by methodology and inferencing.Proceedings of the Confederated In-ternational Conferences CoopIS,DOA and ODBASE 2002, Irvine, Cali-fornia.

Tempich, C., Pinto, S., Staab, S., & Sure,Y. (2004). A case study in supportingdistributed, loosely-controlled andevolving engingeering of ontologies(DILIGENT). Proceedings of the 4thInternational Conference on Knowl-edge Management (I-Know’04),Graz, Austria.

Ushold, M. (2000). Creating, integratingand maintaining local and global ontolo-gies. Proceedings of the First Work-shop on Ontology Learning (OL-2000) in conjunction with the 14thEuropean Conference on ArtificialIntelligence (ECAI 2000).

Ushold, M., & Gruninger, M. (1996).Ontologies: Principles, methods, andapplications. Knowledge EngineeringReview, 11(2), 93-155.

Weick, E.K. (1979). The social psychol-ogy of organizing. McGraw-Hill.

Please provide bios

















A Survey on Ontology Creation Methodologies

Documents

Transcript of A Survey on Ontology Creation Methodologies