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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 21, NO. 11, NOVEMBER 2009

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By.

P. Victer Paul 

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Authors Amal Zouaq,Member, IEEE

Roger Nkambou, Member, IEEE

University of Quebec at Montreal,Montre´al, Canada

E-mail: {zouaq.amal, nkambou.roger}@uqam.ca

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Ontology

O= (C;R; A; Top)

C represents a non-empty set of concepts (includingrelation concepts and Top)

R the set of assertions in which two or more concepts arerelated to one another

 A the set of axioms

Top the highest level concept in the hierarchy.

R, itself, includes two subsets: H depicts the set of assertions for which relations are

taxonomic

N denotes those which are nontaxonomic

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Knowledge Puzzle Project The Knowledge Puzzle, an ontology-based platform

designed to facilitate domain knowledge acquisition forknowledge-based systems and especially for intelligenttutoring systems.

One of the Goals of the Knowledge Puzzle Project is toautomatically generate a domain ontology from plain textdocuments and use this ontology as the domain model incomputer-based education.

TEXCOMON, the Knowledge Puzzle Ontology LearningTool, to extract concept maps from texts. It also explainshow these concept maps are exported into a domainontology

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Why Automatic methods for

Domain Ontologies? ONTOLOGIES are the backbone of knowledge

representatio for the Semantic Web.

manual methods used to build domain ontologies are

not scalable. time- and effort-consuming

represent knowledge as a set structure established atthe time the ontology was conceived and built.

To minimize these drawbacks, automatic methods fordomain ontology building must be adopted.

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Focussed Problems

domain ontology learning and population from text

paper proposes a lexico-syntactic analysis

to extract concept maps from texts and transform them into a

domain ontology in a semiautomatic manner.

proposes a set of domain-independent patterns relying on

dependency grammar. work differsfrom the existing techniques

by the proposed patterns andthe methods used to transforminstantiated patterns into semantic structures.

aims to discover:domain terms, concepts, concept attributes,

taxonomic relationships, nontaxonomic relationships, axioms, and

rules.

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domain ontology evaluation techniques.

Structural: Based on a set of metrics, structural evaluationsconsider ontologies as graphs. structural metrics are the Class

Match Measure (CMM), the Density Measure (DEM), the

Betweenness Measure (BEM), and finally, the Semantic

Similarity Measure (SSM).

Semantic: rely on human expert judgment

Comparative: based on comparisons between the outputs of 

state-of-the-art tools and those of new tools such as

TEXCOMON, using the very same set of documents inorder to

highlight the improvements of new techniques

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Metrics The CMM evaluates the coverage of an ontology for

the given sought terms.

The density measure expresses the degree of detail orthe richness of the attributes of a given concept.

The BEM calculates the betweenness value of eachsearch term in the generated ontologies

the SSM, computes the proximity of the classes thatmatch the sought terms in the ontology.

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Related Work

M. Poesio and A. Almuhareb, “Identifying Concept AttributesUsing a Classifier,” Proc. Assoc. ComputationalLinguistics (ACL)Workshop Deep Lexical Acquisition, pp.18-27, 2005.

P. Hayes, T. Eskridge, R. Saavedra, T. Reichherzer,M.Mehrotra, and D. Bobrovnikoff , “CollaborativeKnowledgeCapture in Ontologies,” Proc. Third Int’l Conf.Knowledge Capture(K-CAP ’05), pp. 99-106, 2005.

D. Lin and P. Pantel, “Discovery of Inference Rules forQuestion Answering,” Natural Language Eng., vol. 7, no. 4,pp. 343-360, 2001.

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