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

By. P. Victer Paul

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AuthorsAmal Zouaq,Member, IEEERoger Nkambou, Member, IEEEUniversity of Quebec at Montreal, Montre´al, CanadaE-mail: {zouaq.amal,

nkambou.roger}@uqam.ca

Ontology O= (C;R; A; Top)C represents a non-empty set of concepts

(including relation concepts and Top)R the set of assertions in which two or more

concepts are related to one anotherA 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

Knowledge Puzzle ProjectThe Knowledge Puzzle, an ontology-based platform

designed to facilitate domain knowledge acquisition for knowledge-based systems and especially for intelligent tutoring systems.

One of the Goals of the Knowledge Puzzle Project is to automatically generate a domain ontology from plain text documents and use this ontology as the domain model in computer-based education.

TEXCOMON, the Knowledge Puzzle Ontology Learning Tool, to extract concept maps from texts. It also explains how these concept maps are exported into a domain ontology

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 at the time the ontology was conceived and built.

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

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 transform instantiated patterns into semantic structures.

aims to discover:domain terms, concepts, concept attributes, taxonomic relationships, nontaxonomic relationships, axioms, and rules.

domain ontology evaluation techniques.Structural: Based on a set of metrics, structural evaluations

consider 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 judgmentComparative: 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

MetricsThe CMM evaluates the coverage of an

ontology for the given sought terms.The density measure expresses the degree of

detail or the richness of the attributes of a given concept.

The BEM calculates the betweenness value of each search term in the generated ontologies

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

Related WorkM. Poesio and A. Almuhareb, “Identifying Concept

AttributesUsing a Classifier,” Proc. Assoc. Computational Linguistics (ACL)Workshop Deep Lexical Acquisition, pp. 18-27, 2005.

P. Hayes, T. Eskridge, R. Saavedra, T. Reichherzer, M.Mehrotra, and D. Bobrovnikoff, “Collaborative KnowledgeCapture 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 for Question Answering,” Natural Language Eng., vol. 7, no. 4, pp. 343-360, 2001.

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