Mining Domain Specific Texts and Glossaries to Evaluate and Enrich Domain Ontologies

Ontologies have been widely accepted as the most advanced knowledge representation model. They are among the most important building blocks of semantic web, hence, very crucial for the success of semantic web. This paper discusses a fast and efficient method to facilitate the evaluation and enrichment of domain ontologies using a text-mining approach. We exploit domain specific texts and glossaries or dictionaries in order to automatically generate g-groups and f-groups. These groups are sets of concepts/terms which have either taxonomic or non-taxonomic relationships among them. The domain expert ontology engineer reviews these generated groups and uses them to evaluate and enrich the domain ontology. We have developed an extensive and detailed ontology in the field of environmental science using this approach in interaction with domain expert. Empirical results show that our approach can support domain expert ontology engineers in building domain specific ontologies efficiently.

[1]  Michael Uschold,et al.  Ontologies: principles, methods and applications , 1996, The Knowledge Engineering Review.

[2]  James A. Hendler,et al.  The Semantic Web" in Scientific American , 2001 .

[3]  Natalya F. Noy,et al.  A Guide to Creating Your First Ontology , 2002 .

[4]  Raphael Volz,et al.  The Ontology Extraction & Maintenance Framework Text-To-Onto , 2001 .

[5]  Brigitte Grau,et al.  SVETLAN - A System to Classify Words in Context , 2000, ECAI Workshop on Ontology Learning.

[6]  N. F. Noy,et al.  Ontology Development 101: A Guide to Creating Your First Ontology , 2001 .

[7]  Daniel Boley,et al.  Principal Direction Divisive Partitioning , 1998, Data Mining and Knowledge Discovery.

[8]  Thomas R. Gruber,et al.  Toward principles for the design of ontologies used for knowledge sharing? , 1995, Int. J. Hum. Comput. Stud..

[9]  Hinrich Schütze,et al.  Information retrieval based on word senses , 1995 .

[10]  David Vogel Using Generic Corpora to Learn Domain-Specific Terminology , 2003 .

[11]  Steffen Staab,et al.  Discovering Conceptual Relations from Text , 2000, ECAI.

[12]  Ralf Steinmetz,et al.  Ontology enrichment with texts from the WWW , 2002 .

[13]  Brigitte Grau,et al.  SVETLAN' a system to classify nouns in context , 2000 .

[14]  Shih-Hung Wu,et al.  SOAT: A Semi-Automatic Domain Ontology Acquisition Tool from Chinese Corpus , 2002, COLING.

[15]  R GruberThomas Toward principles for the design of ontologies used for knowledge sharing , 1995 .

[16]  Steffen Staab,et al.  Ontology Learning for the Semantic Web , 2002, IEEE Intell. Syst..

[17]  Gio Wiederhold,et al.  Ontology Maintenance with an Algebraic Methodology: a Case Study * , 2003 .

[18]  Gilles Bisson,et al.  Designing Clustering Methods for Ontology Building - The Mo'K Workbench , 2000, ECAI Workshop on Ontology Learning.

[19]  Tom M. Mitchell,et al.  Learning to construct knowledge bases from the World Wide Web , 2000, Artif. Intell..

[20]  Charles Nicholas,et al.  Feature Selection and Document Clustering , 2004 .

[21]  Takahira Yamaguchi Acquiring Conceptual Relationships from Domain-Specific Texts , 2001, Workshop on Ontology Learning.

[22]  Claude Roux,et al.  An Ontology Enrichment Method for a Pragmatic Information Extraction System gathering Data on Genetic Interactions , 2000, ECAI Workshop on Ontology Learning.

[23]  Jeremy J. Carroll,et al.  Resource description framework (rdf) concepts and abstract syntax , 2003 .

[24]  Marti A. Hearst Automated Discovery of WordNet Relations , 2004 .