PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

OntoPlus : text-driven ontology extension using ontology content, structure and co-occurrence information
Inna Novalija, Dunja Mladenić and Luka Bradeško
Knowledge-Based Systems Volume 24, Number 8, pp. 1261-1267, 2011.

Abstract

This paper addresses the process of semi-automatic text-driven ontology extension using ontology content, structure and co-occurrence information. A novel OntoPlus methodology is proposed for semi-automatic ontology extension based on text mining methods. It allows for the effective extension of the large ontologies, providing a ranked list of potentially relevant concepts and relationships given a new concept (e.g., glossary term) to be inserted in the ontology. A number of experiments are conducted, evaluating measures for ranking correspondence between existing ontology concepts and new domain concepts suggested for the ontology extension. Measures for ranking are based on incorporating ontology content, structure and co-occurrence information. The experiments are performed using a well known Cyc ontology and textual material from two domains – finances and, fisheries & aquaculture. Our experiments show that the best results are achieved by combining content, structure and co-occurrence information. Furthermore, ontology content and structure seem to be more important than co-occurrence for our data in the financial domain. At the same time, ontology content and co-occurrence seem to have higher importance for our fisheries & aquaculture domain.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Natural Language Processing
Information Retrieval & Textual Information Access
ID Code:8709
Deposited By:Jan Rupnik
Deposited On:21 February 2012