Title
OntoPlus: Text-driven ontology extension using ontology content, structure and co-occurrence information
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.
Year
DOI
Venue
2011
10.1016/j.knosys.2011.06.002
Knowledge-Based Systems
Keywords
Field
DocType
Knowledge engineering methodologies,Ontology extension,Large-scale ontology,Text mining,Semantic technologies
Ontology (information science),Data mining,Ontology-based data integration,Ontology alignment,Process ontology,Information retrieval,Open Biomedical Ontologies,Computer science,Ontology Inference Layer,Suggested Upper Merged Ontology,Upper ontology
Journal
Volume
Issue
ISSN
24
8
0950-7051
Citations 
PageRank 
References 
12
0.60
19
Authors
3
Name
Order
Citations
PageRank
inna novalija1163.08
Dunja Mladenic21484170.14
luka bradesko3224.18