Open AccessA Process for Extracting Non-Taxonomic Relationships of Ontologies from Text
Author(s) -
Ivo Serra,
Rosario Girardi
Publication year - 2011
Publication title -
intelligent information management
Language(s) - English
Resource type - Journals
eISSN - 2160-5920
pISSN - 2160-5912
DOI - 10.4236/iim.2011.34014
Subject(s) - computer science , ontology , task (project management) , information retrieval , ontology learning , ontology alignment , process (computing) , natural language processing , field (mathematics) , hierarchy , precision and recall , frame (networking) , domain (mathematical analysis) , process ontology , ontology components , information extraction , artificial intelligence , suggested upper merged ontology , semantic web , mathematics , market economy , telecommunications , philosophy , mathematical analysis , management , epistemology , pure mathematics , economics , operating system
Manual construction of ontologies by domain experts and knowledge engineers is an expensive and time consuming task so, automatic and/or semiautomatic approaches are needed. Ontology learning looks for identifying ontology elements like non-taxonomic relationships from information sources. These relationships correspond to slots in a frame-based ontology. This article proposes an initial process for semiautomatic extraction of non-taxonomic relationships of ontologies from textual sources. It uses Natural Language Processing (NLP) techniques to identify good candidates of non-taxonomic relationships and a data mining technique to suggest their possible best level in the ontology hierarchy. Once the extraction of these relationships is essentially a retrieval task, the metrics of this field like recall, precision and f-measure are used to perform evaluation
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