Author(s)

Ivo Serra, Rosario Girardi

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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.

Ontology, Ontology Learning, Non-Taxonomic Relationships, Natural Language Processing

Serra, I. and Girardi, R. (2011) A Process for Extracting Non-Taxonomic Relationships of Ontologies from Text. Intelligent Information Management, 3, 119-124. doi: 10.4236/iim.2011.34014.