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Title |
Contextual Hierarchy-based Incremental Clustering for the Ontological Concept Extraction |
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Author |
Lobna Karoui |
| Citation |
Vol. 7 No. 1 pp. 21-29 |
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Abstract |
Context is an elusive concept that cannot be defined automatically by a machine. Many researches, related to text analyses, word sense disambiguation or ontology learning, have used the context as a sentence, a syntactic structure, a set of sentences or a set of words in order to extract concepts but without tacking into account the structure of the document and the relations between the contexts. In our work, we deal with this issue in order to improve the conceptual quality of the ontological concepts. For this purpose, we define a context that exploits the html structure and the location of words to select the semantically closer cooccurrents for each word and to improve the word weighting. Guided by this context definition, we propose a contextual clustering algorithm that refines the context of each word cluster to obtain semantically extracted concepts. Our results show that our context-based algorithm improves the clusters’ conceptual quality and the relevance of the extracted ontological concepts. |
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Keywords |
Context, Ontological concept, Ontology, clustering |
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