An Ontology Learning Method Based on Document Clustering

Abstract:

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Ontology learning is a series method and technology of semi-automatic ontology construction, which uses various data sources to create or expand in-built ontology by semi-automatic method to build a new ontology. Existing ontology construction methods are to collect a large number of conceptual terms based on a large number of field text and background corpus, and then to select field concepts to construct a body. The proposed Cluster-Merge algorithm is to use k-means clustering algorithm in the field document at first, then according to document clustering results to construct body by themself, at last accoring to the ontology similarity for ontology merging to get final output ontology. The experiment may prove that Cluster-Merge algorithm can improve the body resulting recall and precision.

Info:

Periodical:

Edited by:

Dongye Sun, Wen-Pei Sung and Ran Chen

Pages:

1911-1915

DOI:

10.4028/www.scientific.net/AMM.121-126.1911

Citation:

X. M. Wei "An Ontology Learning Method Based on Document Clustering", Applied Mechanics and Materials, Vols. 121-126, pp. 1911-1915, 2012

Online since:

October 2011

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$35.00

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