An Ontology Learning Method Based on Document Clustering

Xian Min Wei

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

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 121-126An Ontology Learning Method Based on Document...

An Ontology Learning Method Based on Document Clustering

Abstract:

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.

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Periodical:

Applied Mechanics and Materials (Volumes 121-126)

Pages:

1911-1915

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.121-126.1911

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Online since:

October 2011

Authors:

Xian Min Wei

Keywords:

Document Clustering, K-Means Clustering Algorithm, Ontology Learning, Ontology Merging, Similarity

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