吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (1): 236-244.doi: 10.13229/j.cnki.jdxbgxb201501035

• Orignal Article • Previous Articles     Next Articles

Test-oriented ontology learning methods

WANG Jun-hua1,2,3,ZUO Wan-li1,2,PENG Tao1,2   

  1. 1.College of Computer Science and Technology, Jilin University, Changchun 130012, China;
    2.Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education,Changchun 130012,China;
    3.College of Computer Science and Engineering,Changchun University of Technology,Changchun 130012,China
  • Received:2013-09-02 Online:2015-02-01 Published:2015-02-01

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Abstract: The techniques of statistics, frequent item mining, pattern matching, heuristic learning and active learning are employed to learn the concepts (including instances), taxonomic relations, semantic relations and the concept properties from the documents based on preprocessing tool Gate and general ontology WordNet. The concept property learning was first proposed in this paper. Experiment results show that the proposed ontology learning method can improve the effect of word semantic disambiguation, enrich phrase concept learning and semantic relationship learning, increase the accuracy of automatic ontology construction and reduce the cost of ontology learning.

Key words: artificial intelligence, ontology learning, active learning, pattern matching, frequent item mining, heuristic learning

CLC Number: 

  • TP18
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