http://www.bibsonomy.org/burst/user/dbenz_test/concept_hierarchyBibSonomy BuRST Feed for /user/dbenz_test/concept_hierarchy2008-08-21T13:15:59+02:00http://www.bibsonomy.org/bibtex/26b560e955077ba6d790082d37059e14d/dbenz_testdbenz_test2008-02-19T09:25:00+01:00tree_similarity. cluster_partitioning hierarchical_clustering diploma_thesis eventually_useful concept_hierarchy <span style="color:#555555;">Jian-Hua <a href="http://www.bibsonomy.org/author/Yeh">Yeh</a> and Shun hong <a href="http://www.bibsonomy.org/author/Sie">Sie</a> </span><em>Advances in Applied Artificial Intelligence, </em><em>volume4031ofLecture Notes in Computer Science, </em><em>page982--989. </em><em>Berlin / Heidelberg, </em><em>Springer, </em><em>August2006. </em>Tue Feb 19 09:25:00 CET 2008Berlin / HeidelbergAdvances in Applied Artificial IntelligenceAugust982--989Lecture Notes in Computer ScienceTowards Automatic Concept Hierarchy Generation for Specific Knowledge Network.40312006tree_similarity. cluster_partitioning hierarchical_clustering diploma_thesis eventually_useful concept_hierarchy This paper discusses the automatic concept hierarchy generation process for specific knowledge network. Traditional concept hierarchy generation uses hierarchical clustering to group similar terms, and the result hierarchy is usually not satisfactory for human being recognition. Human-provided knowledge network presents strong semantic features, but this generation process is both labor-intensive and inconsistent under large scale hierarchy. The method proposed in this paper combines the results of specific knowledge network and automatic concept hierarchy generation, which produces a human-readable, semantic-oriented hierarchy. This generation process can efficiently reduce manual classification efforts, which is an exhausting task for human beings. An evaluation method is also proposed in this paper to verify the quality of the result hierarchy.