BibSonomyburst/user/dbenz/concept_hierarchyBibSonomy RSS feed for /user/dbenz/concept_hierarchy2012-02-17T00:03:27+01:00http://www.bibsonomy.org/bibtex/267a2fdbcec252127fe16337da2dbd104/dbenzdbenz2011-02-08T11:09:09+01:00concept_hierarchy crowdsourcing <span class="authorEditorList"><a href="/author/Eckert">Kai Eckert</a>, <a href="/author/Niepert">Mathias Niepert</a>, <a href="/author/Niemann">Christoph Niemann</a>, <a href="/author/Buckner">Cameron Buckner</a>, <a href="/author/Allen">Colin Allen</a>, and <a href="/author/Stuckenschmidt">Heiner Stuckenschmidt</a> </span><em>Proceedings of the Joint Conference on Digital Libraries JCDL-2010, Brisbane, Australia, </em>(<em>2010</em>)Tue Feb 08 11:09:09 CET 2011{Proceedings of the Joint Conference on Digital Libraries JCDL-2010, Brisbane, Australia}{Crowdsourcing the Assembly of Concept Hierarchies}2010concept_hierarchy crowdsourcing http://www.bibsonomy.org/bibtex/26b560e955077ba6d790082d37059e14d/dbenzdbenz2011-01-28T11:34:14+01:00hierarchical_clustering cluster_partitioning tree_similarity. diploma_thesis concept_hierarchy eventually_useful <span class="authorEditorList"><a href="/author/Yeh">Jian-Hua Yeh</a>, and <a href="/author/hong Sie">Shun hong Sie</a> </span><em>Advances in Applied Artificial Intelligence, </em><em>volume 4031 of Lecture Notes in Computer Science, </em><em>page 982--989. </em><em>Berlin / Heidelberg, </em><em>Springer, </em>(<em>August 2006</em>)Fri Jan 28 11:34:14 CET 2011Berlin / HeidelbergAdvances in Applied Artificial IntelligenceAugust982--989Lecture Notes in Computer ScienceTowards Automatic Concept Hierarchy Generation for Specific Knowledge Network.40312006hierarchical_clustering cluster_partitioning tree_similarity. diploma_thesis concept_hierarchy eventually_useful 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.