Algorithms for clustering data
Algorithms for clustering data
Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Organizing topic-specific web information
HYPERTEXT '00 Proceedings of the eleventh ACM on Hypertext and hypermedia
Demonstration of hierarchical document clustering of digital library retrieval results
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
An Incremental Approach to Building a Cluster Hierarchy
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A scalability analysis of classifiers in text categorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Government ontology and thesaurus construction: a taiwanese experience
ICADL'05 Proceedings of the 8th international conference on Asian Digital Libraries: implementing strategies and sharing experiences
Automatic Ontology Generation Using Schema Information
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Ontology Construction Based on Latent Topic Extraction in a Digital Library
ICADL 08 Proceedings of the 11th International Conference on Asian Digital Libraries: Universal and Ubiquitous Access to Information
Semantic classification of posts in social networks by means of concept hierarchies
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
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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.