Algorithms for clustering data
Algorithms for clustering data
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ACM Computing Surveys (CSUR)
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Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Issues in stacked generalization
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IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
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CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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This paper discusses the automatic ontology generation process in a digital library. Traditional automatic ontology generation uses hierarchical clustering to group similar terms, and the result hierarchy is usually not satisfactory for human's recognition. Human-provided knowledge network presents strong semantic features, but this generation process is both labor-intensive and inconsistent under large scale scenario. The method proposed in this paper combines the results of specific knowledge network and automatic ontology generation from metadata in a digital library, 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.