Algorithms for clustering data
Algorithms for clustering data
ACM Computing Surveys (CSUR)
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Demonstration of hierarchical document clustering of digital library retrieval results
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
Modern Information Retrieval
The Knowledge Model of Protégé-2000: Combining Interoperability and Flexibility
EKAW '00 Proceedings of the 12th European Workshop on Knowledge Acquisition, Modeling and Management
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
On an equivalence between PLSI and LDA
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
A new statistical parser based on bigram lexical dependencies
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Towards automatic concept hierarchy generation for specific knowledge network
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
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
Ontology learning from text: A look back and into the future
ACM Computing Surveys (CSUR)
A PAM-based ontology concept and hierarchy learning method
Journal of Information Science
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This paper discusses the automatic ontology construction process in a digital library. Traditional automatic ontology construction 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 statistical correction and latent topic extraction of textual data in a digital library, which produces a semantic-oriented and OWL-based ontology. The experimental document collection used here is the Chinese Recorder, which served as a link between the various missions that were part of the rise and heyday of the Western effort to Christianize the Far East. The ontology construction process is described and a final ontology in OWL format is shown in our result.