Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Probabilistic techniques for phrase extraction
Information Processing and Management: an International Journal
Information Retrieval
A feature mining based approach for the classification of text documents into disjoint classes
Information Processing and Management: an International Journal
Application of fuzzy logic to approximate reasoning using linguistic synthesis
MVL '76 Proceedings of the sixth international symposium on Multiple-valued logic
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Minimum Entropy Clustering and Applications to Gene Expression Analysis
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
A fuzzy linguistic ontology payoff method for aerospace real options valuation
Expert Systems with Applications: An International Journal
A hybrid approach using pso and K-means for semantic clustering of web documents
Journal of Web Engineering
Cross-language patent matching via an international patent classification-based concept bridge
Journal of Information Science
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This correspondence presents a novel hierarchical clustering approach for knowledge document self-organization, particularly for patent analysis. Current keyword-based methodologies for document content management tend to be inconsistent and ineffective when partial meanings of the technical content are used for cluster analysis. Thus, a new methodology to automatically interpret and cluster knowledge documents using an ontology schema is presented. Moreover, a fuzzy logic control approach is used to match suitable document cluster(s) for given patents based on their derived ontological semantic webs. Finally, three case studies are used to test the approach. The first test case analyzed and clustered 100 patents for chemical and mechanical polishing retrieved from the World Intellectual Property Organization (WIPO). The second test case analyzed and clustered 100 patent news articles retrieved from online Web sites. The third case analyzed and clustered 100 patents for radio-frequency identification retrieved from WIPO. The results show that the fuzzy ontology-based document clustering approach outperforms the K-means approach in precision, recall, F-measure, and Shannon's entropy.