A fuzzy ontological knowledge document clustering methodology

Amy J.C. Trappey*, Charles V. Trappey, Fu Chian Hsu, David W. Hsiao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

54 Scopus citations


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.

Original languageEnglish
Pages (from-to)806-814
Number of pages9
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Issue number3
StatePublished - 27 Mar 2009


  • Fuzzy inference control
  • Hierarchical clustering
  • Ontology schema
  • Patent analysis
  • Text mining

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