Elicitation synergy of extracting conceptual tags and hierarchies in textual document |
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| Affiliation: | 1. Department of Oncology, Fuzhou General Hospital of Nanjing Military Command, Fuzong Clinical College of Fujian Medical University, Fuzhou, Fujian, China;2. Department of Oncology, Fujian Provincial Cancer Hospital, Jinan District, Fuzhou, Fujian, China;3. Department of Pathology, Fuzhou General Hospital of Nanjing Military Command, Fuzhou, Fujian, China;4. Department of Epidemiology and Health Statistics, Fujian Medical University School of Public Health, Fuzhou, Fujian, China;5. Department of Oncology, 180th Hospital of People''s Liberation Army, Quanzhou, Fujian, China;6. Division of Hematology and Oncology, Department of Medicine, Virginia Commonwealth University, Massey Cancer Center, Richmond, VA;1. Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, 43, Sec. 4, Keelung Rd., Taipei 106, Taiwan;2. Faculty of Architecture, Building and Planning, University of Melbourne, 1-100 Grattan Street, Parkville, Victoria 3010, Australia;1. IAE, Université de Rouen, 3, Avenue Pasteur, F-76186 Rouen Cedex, France;2. University of Piraeus, 80, Karaoli & Dimitriou Street, GR-18534 Piraeus, Greece |
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| Abstract: | This study develops an ontology building process for extracting conceptual tags and hierarchies in textual corpus. Though humans have been creating ontologies for many years, efficient ontology building processes in textual corpus are extremely ad hoc. Several issues have identified including how to recognize terminology in textual document, name concept tags in terminologies, and derive conceptual hierarchies among concepts. The proposed approach is extraction technique combinations to produce ontology prototype for editors. The empirical feedback indicates that elicitation synergy is productive during the early stages of building. Additionally, this elicitation synergy is especially useful for ontology editors who lack reference models of a working domain and who encounter textual corpus as major knowledge sources. |
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