Ontologies represent a method of formally expressing a shared understanding of information, and have been seen by many authors as a prerequisite for the 'Semantic web'. A mapping between query terms and members of an ontology is usually a key part of any ontology enhanced searching tool. However the relative importance of a particular mapping to an overloaded term may be different for different users, and this information is vital for accurate satisfaction of a query. One way of overcoming this problem is the postulation of a 'fuzzy ontology'. By adding a value for degree of membership to each term that is 'overloaded', for each user or group of users then the recovered documents from ontology mediated search can reflect the likely information need. The author will discuss means of ontology fuzzification, by both analysis of a corpus of documents and the use of a relevance feedback mechanism and some possible extensions to this scheme.
|Cite as: Parry, D. (2004). A Fuzzy Ontology for Medical Document Retrieval. In Proc. Australasian Workshop on Data Mining and Web Intelligence (DMWI2004), Dunedin, New Zealand. CRPIT, 32. Purvis, M., Ed. ACS. 121-126. |
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