Fuzzy ontology and intelligent systems for discovery of useful medical information
Currently, reliable and appropriate medical information is difficult to find on the Internet. The potential for improvement in human health by the use of internet-based sources of information is potentially huge, as knowledge becomes more widely available, at much lower cost. Medical information has traditionally formed a large part of academic publishing. However, the increasing volume of available information, along with the demand for evidence based medicine makes Internet sources of information appear to be the only practical source of comprehensive and up-to date information. The aim of this work is to develop a system allowing groups of users to identify information that they find useful, and using those particular sources as examples develop an intelligent system that can classify new information sources in terms of their likely usefulness to such groups. Medical information sources are particularly interesting because they cover a very wide range of specialties, they require very strict quality control, and the consequence of error may be extremely serious, in addition, medical information sources are of increasing interest to the general public. This work covers the design, construction and testing of such a system and introduces two new concepts - document structure identification via information entropy and fuzzy ontology for knowledge representation. A mapping between query terms and members of ontology is usually a key part of any ontology enhanced searching tool. However many terms used in queries may be overloaded in terms of the ontology, which limits the potential use of automatic query expansion and refinement. In particular this problem affects information systems where different users are likely to expect different meanings for the same term. This thesis describes the derivation and use of a "Fuzzy Ontology" which uses fuzzy relations between components of the ontology in order to preserve a common structure. The concept is presented in the medical domain. Kolmogorov distance calculations are used to identify similarity between documents in terms of authorship, origin and topic. In addition structural measures such as paragraph tags were examined but found not to be effective in clustering documents. The thesis describes some theoretical and practical evaluation of these approaches in the context of a medical information retrieval system, designed to support ontology-based search refinement, relevance feedback and preference sharing between professional groups.