Mining association rules for adaptive search engine based on RDF technology

A method for mining association rules that reflect the behaviors of past users is proposed for an adaptive search engine. The logs of the users' retrieving behaviors are described with the resource description framework model, from which association rules that reflect successful retrieving behaviors are extracted. The extracted rules are used to improve the performance of a metadata-based search engine. The document repository with adaptive hybrid search engine is also developed based on the proposed method. The repository consists of a document registration module, hybrid search engine, and reasoning base. The document registration module is designed to reduce the cost of adding metadata to documents, and the hybrid search engine combines full-text search with metadata-based search engine to improve the recall of retrieval result. The reasoning base is implemented based on the association rule mining method, which contributes to improve both precision and recall of the hybrid search engine. Experiments are performed with a virtual user model, of which results show that appropriate rules can be extracted with the proposed method. The proposed technologies will contribute to realize the concept of humatronics in terms of establishing symmetric relation between humans and systems, as well as sharing information, knowledge, and experiences via computer networks.