Algorithms for clustering data
Algorithms for clustering data
Proximal nodes: a model to query document databases by content and structure
ACM Transactions on Information Systems (TOIS)
A Multilevel Text Processing Model of Newsgroup Dynamics
NLDB '02 Proceedings of the 6th International Conference on Applications of Natural Language to Information Systems-Revised Papers
Personalized Courseware Construction Based on Web Data Mining
WISE '00 Proceedings of the First International Conference on Web Information Systems Engineering (WISE'00)-Volume 2 - Volume 2
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Querying a database for document retrieval is often a process close to querying an answering expert system. In this work, we apply the knowledge discovery techniques to build an information retrieval system by regarding the structural document database as the expertise of the knowledge discovery. In order to elicit the knowledge embedded in the document structure, a new knowledge representation, named StructuralDocuments(SD), is defined and a transformation process which can transform the documents into a set of SDs is proposed. To evaluate the performance of our idea, we developed an intelligent information retrieval system which can help users to retrieve the required personnel regulations in Taiwan. In our experiments, it can be easily seen that the retrieval results using SD are better than traditional approaches.