The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Document clustering using word clusters via the information bottleneck method
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Partially Supervised Classification of Text Documents
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
PEBL: positive example based learning for Web page classification using SVM
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
SOPHIA: an interactive cluster-based retrieval system for the OHSUMED collection
IEEE Transactions on Information Technology in Biomedicine
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Efficiently finding the most relevant publications in large corpus is an important research topic in information retrieval. The number of biological literatures grows exponentially in various publication databases. The objective of this paper is to quickly identify useful publications from a large number of biological documents. In this paper, we introduce a new iterative search paradigm that integrates biomedical background knowledge in organizing the results returned by search engines and utilizes user feedbacks in pruning irrelevant documents by document classification. A new term weighting strategy based on Gene Ontology is proposed to represent biomedical literatures. A prototype text retrieval system is built on this iterative search approach. Experimental results on MEDLINE abstracts and different keyword inputs show that the system can filter a large number of irrelevant documents in a reasonable time while keeping most of the useful documents. The results also show that the system is robust against different inputs and parameter settings.