A Biological Text Retrieval System Based on Background Knowledge and User Feedback

  • Meng Hu
  • Jiong Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4316)


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.


Gene Ontology Feature Level Prototype System User Feedback Document Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Meng Hu
    • 1
  • Jiong Yang
    • 1
  1. 1.EECSCase Western Reserve UniversityCleveland

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