Ontology Based Clustering for Improving Genomic IR

Recent work has shown that ontology is useful to improve the performance of information retrieval, especially in biomedical literatures. The method of ontology-based can solve synonym problems. In this paper, we propose a new frame for genomic information retrieval based on UMLS. In our frame, genomic information retrieval includes three processes: first, documents were indexed based UMLS, which means documents were represented by concepts, besides, the concept weight was re-calculated combined with similarity between concepts. Second, documents were clustered using fuzzy c-means method. At last cluster language model is utilized for information retrieval. Our method can solve partly synonymy and polysemy problems. The new method is evaluated on TREC 2004/05 genomics track collections. Experiments show that the retrieval performance is greatly improved by the new method compared with the basic language model.