Entropy and information theory
Entropy and information theory
Deriving concept hierarchies from text
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Using LDA to detect semantically incoherent documents
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Journal of Biomedical Informatics
Enabling multi-level relevance feedback on pubmed by integrating rank learning into DBMS
Proceedings of the third international workshop on Data and text mining in bioinformatics
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Exploring PubMed to find relevant information is challenging and time-consuming, as PubMed typically returns a large list of articles as a result of query. Existing works in improving the search quality on PubMed have focused on helping PubMed query formulation, clustering the results, or ranking by relevance. This paper proposes a novel system that dynamically constructs a concept ontology based on the search results, which visualizes related concepts to the query in the form of ontology. The concept ontology can make the PubMed search more effective by detecting related concepts and their relation hidden in the documents. The ontology can broaden the user's knowledge by recommending new concepts unexpected by the user, and also serves to narrow down the search results by recommending additional query terms. The ontology construction is processed in real-time as a result of query, integrated within our PubMed search engine called RefMED. Our system is accesible at "http://dm.hwanjoyu.org/refmed".