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Impressum Keywords Document clustering, text summarization, ontology, scalefree network, MEDLINE. Send us your feedback Part of Springer ScienceBusiness Media Privacy, Disclaimer, Terms and Conditions, Copyright Information Privacy Policy Remote User AgentMozilla4. 0 Windows MSIE 7. 0 Windows NT 5. 1 SV1 CLR 2. 0.
Keywords Document clustering, text summarization, ontology, scalefree network, MEDLINE.
Content Types All Publications Journals Book Series Books Reference Works Protocols Subject Collections Architecture and Design Behavioral Science Biomedical and Life Sciences Business and Economics Chemistry and Materials Science Computer Science Earth and Environmental Science Engineering Humanities, Social Sciences and Law Mathematics and Statistics Medicine Physics and Astronomy Professional and Applied Computing Book Chapter Coherent Biomedical Literature Clustering and Summarization Approach Through OntologyEnriched Graphical Representations IllhoiYoo1, XiaohuaHu2 and IlYeolSong2 1 Department of Health Management and Informatics, School of Medicine, University of MissouriColumbia, Columbia, MO, 65211, USA 2 College of Information Science and Technology, Drexel University, Philadelphia, PA, 19104,
Keywords Document clustering, text summarization, ontology, scalefree network, MEDLINE. These document cluster models as semantic chunks capturing the core semantic relationships in the ontologyenriched scalefree graphical representation of documents. IllhoiYooEmail MU. Prof. Yoogmail. com XiaohuaHuEmail thucis. drexel. edu IlYeolSongEmail songdrexel. edu Fulltext Preview Small, Large, Larger, Largest more options Find Query Builder Close General information on journals and books Text PDF 575.
Our extensive experimental results indicate our approach shows 45 cluster quality improvement and 72 clustering reliability improvement, in terms of misclassification index, over Bisecting Kmeans as leading document clustering approach. Our approach significantly improves the quality of document clusters and understandability of documents through summaries. IllhoiYooEmail MU. Prof. Yoogmail. com XiaohuaHuEmail thucis. drexel. edu IlYeolSongEmail songdrexel. edu Fulltext Preview Small, Large, Larger, Largest. The key of the approach is to construct document cluster models as semantic chunks capturing the core semantic relationships in the ontologyenriched scalefree graphical representation of documents.
The primary contribution of this paper is we introduce coherent biomedical literature clustering and summarization approach that takes advantage of ontologyenriched graphical representations. Text Frequently asked questions Clear Title ti Summary su Author au ISSN issn ISBN isbn DOI doi And Or Not wildcard exact Within all contentWithin this book seriesWithin this book Export this chapter as RIS These document cluster models are used for both document clustering and text summarization by constructing Text Semantic Interaction Network TSIN.
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Language of Publication English. Papers describing novel applications of proteomics are especially welcome.
