{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:15:02Z","timestamp":1781108102310,"version":"3.54.1"},"reference-count":0,"publisher":"IGI Global Scientific Publishing","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2008,10,1]]},"abstract":"<p>In this article, we present a graph-based knowledge representation for biomedical digital library literature clustering. An efficient clustering method is developed to identify the ontology-enriched k-highest density term subgraphs that capture the core semantic relationship information about each document cluster. The distance between each document and the k term graph clusters is calculated. A document is then assigned to the closest term cluster. The extensive experimental results on two PubMed document sets (Disease10 and OHSUMED23) show that our approach is comparable to spherical k-means. The contributions of our approach are the following: (1) we provide two corpus-level graph representations to improve document clustering, a term co-occurrence graph and an abstract-title graph; (2) we develop an efficient and effective document clustering algorithm by identifying k distinguishable class-specific core term subgraphs using terms\u2019 global and local importance information; and (3) the identified term clusters give a meaningful explanation for the document clustering results.<\/p>","DOI":"10.4018\/jdwm.2008100105","type":"journal-article","created":{"date-parts":[[2011,2,15]],"date-time":"2011-02-15T13:41:26Z","timestamp":1297777286000},"page":"84-101","source":"Crossref","is-referenced-by-count":10,"title":["A Graph-Based Biomedical Literature Clustering Approach Utilizing Term's Global and Local Importance Information"],"prefix":"10.4018","volume":"4","author":[{"given":"Xiaodan","family":"Zhang","sequence":"first","affiliation":[{"name":"Drexel University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohua","family":"Hu","sequence":"additional","affiliation":[{"name":"Drexel University, USA and Jiangxi University of Finance and Economics, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiali","family":"Xia","sequence":"additional","affiliation":[{"name":"Jiangxi University of Finance and Economics, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohua","family":"Zhou","sequence":"additional","affiliation":[{"name":"Drexel University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Palakorn","family":"Achananuparp","sequence":"additional","affiliation":[{"name":"Drexel University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","container-title":["International Journal of Data Warehousing and Mining"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=1819","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T17:30:56Z","timestamp":1654104656000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/jdwm.2008100105"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2008,10,1]]},"references-count":0,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2008,10]]}},"URL":"https:\/\/doi.org\/10.4018\/jdwm.2008100105","relation":{},"ISSN":["1548-3924","1548-3932"],"issn-type":[{"value":"1548-3924","type":"print"},{"value":"1548-3932","type":"electronic"}],"subject":[],"published":{"date-parts":[[2008,10,1]]}}}