{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T07:09:31Z","timestamp":1781075371547,"version":"3.54.1"},"reference-count":32,"publisher":"Emerald","issue":"5","license":[{"start":{"date-parts":[[2010,10,5]],"date-time":"2010-10-05T00:00:00Z","timestamp":1286236800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2010,10,5]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-heading\">Purpose<\/jats:title><jats:p>The co\u2010word analysis method is commonly used to cluster\u2010related keywords into the same keyword domain. In other words, traditional co\u2010word analysis cannot cluster the same keywords into more than one keyword domain, and disregards the multi\u2010domain property of keywords. The purpose of this paper is to propose an innovative keyword co\u2010citation approach called \u201cComplete Keyword Pair (CKP) method\u201d, which groups complete keyword sets of reference papers into clusters, and thus finds keywords belonging to more than one keyword domain, namely bridge\u2010keywords.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title><jats:p>The approach regards complete author keywords of a paper as a complete keyword set to compute the relations among keywords. Any two complete keyword sets whose corresponding papers are co\u2010referenced by the same paper are recorded as a CKP. A clustering method is performed with the correlation matrix computed from the frequency counts of the CKPs, for clustering the complete keyword sets. Since keywords may be involved in more than one complete keyword set, the same keywords may end up appearing in different clusters.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>Results of this study show that the CKP method can discover bridge\u2010keywords with average precision of 80 per cent in the <jats:italic>Journal of the Association for Computing Machinery<\/jats:italic> citation bank during 2000\u20102006 when compared against the benchmark of Association for Computing Machinery Computing Classification System.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>Traditional co\u2010word analysis focuses on co\u2010occurrence of keywords, and therefore, cannot cluster the same keywords into more than one keyword domain. The CKP approach considers complete author keyword sets of reference papers to discover bridge\u2010keywords. Therefore, the keyword recommendation system based on CKP can recommend keywords across multiple keyword domains via the bridge\u2010keywords.<\/jats:p><\/jats:sec>","DOI":"10.1108\/02640471011081951","type":"journal-article","created":{"date-parts":[[2010,10,30]],"date-time":"2010-10-30T07:01:05Z","timestamp":1288422065000},"page":"669-687","source":"Crossref","is-referenced-by-count":6,"title":["An approach to discover and recommend cross\u2010domain bridge\u2010keywords in document banks"],"prefix":"10.1108","volume":"28","author":[{"given":"Yu\u2010Min","family":"Su","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping\u2010Yu","family":"Hsu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning\u2010Yao","family":"Pai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","reference":[{"key":"key2022021720470900100_b1","unstructured":"Association for Computing Machinery (2009a), \u201cACM computing classification system toc\u201d, ACM, available at: www.acm.org\/about\/class."},{"key":"key2022021720470900100_b2","unstructured":"Association for Computing Machinery (2009b), \u201cThe ACM portal\u201d, ACM, available at: http:\/\/portal.acm.org."},{"key":"key2022021720470900100_b6","doi-asserted-by":"crossref","unstructured":"Chen, H. and Lynch, K.J. 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