{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:51:43Z","timestamp":1750308703587,"version":"3.41.0"},"reference-count":31,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2012,12,10]],"date-time":"2012-12-10T00:00:00Z","timestamp":1355097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGKDD Explor. Newsl."],"published-print":{"date-parts":[[2012,12,10]]},"abstract":"<jats:p>This paper defines the cross domain similarity mining (CDSM) problem, and motivates CDSM with several potential applications. CDSM has big potential in (1) supporting understanding transfer and (2) supporting research by analogy, since similarity is vital to understanding\/meaning and to identifying analogy, and since analogy is a fundamental approach frequently used in hypothesis generation and in research. CDSM also has big potential in (3) advancing learning transfer since cross domain similarities can shed light on how to best adapt classifiers\/clusterings across given domains and how to avoid negative transfer. CDSM can also be useful for (4) solving the schema\/ontology matching problem. Moreover, this paper gives a list of potential research questions for CDSM, and compares CDSM with related studies. One purpose of this paper is to introduce the CDSM problem to the wide KDD community in order to quickly realize the full potential of CDSM.<\/jats:p>","DOI":"10.1145\/2408736.2408744","type":"journal-article","created":{"date-parts":[[2012,12,11]],"date-time":"2012-12-11T13:13:42Z","timestamp":1355231622000},"page":"43-47","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Cross domain similarity mining"],"prefix":"10.1145","volume":"14","author":[{"given":"Guozhu","family":"Dong","sequence":"first","affiliation":[{"name":"Wright State University, Dayton, OH, USA"}]}],"member":"320","published-online":{"date-parts":[[2012,12,10]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/312129.312263"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/645503.656271"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972788.22"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1080\/15248371003700015"},{"key":"e_1_2_1_5_1","first-page":"23","volume-title":"ACM International Conference on Knowledge Discovery and Data Mining (KDD)","author":"Das G.","year":"1998"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553402"},{"key":"e_1_2_1_7_1","first-page":"5","article-title":"Learning from abroad: the role of policy transfer in contemporary policy making","volume":"13","author":"Dolowitz D. 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In J. D. P. Rolim editor Parallel and Distributed Processing (IPDPS) Workshops volume \n  1800\n   of \n  Lecture Notes in Computer Science pages \n  399\n  --\n  406\n  . \n  Springer 2000\n  .   S. Parthasarathy and M. Ogihara. Exploiting dataset similarity for distributed mining. In J. D. P. Rolim editor Parallel and Distributed Processing (IPDPS) Workshops volume 1800 of Lecture Notes in Computer Science pages 399--406. Springer 2000.","DOI":"10.1007\/3-540-45591-4_52"},{"volume-title":"International Encyclopedia of Education","year":"1992","author":"Perkins D. N.","key":"e_1_2_1_25_1"},{"volume-title":"Cognition and Instruction","year":"1994","author":"Pirolli P.","key":"e_1_2_1_26_1"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/s007780100057"},{"key":"e_1_2_1_28_1","doi-asserted-by":"crossref","unstructured":"P. Shvaiko and J. Euzenat. A survey of schema-based matching approaches. pages 146--171 2005.   P. Shvaiko and J. Euzenat. 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