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Database Syst."],"published-print":{"date-parts":[[2024,3,31]]},"abstract":"<jats:p>\n            This article proposes a notion of parametric simulation to link entities across a relational database \ud835\udc9f and a graph\n            <jats:italic>G<\/jats:italic>\n            . Taking functions and thresholds for measuring vertex closeness, path associations, and important properties as parameters, parametric simulation identifies tuples\n            <jats:italic>t<\/jats:italic>\n            in \ud835\udc9f and vertices\n            <jats:italic>v<\/jats:italic>\n            in\n            <jats:italic>G<\/jats:italic>\n            that refer to the same real-world entity, based on both topological and semantic matching. We develop machine learning methods to learn the parameter functions and thresholds. We show that parametric simulation is in quadratic-time by providing such an algorithm. Moreover, we develop an incremental algorithm for parametric simulation; we show that the incremental algorithm is bounded relative to its batch counterpart, i.e., it incurs the minimum cost for incrementalizing the batch algorithm. Putting these together, we develop\n            <jats:sans-serif>HER<\/jats:sans-serif>\n            , a parallel system to check whether (\n            <jats:italic>t, v<\/jats:italic>\n            ) makes a match, find all vertex matches of\n            <jats:italic>t<\/jats:italic>\n            in\n            <jats:italic>G<\/jats:italic>\n            , and compute all matches across \ud835\udc9f and\n            <jats:italic>G<\/jats:italic>\n            , all in quadratic-time; moreover,\n            <jats:sans-serif>HER<\/jats:sans-serif>\n            supports incremental computation of these in response to updates to \ud835\udc9f and\n            <jats:italic>G<\/jats:italic>\n            . Using real-life and synthetic data, we empirically verify that\n            <jats:sans-serif>HER<\/jats:sans-serif>\n            is accurate with F-measure of 0.94 on average, and is able to scale with database \ud835\udc9f and graph\n            <jats:italic>G<\/jats:italic>\n            for both batch and incremental computations.\n          <\/jats:p>","DOI":"10.1145\/3639363","type":"journal-article","created":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T21:34:09Z","timestamp":1704317649000},"page":"1-50","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Linking Entities across Relations and Graphs"],"prefix":"10.1145","volume":"49","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5149-2656","authenticated-orcid":false,"given":"Wenfei","family":"Fan","sequence":"first","affiliation":[{"name":"Shenzhen Institute of Computing Sciences, China, University of Edinburgh, United Kingdom, and Beihang University, Edinburgh, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4919-8989","authenticated-orcid":false,"given":"Ping","family":"Lu","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4086-1421","authenticated-orcid":false,"given":"Kehan","family":"Pang","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2763-0577","authenticated-orcid":false,"given":"Ruochun","family":"Jin","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5641-2452","authenticated-orcid":false,"given":"Wenyuan","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,2,28]]},"reference":[{"key":"e_1_3_3_2_2","unstructured":"SemTab Challenge. 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