{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T09:50:43Z","timestamp":1763459443830,"version":"3.45.0"},"reference-count":27,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2012,9,1]],"date-time":"2012-09-01T00:00:00Z","timestamp":1346457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000145","name":"Division of Information and Intelligent Systems","doi-asserted-by":"publisher","award":["IIS-1018865"],"award-info":[{"award-number":["IIS-1018865"]}],"id":[{"id":"10.13039\/100000145","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2012,9]]},"abstract":"<jats:p>\n                    Wikipedia is the largest user-generated knowledge base. We propose a structured query mechanism,\n                    <jats:italic toggle=\"yes\">entity-relationship query<\/jats:italic>\n                    , for searching entities in the Wikipedia corpus by their properties and interrelationships. An entity-relationship query consists of multiple predicates on desired entities. The semantics of each predicate is specified with keywords. Entity-relationship query searches entities directly over text instead of preextracted structured data stores. This characteristic brings two benefits: (1) Query semantics can be intuitively expressed by keywords; (2) It only requires rudimentary entity annotation, which is simpler than explicitly extracting and reasoning about complex semantic information before query-time. We present a ranking framework for general entity-relationship queries and a position-based Bounded Cumulative Model (BCM) for accurate ranking of query answers. We also explore various weighting schemes for further improving the accuracy of BCM. We test our ideas on a 2008 version of Wikipedia using a collection of 45 queries pooled from INEX entity ranking track and our own crafted queries. Experiments show that the ranking and weighting schemes are both effective, particularly on multipredicate queries.\n                  <\/jats:p>","DOI":"10.1145\/2337542.2337555","type":"journal-article","created":{"date-parts":[[2012,10,12]],"date-time":"2012-10-12T16:56:02Z","timestamp":1350060962000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Entity-Relationship Queries over Wikipedia"],"prefix":"10.1145","volume":"3","author":[{"given":"Xiaonan","family":"Li","sequence":"first","affiliation":[{"name":"University of Texas at Arlington"}]},{"given":"Chengkai","family":"Li","sequence":"additional","affiliation":[{"name":"University of Texas at Arlington"}]},{"given":"Cong","family":"Yu","sequence":"additional","affiliation":[{"name":"Google Research"}]}],"member":"320","published-online":{"date-parts":[[2012,9]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_2_1_1_1","DOI":"10.1145\/336597.336644"},{"doi-asserted-by":"publisher","key":"e_1_2_1_2_1","DOI":"10.5555\/1785162.1785216"},{"doi-asserted-by":"publisher","key":"e_1_2_1_3_1","DOI":"10.5555\/646543.696220"},{"volume-title":"Proceedings of the Conference on Innovative Data Systems Research. 68--74","author":"Cafarella M. 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Automated construction and growth of a large ontology. Ph.D. thesis Saarland University.","key":"e_1_2_1_22_1"},{"doi-asserted-by":"publisher","key":"e_1_2_1_23_1","DOI":"10.1145\/1242572.1242667"},{"doi-asserted-by":"publisher","key":"e_1_2_1_24_1","DOI":"10.1145\/1363686.1363943"},{"key":"e_1_2_1_25_1","volume-title":"Proceedings of thee 12th Text Retrieval Conference. 54--68","author":"Voorhees E. M.","year":"2003","unstructured":"Voorhees, E. M. 2003. Overview of the trec 2003 question answering track. 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