{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:34:36Z","timestamp":1781109276489,"version":"3.54.1"},"reference-count":32,"publisher":"IGI Global Scientific Publishing","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>This article describes how semantic web data sources follow linked data principles to facilitate efficient information retrieval and knowledge sharing. These data sources may provide complementary, overlapping or contradicting information. In order to integrate these data sources, the authors perform entity linking. Entity linking is an important task of identifying and linking entities across data sources that refer to the same real-world entities. In this work, they have proposed a genetic fuzzy approach to learn linkage rules for entity linking. This method is domain independent, automatic and scalable. Their approach uses fuzzy logic to adapt mutation and crossover rates of genetic programming to ensure guided convergence. The authors' experimental evaluation demonstrates that our approach is competitive and make significant improvements over state of the art methods.<\/jats:p>","DOI":"10.4018\/ijswis.2018070107","type":"journal-article","created":{"date-parts":[[2018,5,15]],"date-time":"2018-05-15T08:38:21Z","timestamp":1526373501000},"page":"134-166","source":"Crossref","is-referenced-by-count":1,"title":["Genetic-Fuzzy Programming Based Linkage Rule Miner (GFPLR-Miner) for Entity Linking in Semantic Web"],"prefix":"10.4018","volume":"14","author":[{"given":"Amit","family":"Singh","sequence":"first","affiliation":[{"name":"Jawaharlal Nehru University, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aditi","family":"Sharan","sequence":"additional","affiliation":[{"name":"SC & SS: School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJSWIS.2018070107-0","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2014.2365779"},{"key":"IJSWIS.2018070107-1","unstructured":"Bergman, A. (1993). Adjusting parameters of genetic algorithms by fuzzy control rules. New Computing Techniques in Physics Reserach III. Retrieved from http:\/\/ci.nii.ac.jp\/naid\/10011278447\/"},{"key":"IJSWIS.2018070107-2","doi-asserted-by":"publisher","DOI":"10.1145\/956750.956759"},{"key":"IJSWIS.2018070107-3","doi-asserted-by":"publisher","DOI":"10.4018\/jswis.2009081901"},{"key":"IJSWIS.2018070107-4","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1402020"},{"key":"IJSWIS.2018070107-5","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"IJSWIS.2018070107-6","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2010.234"},{"key":"IJSWIS.2018070107-7","doi-asserted-by":"crossref","unstructured":"Demartini, G., & Difallah, D. (2012). ZenCrowd: leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking. In Proceedings of the 21st international conference on World Wide Web (pp. 469\u2013478). ACM Press. Retrieved from http:\/\/dl.acm.org\/citation.cfm?id=2187900","DOI":"10.1145\/2187836.2187900"},{"key":"IJSWIS.2018070107-8","doi-asserted-by":"crossref","unstructured":"Elfeky, M., & Verykios, V. (2002). TAILOR: A record linkage toolbox. In 18th International Conference on Data Engineering, 2002. Retrieved from http:\/\/ieeexplore.ieee.org\/abstract\/document\/994694\/","DOI":"10.1109\/ICDE.2002.994694"},{"key":"IJSWIS.2018070107-9","doi-asserted-by":"publisher","DOI":"10.14778\/2876473.2876474"},{"key":"IJSWIS.2018070107-10","doi-asserted-by":"publisher","DOI":"10.1109\/6.819926"},{"key":"IJSWIS.2018070107-11","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-002-0238-y"},{"key":"IJSWIS.2018070107-12","doi-asserted-by":"crossref","unstructured":"Holland, J. H. (John H., & H., J. (1992). Adaptation in natural and artificial systems\u202f: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press. Retrieved from http:\/\/dl.acm.org\/citation.cfm?id=531075","DOI":"10.7551\/mitpress\/1090.001.0001"},{"key":"IJSWIS.2018070107-13","doi-asserted-by":"publisher","DOI":"10.1145\/1963405.1963421"},{"key":"IJSWIS.2018070107-14","unstructured":"Isele, R., & Bizer, C. (2011). Learning linkage rules using genetic programming. In Proceedings of the 6th International Conference on Ontology Matching (pp. 13\u201324). Retrieved from http:\/\/dl.acm.org\/citation.cfm?id=2887543"},{"key":"IJSWIS.2018070107-15","doi-asserted-by":"publisher","DOI":"10.1016\/j.websem.2013.06.001"},{"key":"IJSWIS.2018070107-16","doi-asserted-by":"crossref","unstructured":"Kejriwal, M., & Miranker, D. D. P. (2015). Semi-supervised Instance Matching Using Boosted Classifiers. In European Semantic Web Conference (pp. 388\u2013402). Retrieved from http:\/\/link.springer.com\/chapter\/10.1007\/978-3-319-18818-8_24","DOI":"10.1007\/978-3-319-18818-8_24"},{"key":"IJSWIS.2018070107-17","doi-asserted-by":"crossref","unstructured":"Koza, J., & Poli, R. (2005). Genetic programming. Search Methodologies. Retrieved from http:\/\/link.springer.com\/chapter\/10.1007\/0-387-28356-0_5","DOI":"10.1007\/0-387-28356-0_5"},{"key":"IJSWIS.2018070107-18","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2008.202"},{"key":"IJSWIS.2018070107-19","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45712-7_40"},{"key":"IJSWIS.2018070107-20","doi-asserted-by":"publisher","DOI":"10.1162\/evco.2006.14.3.309"},{"key":"IJSWIS.2018070107-21","unstructured":"Ngomo, A., Lehmann, J., & Auer, S. (2011). Raven-active learning of link specifications. In Proceedings of the 6th International Conference on Semantic Web (pp. 25\u201336). Retrieved from http:\/\/dl.acm.org\/citation.cfm?id=2887544"},{"key":"IJSWIS.2018070107-22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-30284-8_17"},{"key":"IJSWIS.2018070107-23","unstructured":"Ngonga Ngomo, A.-C., Auer, S., Ngomo, A., & Auer, S. (2011). Limes-a time-efficient approach for large-scale link discovery on the web of data. In Proceedings of the Twenty-Second international joint conference on Artificial Intelligence (pp. 2312\u20132317). Retrieved from https:\/\/www.researchgate.net\/profile\/Soeren_Auer\/publication\/220812050_LIMES__A_Time-Efficient_Approach_for_Large-Scale_Link_Discovery_on_the_Web_of_Data\/links\/0deec51c6de460066d000000.pdf"},{"key":"IJSWIS.2018070107-24","doi-asserted-by":"publisher","DOI":"10.1587\/transinf.2015EDP7392"},{"key":"IJSWIS.2018070107-25","doi-asserted-by":"crossref","unstructured":"Nguyen, K., & Ichise, R. (2016b). ScLink: supervised instance matching system for heterogeneous repositories. Journal of Intelligent Information Systems. Retrieved from http:\/\/link.springer.com\/article\/10.1007\/s10844-016-0426-3","DOI":"10.1007\/s10844-016-0426-3"},{"key":"IJSWIS.2018070107-26","unstructured":"Niu, X., Rong, S., Zhang, Y., & Wang, H. (2011). Zhishi. links results for OAEI 2011. In CEUR Workshop Proceedings. Retrieved from http:\/\/dl.acm.org\/citation.cfm?id=2887565"},{"key":"IJSWIS.2018070107-27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-00685-2_3"},{"key":"IJSWIS.2018070107-28","doi-asserted-by":"crossref","unstructured":"Schmachtenberg, M., Bizer, C., & Paulheim, H. (2014). Adoption of the linked data best practices in different topical domains. In International Semantic Web Conference (pp. 245\u2013260). Retrieved from http:\/\/link.springer.com\/chapter\/10.1007\/978-3-319-11964-9_16","DOI":"10.1007\/978-3-319-11964-9_16"},{"key":"IJSWIS.2018070107-29","doi-asserted-by":"publisher","DOI":"10.1016\/S0306-4379(01)00042-4"},{"key":"IJSWIS.2018070107-30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-04930-9_41"},{"key":"IJSWIS.2018070107-31","doi-asserted-by":"publisher","DOI":"10.1016\/S0019-9958(65)90241-X"}],"container-title":["International Journal on Semantic Web and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=206257","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T04:01:31Z","timestamp":1651809691000},"score":1,"resource":{"primary":{"URL":"http:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJSWIS.2018070107"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":32,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.4018\/ijswis.2018070107","relation":{},"ISSN":["1552-6283","1552-6291"],"issn-type":[{"value":"1552-6283","type":"print"},{"value":"1552-6291","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7]]}}}