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Genetic-Fuzzy Programming Based Linkage Rule Miner (GFPLR-Miner) for Entity Linking in Semantic Web

Genetic-Fuzzy Programming Based Linkage Rule Miner (GFPLR-Miner) for Entity Linking in Semantic Web

Amit Singh, Aditi Sharan
Copyright: © 2021 |Pages: 35
ISBN13: 9781799880486|ISBN10: 1799880486|EISBN13: 9781799880998
DOI: 10.4018/978-1-7998-8048-6.ch023
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MLA

Singh, Amit, and Aditi Sharan. "Genetic-Fuzzy Programming Based Linkage Rule Miner (GFPLR-Miner) for Entity Linking in Semantic Web." Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, edited by Information Resources Management Association, IGI Global, 2021, pp. 447-481. https://doi.org/10.4018/978-1-7998-8048-6.ch023

APA

Singh, A. & Sharan, A. (2021). Genetic-Fuzzy Programming Based Linkage Rule Miner (GFPLR-Miner) for Entity Linking in Semantic Web. In I. Management Association (Ed.), Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms (pp. 447-481). IGI Global. https://doi.org/10.4018/978-1-7998-8048-6.ch023

Chicago

Singh, Amit, and Aditi Sharan. "Genetic-Fuzzy Programming Based Linkage Rule Miner (GFPLR-Miner) for Entity Linking in Semantic Web." In Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, edited by Information Resources Management Association, 447-481. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-8048-6.ch023

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Abstract

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.

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