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Semantic Interlinking

  • Reference work entry
  • First Online:
Encyclopedia of Big Data Technologies

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Definitions

Semantic interlinking is defined as the establishment of links and relations between multiple structured datasets.

Overview

Motivation

The exponential growth of data is becoming pervasive across different areas of business and science. Despite its wide availability in large amounts, data is typically stored in standalone silos where different datasets are represented using different formats, stored and indexed within different system architectures, and maintained following different business processes. For example, in certain organizations it is possible to encounter customer databases, technical reports, product images, and other datasets that need to be used in conjunction. Such data integration problems are a long-standing open research challenge in the data management area. The recent rise of big data with its volume and variety dimensions has magnified already existing issues.

Similar challenges are also often present in Open Data where datasets are published and made...

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References

  • Auer S, Bizer C, Kobilarov G, Lehmann J, Cyganiak R, Ives Z (2007) Dbpedia: a nucleus for a web of open data. In: The semantic web, 6th international semantic web conference, 2nd Asian semantic web conference, ISWC 2007 + ASWC 2007, Busan, Korea, 11–15 Nov 2007. Springer, Berlin, pp 722–735

    Google Scholar 

  • Bilenko M, Kamath B, Mooney RJ (2006) Adaptive blocking: learning to scale up record linkage. In: Sixth international conference on data mining (ICDM’06), pp 87–96. https://doi.org/10.1109/ICDM.2006.13

  • Bizer C, Heath T, Ayers D, Raimond Y (2007) Interlinking open data on the web. In: Demonstrations track, 4th European semantic web conference, Innsbruck

    Google Scholar 

  • Christen P (2012) A survey of indexing techniques for scalable record linkage and deduplication. IEEE Trans Knowl Data Eng 24(9):1537–1555. https://doi.org/10.1109/TKDE.2011.127

    Article  Google Scholar 

  • Demartini G, Difallah DE, Cudré-Mauroux P (2012) Zencrowd: leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking. In: Proceedings of the 21st international conference on world wide web. ACM, pp 469–478

    Google Scholar 

  • Demartini G, Difallah DE, Cudré-Mauroux P (2013) Large-scale linked data integration using probabilistic reasoning and crowdsourcing. VLDB J 22(5):665–687

    Article  Google Scholar 

  • Egami S, Kawamura T, Ohsuga A (2016) Building urban LOD for solving illegally parked bicycles in Tokyo. In: Proceedings 15th international semantic web conference, part II, the semantic web – ISWC 2016, Kobe, 17–21 Oct 2016, pp 291–307. https://doi.org/10.1007/978-3-319-46547-0_28

    Google Scholar 

  • Euzenat J, Meilicke C, Stuckenschmidt H, Shvaiko P, Trojahn C (2011) Ontology alignment evaluation initiative: six years of experience. In: Spaccapietra S (ed) Journal on data semantics XV. Springer, Berlin, pp 158–192

    Chapter  Google Scholar 

  • Jain P, Hitzler P, Sheth AP, Verma K, Yeh PZ (2010) Ontology alignment for linked open data. Springer, Berlin/Heidelberg, pp 402–417. https://doi.org/10.1007/978-3-642-17746-0_26

    Google Scholar 

  • Knoblock CA, Szekely PA, Fink EE, Degler D, Newbury D, Sanderson R, Blanch K, Snyder S, Chheda N, Jain N, Krishna RR, Sreekanth NB, Yao Y (2017) Lessons learned in building linked data for the American art collaborative. In: Proceedings of the 16th international semantic web conference, part II, the semantic web – ISWC 2017, Vienna, 21–25 Oct 2017, pp 263–279. https://doi.org/10.1007/978-3-319-68204-4_26

    Google Scholar 

  • Kuhn T, Willighagen E, Evelo C, Queralt-Rosinach N, Centeno E, Furlong LI (2017) Reliable granular references to changing linked data. Springer International Publishing, Cham, pp 436–451. https://doi.org/10.1007/978-3-319-68288-4_26

    Google Scholar 

  • Lin T, Mausam, Etzioni O (2012) Entity linking at web scale. In: Proceedings of the joint workshop on automatic knowledge base construction and web-scale knowledge extraction, association for computational linguistics, AKBC-WEKEX ’12, Stroudsburg, pp 84–88. http://dl.acm.org/citation.cfm?id=2391200.2391216

  • Moro A, Raganato A, Navigli R (2014) Entity linking meets word sense disambiguation: a unified approach. Trans Assoc Comput Linguist 2:231–244

    Article  Google Scholar 

  • Papadakis G, Ioannou E, Palpanas T, Niederee C, Nejdl W (2013) A blocking framework for entity resolution in highly heterogeneous information spaces. IEEE Trans Knowl Data Eng 25(12):2665–2682

    Article  Google Scholar 

  • Parundekar R, Knoblock CA, Ambite JL (2010) Linking and building ontologies of linked data. Springer, Berlin/Heidelberg, pp 598–614. https://doi.org/10.1007/978-3-642-17746-0_38

    Google Scholar 

  • Petersen N, Halilaj L, Grangel-González I, Lohmann S, Lange C, Auer S (2017) Realizing an RDF-based information model for a manufacturing company – a case study. In: Proceedings of the 16th international semantic web conference, part II, the semantic web – ISWC 2017, Vienna, 21–25 Oct 2017, pp 350–366. https://doi.org/10.1007/978-3-319-68204-4_31

    Google Scholar 

  • Rao D, McNamee P, Dredze M (2013) Entity linking: finding extracted entities in a knowledge base. Springer, Berlin/Heidelberg, pp 93–115. https://doi.org/10.1007/978-3-642-28569-1_5

    Google Scholar 

  • Rong S, Niu X, Xiang EW, Wang H, Yang Q, Yu Y (2012) A machine learning approach for instance matching based on similarity metrics. Springer, Berlin/Heidelberg, pp 460–475. https://doi.org/10.1007/978-3-642-35176-1_29

    Google Scholar 

  • Sarasua C, Simperl E, Noy NF (2012) Crowdmap: crowdsourcing ontology alignment with microtasks. In: International semantic web conference. Springer, pp 525–541

    Google Scholar 

  • Shen W, Wang J, Han J (2015) Entity linking with a knowledge base: issues, techniques, and solutions. IEEE Trans Knowl Data Eng 27(2):443–460

    Article  Google Scholar 

  • Shvaiko P, Euzenat J (2013) Ontology matching: state of the art and future challenges. IEEE Trans Knowl Data Eng 25(1):158–176. https://doi.org/10.1109/TKDE.2011.253

    Article  Google Scholar 

  • Vrandečić D, Krötzsch M (2014) Wikidata: a free collaborative knowledgebase. Commun ACM 57(10):78–85

    Article  Google Scholar 

  • Wang J, Kraska T, Franklin MJ, Feng J (2012) Crowder: crowdsourcing entity resolution. Proc VLDB Endow 5(11):1483–1494

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. The University of Queensland, St. Lucia, QLD, Australia

    Gianluca Demartini

Authors
  1. Gianluca Demartini
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Correspondence to Gianluca Demartini .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Sherif Sakr

  2. School of Information Technologies, Sydney University, Sydney, Australia

    Albert Y. Zomaya

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Demartini, G. (2019). Semantic Interlinking. In: Sakr, S., Zomaya, A.Y. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-77525-8_229

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