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Retrieval, Crawling and Fusion of Entity-centric Data on the Web

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Semantic Keyword-Based Search on Structured Data Sources (IKC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10151))

Abstract

While the Web of (entity-centric) data has seen tremendous growth over the past years, take-up and re-use is still limited. Data vary heavily with respect to their scale, quality, coverage or dynamics, what poses challenges for tasks such as entity retrieval or search. This chapter provides an overview of approaches to deal with the increasing heterogeneity of Web data. On the one hand, recommendation, linking, profiling and retrieval can provide efficient means to enable discovery and search of entity-centric data, specifically when dealing with traditional knowledge graphs and linked data. On the other hand, embedded markup such as Microdata and RDFa has emerged a novel, Web-scale source of entity-centric knowledge. While markup has seen increasing adoption over the last few years, driven by initiatives such as schema.org, it constitutes an increasingly important source of entity-centric data on the Web, being in the same order of magnitude as the Web itself with regards to dynamics and scale. To this end, markup data lends itself as a data source for aiding tasks such as knowledge base augmentation, where data fusion techniques are required to address the inherent characteristics of markup data, such as its redundancy, heterogeneity and lack of links. Future directions are concerned with the exploitation of the complementary nature of markup data and traditional knowledge graphs.

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Notes

  1. 1.

    RDFa W3C recommendation: http://www.w3.org/TR/xhtml-rdfa-primer/.

  2. 2.

    http://www.w3.org/TR/microdata.

  3. 3.

    http://microformats.org.

  4. 4.

    http://www.datahub.io.

  5. 5.

    http://vocab.deri.ie/void.

  6. 6.

    http://data.linkededucation.org/vol/.

  7. 7.

    http://data-observatory.org/lod-profiles/profile-explorer/.

  8. 8.

    http://km.aifb.kit.edu/ws/semsearch10/.

  9. 9.

    RDFa W3C recommendation: http://www.w3.org/TR/xhtml-rdfa-primer/.

  10. 10.

    http://www.w3.org/TR/microdata.

  11. 11.

    http://microformats.org.

  12. 12.

    http://www.webdatacommons.org.

  13. 13.

    http://webdatacommons.org/structureddata/.

  14. 14.

    http://www.lrmi.net.

  15. 15.

    http://glimmer.research.yahoo.com/.

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Acknowledgements

While all discussed works are joint research with numerous colleagues, friends and collaborators from a number of research institutions, the author would like to thank all involved researchers for the inspiring and productive work throughout the previous years. In addition, the author expresses his gratitude to all funding bodies that enabled the presented research through a variety of funding programs.

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  1. L3S Research Center, Leibniz Universität Hannover, Hannover, Germany

    Stefan Dietze

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  1. Stefan Dietze
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Correspondence to Stefan Dietze .

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  1. Department of Computer Science and Information Systems, Birkbeck University of London, London, UK

    Andrea Calì

  2. Computer Science Department, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

    Dorian Gorgan

  3. Computer and Decision Engineering (CoDE) Department, Université Libre de Bruxelles, Brussels, Belgium

    Martín Ugarte

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Dietze, S. (2017). Retrieval, Crawling and Fusion of Entity-centric Data on the Web. In: Calì, A., Gorgan, D., Ugarte, M. (eds) Semantic Keyword-Based Search on Structured Data Sources. IKC 2016. Lecture Notes in Computer Science(), vol 10151. Springer, Cham. https://doi.org/10.1007/978-3-319-53640-8_1

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