Reflections on: KnowMore - Knowledge Base Augmentation with StructuredWeb Markup

Research output: Chapter in book/report/conference proceedingConference contributionResearch

Authors

  • Ran Yu
  • Ujwal Gadiraju
  • Besnik Fetahu
  • Oliver Lehmberg
  • Dominique Ritze
  • Stefan Dietze

Research Organisations

External Research Organisations

  • GESIS - Leibniz Institute for the Social Sciences
  • Heinrich-Heine-Universität Düsseldorf
  • University of Mannheim
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Details

Original languageEnglish
Title of host publicationJournal Track at ISWC 2019
Subtitle of host publicationProceedings of the Journal Track co-located with the 18th International Semantic Web Conference (ISWC 2019)
Publication statusPublished - 2019
Event18th International Semantic Web Conference, ISWC 2019 - Auckland, New Zealand
Duration: 26 Oct 201930 Oct 2019

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR Workshop Proceedings
Volume2576
ISSN (Print)1613-0073

Abstract

Knowledge bases are in widespread use for aiding tasks such as information extraction and information retrieval. However, knowledge bases are known to be inherently incomplete. As a complimentary data source, embedded entity markup based on Microdata, RDFa, and Microformats have become prevalent on the Web. RDF statements extracted from markup are fundamentally different from traditional knowledge graphs: entity descriptions are flat, facts are highly redundant and of varied quality, and, explicit links are missing despite a vast amount of coreferences. Therefore, data fusion is required in order to facilitate the use of markup data for KBA. We present a novel data fusion approach which addresses these issues. We perform a thorough evaluation on a subset of the Web Data Commons dataset and show significant potential for augmenting existing knowledge bases. A comparison with existing data fusion baselines demonstrates superior performance of our approach when applied to Web markup data.

ASJC Scopus subject areas

Cite this

Reflections on: KnowMore - Knowledge Base Augmentation with StructuredWeb Markup. / Yu, Ran; Gadiraju, Ujwal; Fetahu, Besnik et al.
Journal Track at ISWC 2019: Proceedings of the Journal Track co-located with the 18th International Semantic Web Conference (ISWC 2019). 2019. (CEUR Workshop Proceedings; Vol. 2576).

Research output: Chapter in book/report/conference proceedingConference contributionResearch

Yu, R, Gadiraju, U, Fetahu, B, Lehmberg, O, Ritze, D & Dietze, S 2019, Reflections on: KnowMore - Knowledge Base Augmentation with StructuredWeb Markup. in Journal Track at ISWC 2019: Proceedings of the Journal Track co-located with the 18th International Semantic Web Conference (ISWC 2019). CEUR Workshop Proceedings, vol. 2576, 18th International Semantic Web Conference, ISWC 2019, Auckland, New Zealand, 26 Oct 2019. <https://ceur-ws.org/Vol-2576/paper12.pdf>
Yu, R., Gadiraju, U., Fetahu, B., Lehmberg, O., Ritze, D., & Dietze, S. (2019). Reflections on: KnowMore - Knowledge Base Augmentation with StructuredWeb Markup. In Journal Track at ISWC 2019: Proceedings of the Journal Track co-located with the 18th International Semantic Web Conference (ISWC 2019) (CEUR Workshop Proceedings; Vol. 2576). https://ceur-ws.org/Vol-2576/paper12.pdf
Yu R, Gadiraju U, Fetahu B, Lehmberg O, Ritze D, Dietze S. Reflections on: KnowMore - Knowledge Base Augmentation with StructuredWeb Markup. In Journal Track at ISWC 2019: Proceedings of the Journal Track co-located with the 18th International Semantic Web Conference (ISWC 2019). 2019. (CEUR Workshop Proceedings).
Yu, Ran ; Gadiraju, Ujwal ; Fetahu, Besnik et al. / Reflections on: KnowMore - Knowledge Base Augmentation with StructuredWeb Markup. Journal Track at ISWC 2019: Proceedings of the Journal Track co-located with the 18th International Semantic Web Conference (ISWC 2019). 2019. (CEUR Workshop Proceedings).
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abstract = "Knowledge bases are in widespread use for aiding tasks such as information extraction and information retrieval. However, knowledge bases are known to be inherently incomplete. As a complimentary data source, embedded entity markup based on Microdata, RDFa, and Microformats have become prevalent on the Web. RDF statements extracted from markup are fundamentally different from traditional knowledge graphs: entity descriptions are flat, facts are highly redundant and of varied quality, and, explicit links are missing despite a vast amount of coreferences. Therefore, data fusion is required in order to facilitate the use of markup data for KBA. We present a novel data fusion approach which addresses these issues. We perform a thorough evaluation on a subset of the Web Data Commons dataset and show significant potential for augmenting existing knowledge bases. A comparison with existing data fusion baselines demonstrates superior performance of our approach when applied to Web markup data.",
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