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Richpedia: A Comprehensive Multi-modal Knowledge Graph

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Semantic Technology (JIST 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12032))

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Abstract

Large-scale knowledge graphs such as Wikidata and DBpedia have become a powerful asset for semantic search and question answering. However, most of the knowledge graph construction works focus on organizing and discovering textual knowledge in a structured representation while paying little attention to the proliferation of visual resources on the Web. To improve the situation, in this paper, we present Richpedia, aim to provide a comprehensive multi-modal knowledge graph by distributing sufficient and diverse images to textual entities in Wikidata. We also set RDF links (visual semantic relations) between image entities based on the hyperlinks and descriptions in Wikipedia. The Richpedia resource is accessible on the Web via a faceted query endpoint and provides a pathway for knowledge graph and computer vision tasks, such as link prediction and visual relation detection.

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Notes

  1. 1.

    https://jena.apache.org/documentation/fuseki2/index.html.

  2. 2.

    https://www.w3.org/TR/rdf11-concepts/#dfn-iri.

  3. 3.

    https://www.wikidata.org/wiki/Wikidata:Main_Page.

  4. 4.

    https://www.wikipedia.org/.

  5. 5.

    https://www.google.com/.

  6. 6.

    https://search.yahoo.com/.

  7. 7.

    https://www.bing.com/.

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Acknowledgment

This work was supported by National Science Foundation of China with Grant Nos. 61906037 and U1736204; National Key Research and Development Program of China with Grant Nos. 2018YFC0830201 and 2017YFB1002801; the Judicial Big Data Research Centre, School of Law at Southeast University with Grant No.4313059291; the Fundamental Research Funds for the Central Universities with Grant No.4009009106.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Southeast University, Nanjing, China

    Meng Wang & Guilin Qi

  2. Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China

    Meng Wang & Guilin Qi

  3. Intelligent Big Data Visualization Lab, Tongji University, Shanghai, China

    HaoFen Wang

  4. School of Cyber Science and Engineering, Southeast University, Nanjing, China

    Qiushuo Zheng

Authors
  1. Meng Wang
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  2. Guilin Qi
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  3. HaoFen Wang
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  4. Qiushuo Zheng
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Corresponding author

Correspondence to Meng Wang .

Editor information

Editors and Affiliations

  1. Tianjin University, Tianjin, China

    Xin Wang

  2. University of Bari, Bari, Italy

    Francesca Alessandra Lisi

  3. Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Guohui Xiao

  4. Imperial College London, London, UK

    Elena Botoeva

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Wang, M., Qi, G., Wang, H., Zheng, Q. (2020). Richpedia: A Comprehensive Multi-modal Knowledge Graph. In: Wang, X., Lisi, F., Xiao, G., Botoeva, E. (eds) Semantic Technology. JIST 2019. Lecture Notes in Computer Science(), vol 12032. Springer, Cham. https://doi.org/10.1007/978-3-030-41407-8_9

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  • DOI: https://doi.org/10.1007/978-3-030-41407-8_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41406-1

  • Online ISBN: 978-3-030-41407-8

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