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Knowledge graph construction from multiple online encyclopedias

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Abstract

In recent years, lots of knowledge graphs built from Wikipedia, the largest multilingual online encyclopedia, have been published on the Web to support various applications. However, since non-English data in Wikipedia are sparse, some projects work on knowledge graph construction from multiple non-English online encyclopedias, but many technical details are missing, so it is hard to reuse their frameworks or techniques. In this paper, we propose a new framework to solve knowledge graph construction from multiple online encyclopedias. The core modules are knowledge extraction and knowledge linking. Knowledge extraction consists of regular extraction, i.e., extracting targeted article contents in the whole online encyclopedias periodically, and live extraction, which only extracts the article contents of new and updated entities. Knowledge linking utilizes heuristic lightweight entity matching strategies and a semi-supervised learning method to find duplicated entities and properties from different online encyclopedias. Experimental results show that our approaches for knowledge extraction and linking outperform state-of-the-art baselines in different evaluation metrics, and our framework can generate a large-scale knowledge graph after inputting multiple online encyclopedias.

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Notes

  1. http://www.w3.org/wiki/SweoIG/TaskForces/CommunityProjects/LinkingOpenData

  2. https://baike.baidu.com/

  3. http://www.baike.com/

  4. http://www.doopedia.co.kr/

  5. https://www.ecured.cu/

  6. https://zh.wikipedia.org/

  7. https://stream.wikimedia.org/v2/stream/recentchange/

  8. http://www.geonames.org/

  9. http://www.omegawiki.org/

  10. https://www.wiktionary.org/

  11. http://dumps.wikimedia.org/

  12. http://www.mediawiki.org/wiki/Extension:OAIRepository

  13. We directly use Google Translate: https://translate.google.com/.

  14. owl:sameAs denotes the equality relation (between individual entities) defined by the W3C Web Ontology Language.

  15. The Dempster’s rule has the best performance in combining entity matching rules in our previous work [24] when comparing with other combination methods.

  16. http://oaei.ontologymatching.org/

  17. http://zhishi.me/

  18. https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/linking/

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Acknowledgements

This work was supported in part by National Key R&D Program of China (2017YFB1002801, 2018YFC0830200), National Natural Science Foundation of China Key Project (U1736204), and the Judicial Big Data Research Centre, School of Law at Southeast University.

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Authors and Affiliations

  1. Southeast University, Nanjing, China

    Tianxing Wu, Cheng Li, Guilin Qi, Meng Wang, Lin Li & Chaomin Shi

  2. Nanyang Technological University, Singapore, Singapore

    Tianxing Wu

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

    Haofen Wang

  4. University of Maryland, College Park, MD, USA

    Xing Niu

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  1. Tianxing Wu
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Correspondence to Guilin Qi.

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Wu, T., Wang, H., Li, C. et al. Knowledge graph construction from multiple online encyclopedias. World Wide Web 23, 2671–2698 (2020). https://doi.org/10.1007/s11280-019-00719-4

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