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Knowledge graph construction from multiple online encyclopedias

  • Tianxing Wu
  • Haofen Wang
  • Cheng Li
  • Guilin QiEmail author
  • Xing Niu
  • Meng Wang
  • Lin Li
  • Chaomin Shi
Article
Part of the following topical collections:
  1. Special Issue on Application-Driven Knowledge Acquisition

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.

Keywords

Knowledge graph Knowledge extraction Knowledge linking Semantic Web 

Notes

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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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Tianxing Wu
    • 1
    • 2
  • Haofen Wang
    • 3
  • Cheng Li
    • 1
  • Guilin Qi
    • 1
    Email author
  • Xing Niu
    • 4
  • Meng Wang
    • 1
  • Lin Li
    • 1
  • Chaomin Shi
    • 1
  1. 1.Southeast UniversityNanjingChina
  2. 2.Nanyang Technological UniversitySingaporeSingapore
  3. 3.Intelligent Big Data Visualization Lab, Tongji UniversityShanghaiChina
  4. 4.University of MarylandCollege ParkUSA

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