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Frontiers of Computer Science

, Volume 12, Issue 1, pp 55–74 | Cite as

A retrospective of knowledge graphs

  • Jihong Yan
  • Chengyu Wang
  • Wenliang Cheng
  • Ming Gao
  • Aoying Zhou
Review Article
  • 212 Downloads

Abstract

Information on the Internet is fragmented and presented in different data sources, which makes automatic knowledge harvesting and understanding formidable for machines, and even for humans. Knowledge graphs have become prevalent in both of industry and academic circles these years, to be one of the most efficient and effective knowledge integration approaches. Techniques for knowledge graph construction can mine information from either structured, semi-structured, or even unstructured data sources, and finally integrate the information into knowledge, represented in a graph. Furthermore, knowledge graph is able to organize information in an easy-to-maintain, easy-to-understand and easy-to-use manner.

In this paper, we give a summarization of techniques for constructing knowledge graphs. We review the existing knowledge graph systems developed by both academia and industry. We discuss in detail about the process of building knowledge graphs, and survey state-of-the-art techniques for automatic knowledge graph checking and expansion via logical inferring and reasoning. We also review the issues of graph data management by introducing the knowledge data models and graph databases, especially from a NoSQL point of view. Finally, we overview current knowledge graph systems and discuss the future research directions.

Keywords

knowledge graph knowledge base information extraction logical reasoning graph database 

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Notes

Acknowledgements

This work has been supported by the National Key Research and Development Program of China (2016YFB1000905), the National Natural Science Foundation of China (Grant Nos. U1401256, 61402177, 61402180) and the Natural Science Foundation of Shanghai (14ZR1412600). This work was also supported by CCF-Tecent Research Program of China (AGR20150114). The author would also like to thank Key Disciplines of Software Engineering of Shanghai Second Polytechnic University (XXKZD1301).

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Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2018

Authors and Affiliations

  • Jihong Yan
    • 1
    • 2
  • Chengyu Wang
    • 1
  • Wenliang Cheng
    • 1
  • Ming Gao
    • 1
  • Aoying Zhou
    • 3
  1. 1.Institute for Data Science and EngineeringEast China Normal UniversityShanghaiChina
  2. 2.Institute for Computer and Information EngineeringShanghai Second Polytechnic UniversityShanghaiChina
  3. 3.Shanghai Key Lab for Trustworthy ComputingEast China Normal UniversityShanghaiChina

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