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A retrospective of knowledge graphs |
Jihong YAN1,2, Chengyu WANG1, Wenliang CHENG1, Ming GAO1( ), Aoying ZHOU3 |
1. Institute for Data Science and Engineering, East China Normal University, Shanghai 200062, China 2. Institute for Computer and Information Engineering, Shanghai Second Polytechnic University, Shanghai 201209, China 3. Shanghai Key Lab for Trustworthy Computing, East China Normal University, Shanghai 200062, China |
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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.
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Keywords
knowledge graph
knowledge base
information extraction
logical reasoning
graph database
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Corresponding Author(s):
Ming GAO
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Just Accepted Date: 22 February 2016
Online First Date: 17 October 2016
Issue Date: 12 January 2018
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