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A retrospective of knowledge graphs

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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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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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Correspondence to Ming Gao.

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Jihong Yan is a PhD candidate in Institute for Data Science and Engineering, East China Normal University, China. She is an associate professor at the Institute for Computer and Information Engineering, Shanghai Second Polytechnic University, China. Her research interests include Web data management and mining.

Chengyu Wang is a PhD candidate in Institute for Data Science and Engineering, East China Normal University, China. His research interests include Web data mining and information extraction. He is currently working on constructing Chinese knowledge graphs from Web data sources.

Wenliang Cheng is a graduate student of Institute for Data Science and Engineering, East China Normal University, China. His research interests include knowledge graph and natural language processing.

Ming Gao is an associate professor at the Institute for Data Science and Engineering, East China Normal University, China. He received his PhD in computer science from Fudan University, China. Prior to joining ECNU, Ming Gao worked with Living Analytics Research Centre (LARC), Singapore Management University, Singapore. His current research interests include distributed data management, user profiling and social mining, data stream management and mining, and uncertain data management.

Aoying Zhou is a professor of computer science at East China Normal University, China, where he is heading the Institute of Massive Computing. He is the winner of the National Science Fund for Distinguished Young Scholars supported by NSFC and the professorship appointment under Changjiang Scholars Program of Ministry of Education.

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Yan, J., Wang, C., Cheng, W. et al. A retrospective of knowledge graphs. Front. Comput. Sci. 12, 55–74 (2018). https://doi.org/10.1007/s11704-016-5228-9

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