Abstract
With the rise of artificial intelligence, knowledge graphs have been widely considered as a cornerstone of AI. In recent years, an increasing number of large-scale knowledge graphs have been constructed and published, by both academic and industrial communities, such as DBpedia, YAGO, Wikidata, Google Knowledge Graph, Microsoft Satori, Facebook Entity Graph, and others. In fact, a knowledge graph is essentially a large network of entities, their properties, semantic relationships between entities, and ontologies the entities conform to. Such kind of graph-based knowledge data has been posing a great challenge to the traditional data management theories and technologies. In this paper, we introduce the state-of-the-art research on knowledge graph data management, which includes knowledge graph data models, query languages, storage schemes, query processing, and reasoning. We will also describe the latest development trends of various database management systems for knowledge graphs.
Supported by the National Natural Science Foundation of China (61972275) and the Natural Science Foundation of Tianjin (17JCYBJC15400).
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References
Dbpedia. https://wiki.dbpedia.org/. Accessed 29 Nov 2019
The linked open data cloud. https://lod-cloud.net/. Accessed 29 Nov 2019
Linkedgeodata. http://linkedgeodata.org/. Accessed 29 Nov 2019
The neo4j manual v3.5. https://neo4j.com/docs/developer-manual/current/. Accessed 29 Nov 2019
Tinkerpop3 documentation v.3.4.4. https://tinkerpop.apache.org/docs/current/reference/. Accessed 29 Nov 2019
Uniprot. https://www.uniprot.org/. Accessed 29 Nov 2019
Abadi, D.J., Marcus, A., Madden, S.R., Hollenbach, K.: SW-store: a vertically partitioned DBMS for semantic web data management. VLDB J. 18(2), 385–406 (2009)
Angles, R., et al.: G-CORE: a core for future graph query languages. In: Proceedings of the 2018 International Conference on Management of Data, pp. 1421–1432. ACM (2018)
Angles, R., Gutierrez, C.: Survey of graph database models. ACM Comput. Surv. (CSUR) 40(1), 1 (2008)
Bornea, M.A., et al.: Building an efficient RDF store over a relational database. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pp. 121–132. ACM (2013)
Chen, X., Jia, S., Xiang, Y.: A review: knowledge reasoning over knowledge graph. Expert Syst. Appl. 141, 112948 (2019)
Cyganiak, R., Wood, D., Lanthaler, M., Klyne, G., Carroll, J.J., McBride, B.: RDF 1.1 concepts and abstract syntax. W3C Recomm. 25(02) (2014)
Harris, S., Gibbins, N.: 3store: efficient bulk RDF storage (2003)
Harris, S., Seaborne, A., Prud’hommeaux, E.: SPARQL 1.1 query language. W3C Recomm. 21(10), 778 (2013)
Kostylev, E.V., Reutter, J.L., Romero, M., Vrgoč, D.: SPARQL with property paths. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 3–18. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25007-6_1
Mendelzon, A.O., Wood, P.T.: Finding regular simple paths in graph databases. SIAM J. Comput. 24(6), 1235–1258 (1995)
Neumann, T., Weikum, G.: RDF-3X: a RISC-style engine for RDF. Proc. VLDB Endow. 1(1), 647–659 (2008)
van Rest, O., Hong, S., Kim, J., Meng, X., Chafi, H.: PGQL: a property graph query language. In: Proceedings of the Fourth International Workshop on Graph Data Management Experiences and Systems, p. 7. ACM (2016)
Robinson, I., Webber, J., Eifrem, E.: Graph Databases: New Opportunities for Connected Data. O’Reilly Media, Inc., Sebastopol (2015)
Wilkinson, K., Wilkinson, K.: Jena property table implementation (2006)
Zou, L., Özsu, M.T., Chen, L., Shen, X., Huang, R., Zhao, D.: gStore: a graph-based sparql query engine. VLDB J.- Int. J. Very Large Data Bases 23(4), 565–590 (2014)
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This work is supported by the National Natural Science Foundation of China (61572353) and the Natural Science Foundation of Tianjin (17JCYBJC15400).
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Wang, X., Chen, W. (2020). Knowledge Graph Data Management: Models, Methods, and Systems. In: U, L., Yang, J., Cai, Y., Karlapalem, K., Liu, A., Huang, X. (eds) Web Information Systems Engineering. WISE 2020. Communications in Computer and Information Science, vol 1155. Springer, Singapore. https://doi.org/10.1007/978-981-15-3281-8_1
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