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Cluster query: a new query pattern on temporal knowledge graph

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Abstract

A temporal knowledge graph (TKG) is theoretically a temporal graph. Recently, systems have been developed to support snapshot queries over temporal graphs. However, snapshot queries can only give separate answers. To retrieve forward-backward correlation facts from temporal knowledge graph, cluster query is proposed in this paper. To deal with the query, the logical view and physical model are presented. Subsequently, five corresponding basic query patters of unit matching are studied, and then the complete matchings are also addressed. To improve the query performance, index-based methods and pruning strategies are adopted. Experiments are conducted to evaluate cluster queries on three real datasets. The experimental results show the effectiveness and efficiency of cluster queries on temporal knowledge graphs.

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References

  1. Bollacker, K. D., Cook, R. P., Tufts, P.: Freebase: A shared database of structured general human knowledge. In: Proceedings of the 22nd conference on artificial intelligence (AAAI), pp. 1962–1963 (2007)

  2. Wikedata. https://www.wikidata.org/wiki/Wikidata:Main_Page

  3. Suchanek, F. M., Kasneci, G., Weikum, G.: Yago: A core of semantic knowledge. In: Proceedings of the 16th international conference on World Wide Web (WWW), p.p 697–706 (2007)

  4. Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: Dbpedia - a crystallization point for the web of data Web Semantics: Science. Services and Agents on the World Wide Web 7(3), 154–165 (2009)

    Article  Google Scholar 

  5. Gutierrez, C., Hurtado, C. A., Vaisman, A. A.: Temporal RDF. In: Proceedings of the 2nd European semantic Web conference (ESWC), pp. 93–107 (2005)

    Google Scholar 

  6. Gutierrez, C., Hurtado, C. A., Vaisman, A. A.: Introducing time into RDF. IEEE Transaction on Knowledge and Data Engineering 19(2), 207–218 (2007)

    Article  Google Scholar 

  7. Dylla, M., Sozio, M., Theobald, M.: Resolving temporal conflicts in inconsistent RDF knowledge bases. In: Proceedings of Datenbanksysteme für Business, Technologie und Web (BTW), pp. 474–493 (2011)

  8. Dylla, M., Miliaraki, I., Theobald, M.: A temporal-probabilistic database model for information extraction. PVLDB 6(14), 1810–1821 (2013)

    Google Scholar 

  9. Chekol, M. W., Pirro, G., Schoenfisch, J., Stuckenschmidt, H.: Marring uncertainty and time in knowledge graphs. In: Proceedings of the 31st AAAI conference on artificial intelligence (AAAI), pp 88–94 (2017)

  10. Trivedi, R., Dai, H., Wang, Y., Song, L.: Know-evolve: Deep temporal reasoning for dynamic knowledge graphs. In: proceedings of the 34th international conference on machine learning (ICML), pp. 3462–3471 (2017)

  11. Zhao, Y., Zheng, K., Li, Y., Su, H., Liu, J., Zhou, X.: Destination-aware task assignment in spatial crowdsourcing: A worker decomposition approach. IEEE Transactions on Knowledge and Data Engineering (2019)

  12. Lian, D., Zheng, K., Ge, Y., Cao, L., Chen, E., Xie, X.: Geomf++:, Scalable location recommendation via joint geographical modeling and matrix factorization. ACM Trans. Inf. Syst. 36(3), 33:1–33:29 (2018)

    Article  Google Scholar 

  13. Zheng, K., Zhao, Y., Lian, D., Zheng, B., Liu, G., Zhou, X.: Reference-based framework for spatio-temporal trajectory compression and query processing. IEEE Trans. Knowl. Data Eng. 05, 1–1 (2019)

    Google Scholar 

  14. Li, L., Zheng, K., Wang, S., Hua, W., Zhou, X.: Go slow to go fast: minimal on-road time route scheduling with parking facilities using historical trajectory. VLDB J. 27(3), 321–345 (2018)

    Article  Google Scholar 

  15. Zheng, B., Su, H., Hua, W., Zheng, K., Zhou, X., Li, G.: Efficient clue-based route search on road networks. IEEE Trans. Knowl Data Eng. 29 (9), 1846–1859 (2017)

    Article  Google Scholar 

  16. Pėrez, J., Arenas, M., Gutierrez, C.: Semantics and complexity of SPARQL. ACM Trans. Database Syst. 34(3), 16:1–16:45 (2009)

    Article  Google Scholar 

  17. Quilitz, B., Leser, U.: Querying distributed RDF data sources with SPARQL. In: Proceedings of the 5th European semantic Web conference (ESWC), pp. 524–538 (2008)

  18. Abadi, D. J., Marcus, A., Madden, S., Hollenbach, K.: Sw-store: A vertically partitioned DBMS for semantic web data management. The VLDB J. 18(2), 385–406 (2009)

    Article  Google Scholar 

  19. Wu, B., Zhou, Y., Yuan, P., Liu, L., Jin, H.: Scalable SPARQL querying using path partitioning. In: Proceedings of the 31st IEEE international conference on data engineering (ICDE), pp. 795–806 (2015)

  20. Abdelaziz, I., Harbi, R., Khayyat, Z., Kalnis, P.: A survey and experimental comparison of distributed SPARQL engines for very large RDF data. PVLDB 10(13), 2049–2060 (2017)

    Google Scholar 

  21. Angles, R., Gutierrez, C.: Querying RDF data from a graph database perspective. In: Proceedings of the 2nd European semantic Web conference (ESWC), pp. 346–360 (2005)

    Google Scholar 

  22. Zou, L., Tamer Ȯzsu, M., Chen, L., Shen, X., Huang, R., Zhao, D.: gstore: a graph-based SPARQL query engine. The VLDB Journal 23(4), 565–590 (2014)

    Article  Google Scholar 

  23. Yang, S., Han, F., Wu, Y., Yan, X.: Fast top-k search in knowledge graphs. In: Proceedings of the 32nd IEEE international conference on data engineering (ICDE), pp. 990–1001 (2016)

  24. Yahya, M., Barbosa, D., Berberich, K., Wang, Q., Weikum, G.: Relationship queries on extended knowledge graphs. In: Proceedings of the 9th ACM international conference on web search and data mining (WSDM), pp. 605–614 (2016)

  25. Jayaram, N., Gupta, M., Khan, A., Li, C., Yan, X., Elmasri, R.: GQBE: querying knowledge graphs by example entity tuples. In: Proceedings of the 30th IEEE international conference on data engineering (ICDE), pp. 1250–1253 (2014)

  26. Zheng, W., Lian, X., Zou, L., Hong, L., Zhao, D.: Online subgraph skyline analysis over knowledge graphs. IEEE Trans Knowl Data Eng 28(7), 1805–1819 (2016)

    Article  Google Scholar 

  27. Yahya, M., Berberich, K., Ramanath, M., Weikum, G.: Exploratory querying of extended knowledge graphs. PVLDB 9(13), 1521–1524 (2016)

    Google Scholar 

  28. Cheng, J., Yu, J. X., Ding, B., Yu, P. S., Wang, H.: Fast graph pattern matching. In: Proceedings of the 24th international conference on data engineering (ICDE), pp. 913–922 (2008)

  29. Zou, L., Chen, L., Ȯzsu, M.T.: Distancejoin: Pattern match query in a large graph database. PVLDB 2(1), 886–897 (2009)

    Google Scholar 

  30. Lee, J., Han, W., Kasperovics, R., Lee, J.: An in-depth comparison of subgraph isomorphism algorithms in graph databases. PVLDB 6(2), 133–144 (2012)

    Google Scholar 

  31. Fan, W., Li, J., Ma, S., Tang, N., Wu, Y., Wu, Y.: Graph pattern matching: From intractable to polynomial time. PVLDB 3(1), 264–275 (2010)

    Google Scholar 

  32. Ma, S., Cao, Y., Fan, W., Huai, J., Wo, T.: Strong simulation: Capturing topology in graph pattern matching. ACM Trans. Database Syst. 39(1), 4:1–4:46 (2014)

    Article  MathSciNet  Google Scholar 

  33. Song, C., Ge, T., Chen, C. X., Wang, J.: Event pattern matching over graph streams. PVLDB 8(4), 413–424 (2014)

    Google Scholar 

  34. Xu, Y., Huang, J., Liu, A., Li, Z., Yin, H., Zhao, L.: Time-constrained graph pattern matching in a large temporal graph. In: Proceedings of the the 1st international joint conference on web and big data (APWEB-WAIM), pp. 100–115 (2017)

  35. Zheng, K., Zheng, Y., Yuan, N. J., Shang, S., Zhou, X.: Online discovery of gathering patterns over trajectories. IEEE Trans. Knowl Data Eng. 26(8), 1974–1988 (2014)

    Article  Google Scholar 

  36. Liu, G., Liu, Y., Zheng, K., Liu, A., Li, Z., Wang, Y., Zhou, X.: MCS-GPM: multi-constrained simulation based graph pattern matching in contextual social graphs. IEEE Trans. Knowl. Data Eng. 30(6), 1050–1064 (2018)

    Article  Google Scholar 

  37. Allen, J. F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983)

    Article  Google Scholar 

  38. Lee, J., Clifton, C. W.: Top-k frequent itemsets via differentially private fp-trees. In: The 20th ACM SIGKDD international conference on knowledge discovery and data mining(KDD), pp. 931–940 (2014)

  39. Cordella, L. P., Foggia, P., Sansone, C., Vento, M.: A (sub)graph isomorphism algorithm for matching large graphs. IEEE Trans. Pattern Anal. Mach Intell. 26(10), 1367–1372 (2004)

    Article  Google Scholar 

  40. He, H., Singh, A. K.: Graphs-at-a-time: query language and access methods for graph databases. In: Proceedings of the ACM SIGMOD international conference on management of data, SIGMOD 2008, Vancouver, BC, Canada, June 10-12, 2008, pp. 405–418 (2008)

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 61572335, 61572336, 61902270), and the Major Program of Natural Science Foundation, Educational Commission of Jiangsu Province, China (Grant No. 19KJA610002), and the Natural Science Foundation, Educational Commission of Jiangsu Province, China (Grant No. 19KJB520052, 19KJB520050), and Collaborative Innovation Center of Novel Software Technology and Industrialization, Jiangsu, China.

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Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, China

    Jinjing Huang, Wei Chen, An Liu & Lei Zhao

  2. School of Software and Service Outsourcing, Suzhou Vocational Institute of Industrial Technology, Suzhou, China

    Jinjing Huang

  3. Faculty of Information Technology, Monash University, Melbourne, Australia

    Weiqing Wang

  4. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia

    Hongzhi Yin

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  1. Jinjing Huang
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  2. Wei Chen
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  3. An Liu
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Correspondence to Lei Zhao.

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This article belongs to the Topical Collection: Special Issue on Graph Data Management in Online Social Networks

Guest Editors: Kai Zheng, Guanfeng Liu, Mehmet A. Orgun, and Junping Du

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Huang, J., Chen, W., Liu, A. et al. Cluster query: a new query pattern on temporal knowledge graph. World Wide Web 23, 755–779 (2020). https://doi.org/10.1007/s11280-019-00754-1

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