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Pattern Match Query for Spatiotemporal RDF Graph

  • Xiaofeng Di
  • Jinyao Wang
  • Shaohui Cheng
  • Luyi BaiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)

Abstract

RDF is the W3C standard, whose model is defined as a triple. RDF is designed to provide a common way of describing resource so that it can be read and understood by computer applications. In RDF model, the statement in the resource description may correspond to a natural language statement, the resource corresponds to the subject in the natural language, the attribute type corresponds to the predicate, and the attribute value corresponds to the object. Meanwhile, RDF information has temporal attribute and spatial attribute. But classical RDF model can’t show the spatial and temporal properties of resources. So, combining spatiotemporal information with RDF is necessary. However, SPARQL, the W3C-recommended query language of RDF, only meets the classic RDF query. This paper presents a novel representation model of spatiotemporal RDF. Based on this model, a Find Isomorphic Graphs of the Query Graph algorithm is introduced to obtain some candidate isomorphic graph of the query graph. Finally, we define the process of pattern matching.

Keywords

Isomorphic graph Pattern matching Spatiotemporal RDF model 

Notes

Acknowledgment

This work was supported by the National Natural Science Foundation of China (61402087), the Natural Science Foundation of Hebei Province (F2019501030), the Natural Science Foundation of Liaoning Province (2019-MS-130), and the Fundamental Research Funds for the Central Universities (N172304026).

References

  1. 1.
    Klyne, G., Carroll, J.J.: Resource description framework (RDF): concepts and abstract syntax (2006)Google Scholar
  2. 2.
    Yan, Y., Wang, C., Zhou, A., et al.: Efficiently querying RDF data in triple stores. In: Proceedings of the 17th International Conference on World Wide Web, pp. 1053–1054. ACM (2008)Google Scholar
  3. 3.
    Chen, S.C., Shyu, M.L., Peeta, S., et al.: Learning-based spatio-temporal vehicle tracking and indexing for transportation multimedia database systems. IEEE Trans. Intell. Transp. Syst. 4(3), 154–167 (2003)CrossRefGoogle Scholar
  4. 4.
    Mennis, J.L., Fountain, A.G.: A spatio-temporal GIS database for monitoring alpine glacier change. Photogram. Eng. Remote Sens. 67(8), 967–974 (2001)Google Scholar
  5. 5.
    Antonić, O., Križanb, J., Marki, A., et al.: Spatio-temporal interpolation of climatic variables over large region of complex terrain using neural networks. Ecol. Model. 138(1–3), 255–263 (2001)CrossRefGoogle Scholar
  6. 6.
    Koubarakis, M., Kyzirakos, K.: Modeling and querying metadata in the semantic sensor web: the model stRDF and the query language stSPARQL. In: Aroyo, L., et al. (eds.) ESWC 2010. LNCS, vol. 6088, pp. 425–439. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-13486-9_29CrossRefGoogle Scholar
  7. 7.
    Perry, M., Sheth, A.P., Hakimpour, F., Jain, P.: Supporting complex thematic, spatial and temporal queries over semantic web data. In: Fonseca, F., Rodríguez, M.A., Levashkin, S. (eds.) GeoS 2007. LNCS, vol. 4853, pp. 228–246. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-76876-0_15CrossRefGoogle Scholar
  8. 8.
    Perry, M., Jain, P., Sheth, A.P.: SPARQL-ST: extending SPARQL to support spatiotemporal queries. In: Ashish, N., Sheth, A. (eds.) Geospatial Semantics and the Semantic Web. Semantic Web and Beyond (Computing for Human Experience), vol. 12, pp. 61–86. Springer, Boston (2011).  https://doi.org/10.1007/978-1-4419-9446-2_3CrossRefGoogle Scholar
  9. 9.
    Zou, L., Mo, J.H., Chen, L., et al.: gStore: answering SPARQL queries via subgraph matching. Proc. VLDB Endow. 4(8), 482–493 (2011)CrossRefGoogle Scholar
  10. 10.
    Li, G.F., Yan, L., Ma, Z.M.: Pattern match query over fuzzy RDF graph. Knowl.-Based Syst. 165, 460–473 (2019)CrossRefGoogle Scholar
  11. 11.
    Zou, L., Chen, L., Özsu, M.T.: Distance-join: pattern match query in a large graph database. Proc. VLDB Endow. 2(1), 886–897 (2009)CrossRefGoogle Scholar
  12. 12.
    Ma, Z.M., Li, G.F., Yan, L.: Fuzzy data modeling and algebraic operations in RDF. Fuzzy Sets Syst. 351, 41–63 (2018)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Wang, D., Zou, L., Zhao, D.Y.: gst-store: querying large spatiotemporal RDF graphs. Data Inf. Manag. 1(2), 84–103 (2017)Google Scholar
  14. 14.
    Gutierrez, C., Hurtado, C.A., Vaisman, A.: Introducing time into RDF. IEEE Trans. Knowl. Data Eng. 19(2), 207–218 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Xiaofeng Di
    • 1
  • Jinyao Wang
    • 1
  • Shaohui Cheng
    • 1
  • Luyi Bai
    • 1
    Email author
  1. 1.School of Computer and Communication EngineeringNortheastern University (Qinhuangdao)QinhuangdaoChina

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