Querying Graph Databases: What Do Graph Patterns Mean?

  • Stephan MennickeEmail author
  • Jan-Christoph Kalo
  • Wolf-Tilo Balke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10650)


Querying graph databases often amounts to some form of graph pattern matching. Finding (sub-)graphs isomorphic to a given graph pattern is common to many graph query languages, even though graph isomorphism often is too strict, since it requires a one-to-one correspondence between the nodes of the pattern and that of a match. We investigate the influence of weaker graph pattern matching relations on the respective queries they express. Thereby, these relations abstract from the concrete graph topology to different degrees. An extension of relation sequences, called failures which we borrow from studies on concurrent processes, naturally expresses simple presence conditions for relations and properties. This is very useful in application scenarios dealing with databases with a notion of data completeness. Furthermore, failures open up the query modeling for more intricate matching relations directly incorporating concrete data values.


Graph databases Query modeling Pattern matching 


  1. 1.
    Abriola, S., Barceió, P., Figueira, D., Figueira, S.: Bisimulations on data graphs. In: KR 2016, pp. 309–318. AAAI Press (2016)Google Scholar
  2. 2.
    Angles, R., Gutierrez, C.: Querying RDF data from a graph database perspective. In: Gómez-Pérez, A., Euzenat, J. (eds.) ESWC 2005. LNCS, vol. 3532, pp. 346–360. Springer, Heidelberg (2005). doi: 10.1007/11431053_24CrossRefGoogle Scholar
  3. 3.
    Brookes, S.D., Hoare, C.A.R., Roscoe, A.W.: A theory of communicating sequential processes. J. ACM 31(3), 560–599 (1984)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Brynielsson, J., Högberg, J., Kaati, L., Mårtenson, C., Svenson, P.: Detecting social positions using simulation. In: ASONAM 2010, pp. 48–55 (2010)Google Scholar
  5. 5.
    Fan, W.: Graph pattern matching revised for social network analysis. In: ICDT 2012, pp. 8–21. ACM, New York (2012)Google Scholar
  6. 6.
    Fan, W., Li, J., Ma, S., Tang, N., Wu, Y., Wu, Y.: Graph pattern matching: from intractable to polynomial time. PVLDB Endow. 3(1–2), 264–275 (2010)CrossRefGoogle Scholar
  7. 7.
    Gallagher, B.: Matching structure and semantics: a survey on graph-based pattern matching. In: Papers from the AAAI FS 2006, pp. 45–53 (2006)Google Scholar
  8. 8.
    van Glabbeek, R.J.: The linear time - branching time spectrum. In: Baeten, J.C.M., Klop, J.W. (eds.) CONCUR 1990. LNCS, vol. 458, pp. 278–297. Springer, Heidelberg (1990). doi: 10.1007/BFb0039066CrossRefGoogle Scholar
  9. 9.
    Henzinger, M., Henzinger, T., Kopke, P.: Computing simulations on finite and infinite graphs. In: FOCS 1995, pp. 453–462. IEEE Computer Society (1995)Google Scholar
  10. 10.
    Imielinski, T., Lipski Jr., W.: Incomplete information in relational databases. J. ACM 31(4), 761–791 (1984)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Khan, A., Wu, Y., Aggarwal, C.C., Yan, X.: NeMa: fast graph search with label similarity. PVLDB Endow. 6(3), 181–192 (2013)CrossRefGoogle Scholar
  12. 12.
    Lee, J., Han, W.S., Kasperovics, R., Lee, J.H.: An in-depth comparison of subgraph isomorphism algorithms in graph databases. PVLDB Endow. 6(2), 133–144 (2012)CrossRefGoogle Scholar
  13. 13.
    Libkin, L., Martens, W., Vrgoč, D.: Querying graph databases with XPath. In: ICDT 2013, pp. 129–140. ACM, New York (2013)Google Scholar
  14. 14.
    Libkin, L., Martens, W., Vrgoč, D.: Querying graphs with data. J. ACM 63(2), 14:1–14:53 (2016)MathSciNetCrossRefGoogle Scholar
  15. 15.
    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)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Motik, B., Horrocks, I., Sattler, U.: Bridging the gap between OWL and relational databases. In: WWW 2007, pp. 807–816. ACM, New York (2007)Google Scholar
  17. 17.
    Mottin, D., Lissandrini, M., Velegrakis, Y., Palpanas, T.: Exemplar queries: a new way of searching. VLDB J. 25(6), 741–765 (2016)CrossRefGoogle Scholar
  18. 18.
    Zheng, W., Zou, L., Lian, X., Wang, D., Zhao, D.: Efficient graph similarity search over large graph databases. IEEE Trans. Knowl. Data Eng. 27(4), 964–978 (2015)CrossRefGoogle Scholar
  19. 19.
    Zheng, W., Zou, L., Peng, W., Yan, X., Song, S., Zhao, D.: Semantic SPARQL similarity search over RDF knowledge graphs. PVLDB Endow. 9(11), 840–851 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Stephan Mennicke
    • 1
    Email author
  • Jan-Christoph Kalo
    • 2
  • Wolf-Tilo Balke
    • 2
  1. 1.Institut für Programmierung und Reaktive Systeme, TU BraunschweigBraunschweigGermany
  2. 2.Institut für Informationssysteme, TU BraunschweigBraunschweigGermany

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