An Evolutionary Algorithm to Learn SPARQL Queries for Source-Target-Pairs

Finding Patterns for Human Associations in DBpedia
  • Jörn HeesEmail author
  • Rouven Bauer
  • Joachim Folz
  • Damian Borth
  • Andreas Dengel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10024)


Efficient usage of the knowledge provided by the Linked Data community is often hindered by the need for domain experts to formulate the right SPARQL queries to answer questions. For new questions they have to decide which datasets are suitable and in which terminology and modelling style to phrase the SPARQL query.

In this work we present an evolutionary algorithm to help with this challenging task. Given a training list of source-target node-pair examples our algorithm can learn patterns (SPARQL queries) from a SPARQL endpoint. The learned patterns can be visualised to form the basis for further investigation, or they can be used to predict target nodes for new source nodes.

Amongst others, we apply our algorithm to a dataset of several hundred human associations (such as “circle - square”) to find patterns for them in DBpedia. We show the scalability of the algorithm by running it against a SPARQL endpoint loaded with \(> 7.9\) billion triples. Further, we use the resulting SPARQL queries to mimic human associations with a Mean Average Precision (MAP) of \(39.9\,\%\) and a Recall@10 of \(63.9\,\%\).


  1. 1.
    Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Sci. Am. 284(5), 34–43 (2001)CrossRefGoogle Scholar
  2. 2.
    Bizer, C., Heath, T., Berners-Lee, T.: Linked Data - The Story So Far. Int. J. Semant. Web Inf. Syst. 5(3), 1–22 (2009)CrossRefGoogle Scholar
  3. 3.
    Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: DBpedia - a crystallization point for the Web of Data. Web Semant. Sci. Serv. Agents World Wide Web 7(3), 154–165 (2009)CrossRefGoogle Scholar
  4. 4.
    Fortin, F.A., De Rainville, F.M., Gardner, M.A., Parizeau, M., Gagné, C.: DEAP: evolutionary algorithms made easy. J. Mach. Learn. Res. 13, 2171–2175 (2012)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Hees, J., Bauer, R., Folz, J., Borth, D., Dengel, A.: Edinburgh associative thesaurus as RDF and DBpedia mapping. The Semantic Web. In: ESWC SE. Springer, Heraklion, Crete, Greece (2016)Google Scholar
  6. 6.
    Hees, J., Khamis, M., Biedert, R., Abdennadher, S., Dengel, A.: Collecting links between entities ranked by human association strengths. In: Cimiano, P., Corcho, O., Presutti, V., Hollink, L., Rudolph, S. (eds.) ESWC 2013. LNCS, vol. 7882, pp. 517–531. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Hees, J., Roth-Berghofer, T., Biedert, R., Adrian, B., Dengel, A.: BetterRelations: using a game to rate linked data triples. In: Bach, J., Edelkamp, S. (eds.) KI 2011. LNCS (LNAI), vol. 7006, pp. 134–138. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-24455-1_12 CrossRefGoogle Scholar
  8. 8.
    Hees, J., Roth-Berghofer, T., Biedert, R., Adrian, B., Dengel, A.: BetterRelations: collecting association strengths for linked data triples with a game. In: Ceri, S., Brambilla, M. (eds.) Search Computing. LNCS, vol. 7538, pp. 223–239. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Hees, J., Roth-Berghofer, T., Dengel, A.: Linked data games: simulating human association with linked data. In: LWA, pp. 255–260. Kassel, Germany (2010)Google Scholar
  10. 10.
    Heim, P., Hellmann, S., Lehmann, J., Lohmann, S., Stegemann, T.: RelFinder: revealing relationships in RDF knowledge bases. In: Chua, T.-S., Kompatsiaris, Y., Mérialdo, B., Haas, W., Thallinger, G., Bailer, W. (eds.) SAMT 2009. LNCS, vol. 5887, pp. 182–187. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Heim, P., Lohmann, S., Stegemann, T.: Interactive relationship discovery via the semantic web. In: Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010, Part I. LNCS, vol. 6088, pp. 303–317. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Kiss, G.R., Armstrong, C., Milroy, R., Piper, J.: An associative thesaurus of English and its computer analysis. In: The Computer and Literary Studies, pp. 153–165. Edinburgh University Press, Edinburgh, UK (1973)Google Scholar
  13. 13.
    Klyne, G., Carroll, J.J.: Resource Description Framework (RDF): Concepts and Abstract Syntax (2004).
  14. 14.
    Ławrynowicz, A., Potoniec, J.: Pattern based feature construction in semantic data mining. Int. J. SemWeb Inf. Syst. (IJSWIS) 10(1), 27–65 (2014)Google Scholar
  15. 15.
    Lehmann, J., Bühmann, L.: AutoSPARQL: let users query your knowledge base. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011, Part I. LNCS, vol. 6643, pp. 63–79. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  16. 16.
    McCusker, J.P.: WebSig: a digital signature framework for the web. Ph.D. thesis, Rensselaer Polytechnic Institute, Troy, NY (2015)Google Scholar
  17. 17.
    Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E.: A review of relational machine learning for knowledge graphs, pp. 1–23 (2015)Google Scholar
  18. 18.
    Reddy, D., Knuth, M., Sack, H.: DBpedia GraphMeasures (2014).
  19. 19.
    Thalhammer, A., Rettinger, A.: PageRank on Wikipedia: towards general importance scores for entities. In: Know@LOD&CoDeS 2016, CEUR-WS Proceedings (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Jörn Hees
    • 1
    • 2
    Email author
  • Rouven Bauer
    • 1
    • 2
  • Joachim Folz
    • 1
    • 2
  • Damian Borth
    • 1
    • 2
  • Andreas Dengel
    • 1
    • 2
  1. 1.Computer Science DepartmentUniversity of KaiserslauternKaiserslauternGermany
  2. 2.Knowledge Management DepartmentDFKI GmbHKaiserslauternGermany

Personalised recommendations