On Enhancing Visual Query Building over KGs Using Query Logs

  • Vidar KlungreEmail author
  • Ahmet Soylu
  • Martin Giese
  • Arild Waaler
  • Evgeny Kharlamov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11341)


Knowledge Graphs have recently gained a lot of attention and have been successfully applied in both academia and industry. Since KGs may be very large: they may contain millions of entities and triples relating them to each other, to classes, and assigning them data values, it is important to provide endusers with effective tools to explore information encapsulated in KGs. In this work we present a visual query system that allows users to explore KGs by intuitively constructing tree-shaped conjunctive queries. It is known that systems of this kind suffer from the problem of information overflow: when constructing a query the users have to iteratively choose from a potentially very long list of options, sich as, entities, classes, and data values, where each such choice corresponds to an extension of the query new filters. In order to address this problem we propose an approach to substantially reduce such lists with the help of ranking and by eliminating the so-called deadends, options that yield queries with no answers over a given KG.



This work is partially funded by EU H2020 TheyBuyForYou (780247) project, by the EPSRC projects MaSI\(^3\), DBOnto, ED\(^3\), and by the SIRIUS Centre, Norwegian Research Council project number 237898.


  1. 1.
  2. 2.
  3. 3.
    W3C: OWL 2 Web Ontology Language.
  4. 4.
    W3C: Resource Description Framework (RDF).
  5. 5.
    Arenas, M., Grau, B.C., Kharlamov, E., Marciuska, S., Zheleznyakov, D.: Faceted search over ontology-enhanced RDF data. In: CIKM, pp. 939–948 (2014)Google Scholar
  6. 6.
    Arenas, M., Grau, B.C., Kharlamov, E., Marciuska, S., Zheleznyakov, D.: Faceted search over RDF-based knowledge graphs. J. Web Sem. 37–38, 55–74 (2016)CrossRefGoogle Scholar
  7. 7.
    Broekstra, J., Kampman, A., van Harmelen, F.: Sesame: a generic architecture for storing and querying RDF and RDF schema. In: Horrocks, I., Hendler, J. (eds.) ISWC 2002. LNCS, vol. 2342, pp. 54–68. Springer, Heidelberg (2002). Scholar
  8. 8.
    Franconi, E., Guagliardo, P., Trevisan, M., Tessaris, S.: Quelo: an ontology-driven query interface. In: DL (2011)Google Scholar
  9. 9.
    Grau, B.C., et al.: Towards query formulation, query-driven ontology extensions in OBDA systems. In: OWLED (2013)Google Scholar
  10. 10.
    Haag, F., Lohmann, S., Siek, S., Ertl, T.: Visual querying of linked data with QueryVOWL. In: Joint Proceedings of SumPre 2015 and HSWI 2014–15. CEUR-WS (2015)Google Scholar
  11. 11.
    Harabagiu, S.M., et al.: FALCON: boosting knowledge for answer engines. In: TREC (2000)Google Scholar
  12. 12.
    Harris, S., Seaborne, A.: SPARQL 1.1 query language. W3C Recommendation, 21 March 2013Google Scholar
  13. 13.
    Heim, P., Ertl, T., Ziegler, J.: Facet graphs: complex semantic querying made easy. In: Aroyo, L., et al. (eds.) ESWC 2010. LNCS, vol. 6088, pp. 288–302. Springer, Heidelberg (2010). Scholar
  14. 14.
    Horrocks, I., Giese, M., Kharlamov, E., Waaler, A.: Using semantic technology to tame the data variety challenge. IEEE Internet Comput. 20(6), 62–66 (2016)CrossRefGoogle Scholar
  15. 15.
    Huang, H., Liu, C., Zhou, X.: Computing relaxed answers on RDF databases. In: Bailey, J., Maier, D., Schewe, K.-D., Thalheim, B., Wang, X.S. (eds.) WISE 2008. LNCS, vol. 5175, pp. 163–175. Springer, Heidelberg (2008). Scholar
  16. 16.
    Kharlamov, E., et al.: Enabling semantic access to static and streaming distributed data with optique: demo. In: DEBS, pp. 350–353 (2016)Google Scholar
  17. 17.
    Kharlamov, E., et al.: Ontology-based integration of streaming and static relational data with optique. In: SIGMOD, pp. 2109–2112 (2016)Google Scholar
  18. 18.
    Kharlamov, E., Giacomelli, L., Sherkhonov, E., Grau, B.C., Kostylev, E.V., Horrocks, I.: Ranking, aggregation, and reachability in faceted search with SemFacet. In: ISWC Posters & Demonstrations (2017)Google Scholar
  19. 19.
    Kharlamov, E., Giacomelli, L., Sherkhonov, E., Grau, B.C., Kostylev, E.V., Horrocks, I.: Semfacet: making hard faceted search easier. In: CIKM, pp. 2475–2478 (2017)Google Scholar
  20. 20.
    Kharlamov, E., et al.: Ontology based access to exploration data at statoil. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9367, pp. 93–112. Springer, Cham (2015). Scholar
  21. 21.
    Kharlamov, E., et al.: Ontology based data access in statoil. J. Web Sem. 44, 3–36 (2017)CrossRefGoogle Scholar
  22. 22.
    Kharlamov, E., et al.: Semantic access to streaming and static data at siemens. J. Web Sem. 44, 54–74 (2017)CrossRefGoogle Scholar
  23. 23.
    Kharlamov, E., et al.: A semantic approach to polystores. In: IEEE BigData, pp. 2565–2573 (2016)Google Scholar
  24. 24.
    Motik, B., Nenov, Y., Piro, R., Horrocks, I., Olteanu, D.: Parallel materialisation of datalog programs in centralised, main-memory RDF systems. In: AAAI, pp. 129–137 (2014)Google Scholar
  25. 25.
    Pérez-Urbina, H., Rodríguez-Díaz, E., Grove, M., Konstantinidis, G., Sirin, E.: Evaluation of query rewriting approaches for OWL 2. In: Proceedings of SSWS+HPCSW (2012)Google Scholar
  26. 26.
    Russell, A., Smart, P.: NITELIGHT: a graphical editor for SPARQL queries. In: ISWC (Posters and Demos) (2008)Google Scholar
  27. 27.
    Sherkhonov, E., Grau, B.C., Kharlamov, E., Kostylev, E.V.: Semantic faceted search with aggregation and recursion. In: d’Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10587, pp. 594–610. Springer, Cham (2017). Scholar
  28. 28.
    Soylu, A., Giese, M., Jimenez-Ruiz, E., Kharlamov, E., Zheleznyakov, D., Horrocks, I.: Ontology-based end-user visual query formulation: why, what, who, how, and which? Univ. Access Inf. Soc. 16(2), 435–467 (2017)CrossRefGoogle Scholar
  29. 29.
    Soylu, A., Giese, M., Jimenez-Ruiz, E., Vega-Gorgojo, G., Horrocks, I.: Experiencing OptiqueVQS: a multi-paradigm and ontology-based visual query system for end users. Univ. Access Inf. Soc. 15(1), 129–152 (2016)CrossRefGoogle Scholar
  30. 30.
    Soylu, A., et al.: OptiqueVQS: a visual query system over ontologies for industry. Semant. Web 9(5), 627–660 (2018)CrossRefGoogle Scholar
  31. 31.
    Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: WWW, pp. 697–706 (2007)Google Scholar
  32. 32.
    Tunkelang, D.: Faceted Search. Synthesis Lectures on Information Concepts, Retrieval, and Services. Morgan & Claypool Publishers, San Rafael (2009)Google Scholar
  33. 33.
    Wagner, A., Ladwig, G., Tran, T.: Browsing-oriented semantic faceted search. In: Hameurlain, A., Liddle, S.W., Schewe, K.-D., Zhou, X. (eds.) DEXA 2011. LNCS, vol. 6860, pp. 303–319. Springer, Heidelberg (2011). Scholar
  34. 34.
    Yamada, N., Yamagata, Y., Fukuta, N.: Query rewriting or ontology modification? Toward a faster approximate reasoning on LOD endpoints. IEICE Trans. Inf. Syst. E100–D(12), 2923–2930 (2017)CrossRefGoogle Scholar
  35. 35.
    Zhou, Q., Wang, C., Xiong, M., Wang, H., Yu, Y.: SPARK: adapting keyword query to semantic search. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 694–707. Springer, Heidelberg (2007). Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Vidar Klungre
    • 1
    Email author
  • Ahmet Soylu
    • 2
    • 3
  • Martin Giese
    • 1
  • Arild Waaler
    • 1
  • Evgeny Kharlamov
    • 1
    • 4
  1. 1.University of OsloOsloNorway
  2. 2.Norwegian University of Science and TechnologyGøvikNorway
  3. 3.SINTEF DigitalOsloNorway
  4. 4.University of OxfordOxfordUK

Personalised recommendations