Query Extension Suggestions for Visual Query Systems Through Ontology Projection and Indexing

Abstract

Ontology-based visual query formulation is a viable alternative to textual query editors in the Semantic Web domain for extracting data from structured data sources in terms of the skills and knowledge required. A visual query system is at any moment responsible for providing the user with query extension suggestions; however, suggestions leading to empty results are often not useful. To this end, in this article, we first present an approach for projecting OWL 2 ontologies into navigation graphs to be used for query formulation and then a solution where an efficient finite index is used to calculate non-ranked approximated extension suggestions for ontology-based visual query systems using navigation graphs. The results of our experiments suggest that one can efficiently project an ontology into a navigation graph, query it for running an interactive user interface, and suggest query extensions that do not lead to dead-ends.

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Notes

  1. 1.

    https://lucene.apache.org.

  2. 2.

    http://sphinxsearch.com.

  3. 3.

    http://pricespy.co.uk.

  4. 4.

    https://gitlab.com/ernesto.jimenez.ruiz/OptiqueVQS.

  5. 5.

    https://github.com/OxfordSemTech/SemFacet.

  6. 6.

    https://github.com/Sirius-sfi/geoscience-image-classification.

  7. 7.

    http://www.hermit-reasoner.com/.

  8. 8.

    https://gitlab.com/ernesto.jimenez.ruiz/ontology-services-toolkit/tree/master.

  9. 9.

    https://bioportal.bioontology.org/ontologies/TMO.

  10. 10.

    https://gitlab.com/logid/npd-factpages/blob/develop/ontology/npd-db.ttl.owl.

  11. 11.

    https://bioportal.bioontology.org/ontologies/MI.

  12. 12.

    https://bioportal.bioontology.org/ontologies/IDOMAL.

  13. 13.

    https://bioportal.bioontology.org/ontologies/ENM.

  14. 14.

    https://gitlab.com/logid/npd-factpages.

  15. 15.

    https://github.com/Alopex8064/npd-factpages-experiments.

  16. 16.

    https://github.com/Alopex8064/npd-factpages-experiments.

  17. 17.

    There are two queries pointing towards (1.0, 110.5) and one pointing towards (1.0, 39.3).

  18. 18.

    https://www.ebay.com/.

  19. 19.

    https://pricespy.co.uk/.

  20. 20.

    http://lucene.apache.org/solr/.

  21. 21.

    https://www.elastic.co/.

References

  1. 1.

    Arenas, M., Grau, B.C., Kharlamov, E., Marciuska, S., Zheleznyakov, D.: Faceted search over RDF-based knowledge graphs. J. Web Semant. 37–38, 55–74 (2016). https://doi.org/10.1016/j.websem.2015.12.002

    Article  Google Scholar 

  2. 2.

    Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, New York, NY, USA (2003)

    Google Scholar 

  3. 3.

    Brunetti, J.M., García, R., Auer, S.: From overview to facets and pivoting for interactive exploration of semantic web data. Int. J. Semant. Web Inf. Syst. 9(1), 1–20 (2013). https://doi.org/10.4018/jswis.2013010101

    Article  Google Scholar 

  4. 4.

    Catarci, T.: What happened when database researchers met usability. Inf. Syst. 25(3), 177–212 (2000). https://doi.org/10.1016/S0306-4379(00)00015-6

    Article  Google Scholar 

  5. 5.

    Catarci, T., Costabile, M.F., Levialdi, S., Batini, C.: Visual query systems for databases: a survey. J. Vis. Lang. Comput. 8(2), 215–260 (1997). https://doi.org/10.1006/jvlc.1997.0037

    Article  Google Scholar 

  6. 6.

    Grau, B.C., Giese, M., Horrocks, I., Hubauer, T., Jiménez-Ruiz, E., Kharlamov, E., Schmidt, M., Soylu, A., Zheleznyakov, D.: Towards query formulation, query-driven ontology extensions in OBDA systems. In: Proceedings of the 10th International Workshop on OWL: Experiences and Directions (OWLED 2013), CEUR Workshop Proceedings, vol. 1080. CEUR-WS.org (2013)

  7. 7.

    Heim, P., Ertl, T., Ziegler, J.: Facet Graphs: Complex semantic querying made easy. In: Proceedings of the 7th Extended Semantic Web Conference (ESWC 2010), LNCS, vol. 6088, pp. 288–302. Springer (2010). https://doi.org/10.1007/978-3-642-13486-9_20

    Google Scholar 

  8. 8.

    Katifori, A., Halatsis, C., Lepouras, G., Vassilakis, C., Giannopoulou, E.G.: Ontology visualization methods—a survey. ACM Comput. Surv. 39(4), 1 (2007). https://doi.org/10.1145/1287620.1287621

    Article  Google Scholar 

  9. 9.

    Kharlamov, E., Hovland, D., Skjæveland, M.G., Bilidas, D., Jiménez-Ruiz, E., Xiao, G., Soylu, A., Lanti, D., Rezk, M., Zheleznyakov, D., Giese, M., Lie, H., Ioannidis, Y.E., Kotidis, Y., Koubarakis, M., Waaler, A.: Ontology based data access in statoil. J. Web Semant. 44, 3–36 (2017). https://doi.org/10.1016/j.websem.2017.05.005

    Article  Google Scholar 

  10. 10.

    Kharlamov, E., Mailis, T., Mehdi, G., Neuenstadt, C., Özçep, Ö.L., Roshchin, M., Solomakhina, N., Soylu, A., Svingos, C., Brandt, S., Giese, M., Ioannidis, Y.E., Lamparter, S., Möller, R., Kotidis, Y., Waaler, A.: Semantic access to streaming and static data at Siemens. J. Web Semant. 44, 54–74 (2017)

    Article  Google Scholar 

  11. 11.

    Klungre, V., Giese, M.: Evaluating a faceted search index for graph data. In: Proceedings of the On the Move to Meaningful Internet Systems (OTM 2018). LNCS, vol. 11230, pp. 573–583 (2018). https://doi.org/10.1007/978-3-030-02671-4_36

    Google Scholar 

  12. 12.

    Klungre, V., Soylu, A., Giese, M., Waaler, A., Kharlamov, E.: On enhancing visual query building over KGs using query logs. In: The Proceedings of the 8th Joint International Conference on Semantic Technology (JIST 2018), LNCS, vol. 11341, pp. 77–85. Springer (2018). https://doi.org/10.1007/978-3-030-04284-4_6

    Google Scholar 

  13. 13.

    Kogalovsky, M.R.: Ontology-based data access systems. Program. Comput. Softw. 38(4), 167–182 (2012)

    MathSciNet  Article  Google Scholar 

  14. 14.

    Krivov, S., Williams, R., Villa, F.: GrOWL: a tool for visualization and editing of OWL ontologies. J. Web Semant. 5(2), 54–57 (2007). https://doi.org/10.1016/j.websem.2007.03.005

    Article  Google Scholar 

  15. 15.

    Lohmann, S., Negru, S., Haag, F., Ertl, T.: Visualizing ontologies with VOWL. Semant. Web 7(4), 399–419 (2016). https://doi.org/10.3233/SW-150200

    Article  Google Scholar 

  16. 16.

    Motta, E., Mulholland, P., Peroni, S., d’Aquin, M., Gomez-Perez, J.M., Mendez, V., Zablith, F.: A Novel approach to visualizing and navigating ontologies. In: Proceedings of the 10th International Conference on The Semantic Web (ISWC 2011), LNCS, vol. 7031, pp. 470–486. Springer (2011). https://doi.org/10.1007/978-3-642-25073-6_30

    Google Scholar 

  17. 17.

    Nielsen, J.: Usability Engineering. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1993)

    Google Scholar 

  18. 18.

    Pattyn, F., Vermaere, S., Van Huffel, P., Knecht, K., Constandt, H.: Semantic linking and integration of researchers and research organizations in DISQOVER. In: Proceedings of the 9th International Conference Semantic Web Applications and Tools for Life Sciences (SWAT4LS 2016), CEUR Workshop Proceedings, vol. 1795. CEUR-WS.org (2016)

  19. 19.

    Picalausa, F., Vansummeren, S.: What are real SPARQL queries like? In: Proceedings of the International Workshop on Semantic Web Information Management (SWIM 2011), pp. 7:1–7:6. ACM (2011)

  20. 20.

    Sarker, M.K., Krisnadhi, A.A., Hitzler, P.: OWLAx: A protege plugin to support ontology axiomatization through diagramming. In: Proceedings of the Posters and Demonstrations Track co-located with 15th International Semantic Web Conference (ISWC 2016), CEUR Workshop Proceedings, vol. 1690. CEUR-WS.org (2016)

  21. 21.

    Smart, P.R., Russell, A., Braines, D., Kalfoglou, Y., Bao, J., Shadbolt, N.R.: A visual approach to semantic query design using a web-based graphical query designer. In: Proceedings of the 16th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2008), LNAI, vol. 5268, pp. 275–291. Springer (2008). https://doi.org/10.1007/978-3-540-87696-0_25

  22. 22.

    Soylu, A., Giese, M., Jiménez-Ruiz, E., Kharlamov, E., Zheleznyakov, D., Horrocks, I.: Towards exploiting query history for adaptive ontology-based visual query formulation. In: Proceedings of the 8th Metadata and Semantics Research Conference (MTSR 2014), CCIS, vol. 478, pp. 107–119. Springer (2014)

  23. 23.

    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). https://doi.org/10.1007/s10209-016-0465-0

    Article  Google Scholar 

  24. 24.

    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). https://doi.org/10.1007/s10209-015-0404-5

    Article  Google Scholar 

  25. 25.

    Soylu, A., Giese, M., Schlatte, R., Jiménez-Ruiz, E., Kharlamov, E., Özçep, Ö.L., Neuenstadt, C., Brandt, S.: Querying industrial stream-temporal data: an ontology-based visual approach. J. Ambient Intell. Smart Environ. 9(1), 77–95 (2017). https://doi.org/10.3233/AIS-160415

    Article  Google Scholar 

  26. 26.

    Soylu, A., Kharlamov, E.: Making complex ontologies end user accessible via ontology projections. In: Proceedings of the 8th Joint International Conference on Semantic Technology (JIST 2018), LNCS, vol. 11341, pp. 295–303. Springer (2018). https://doi.org/10.1007/978-3-030-04284-4_20

    Google Scholar 

  27. 27.

    Soylu, A., Kharlamov, E., Zheleznyakov, D., Jimenez Ruiz, E., Giese, M., Skjaeveland, M.G., Hovland, D., Schlatte, R., Brandt, S., Lie, H., Horrocks, I.: Optique VQS: a Visual query system over ontologies for industry. Semant. Web 9(5), 627–660 (2018). https://doi.org/10.3233/SW-180293

    Article  Google Scholar 

  28. 28.

    Soylu, A., Modritscher, F., De Causmaecker, P.: Ubiquitous web navigation through harvesting embedded semantic data: a mobile scenario. Integr. Comput. Aid. Eng. 19(1), 93–109 (2012)

    Article  Google Scholar 

  29. 29.

    Spanos, D.E., Stavrou, P., Mitrou, N.: Bringing relational databases into the semantic web: a survey. Semant. Web 3(2), 169–209 (2012)

    Google Scholar 

  30. 30.

    Tunkelang, D.: Faceted Search. Synthesis Lectures on Information Concepts, Retrieval, and Services. Morgan & Claypool Publishers, San Rafael (2009)

    Google Scholar 

  31. 31.

    Vega-Gorgojo, G., Giese, M., Heggestøyl, S., Soylu, A., Waaler, A.: PepeSearch: semantic data for the masses. PLoS One 11(3), 1 (2016). https://doi.org/10.1371/journal.pone.0151573

    Article  Google Scholar 

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Acknowledgements

This project is partly funded by the Center for Scalable Data Access in the Oil and Gas Domain (SIRIUS).

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Correspondence to Ahmet Soylu.

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Klungre, V.N., Soylu, A., Jimenez-Ruiz, E. et al. Query Extension Suggestions for Visual Query Systems Through Ontology Projection and Indexing. New Gener. Comput. 37, 361–392 (2019). https://doi.org/10.1007/s00354-019-00071-1

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Keywords

  • Visual query system
  • Ontology projection
  • Query extensions
  • Indexing