Advertisement

Qualifying Ontology-Based Visual Query Formulation

  • Ahmet Soylu
  • Martin Giese
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 400)

Abstract

This paper elaborates on ontology-based end-user visual query formulation, particularly for users who otherwise cannot/do not desire to use formal textual query languages to retrieve data due to the lack of technical knowledge and skills. Then, it provides a set of quality attributes and features, primarily elicited via a series of industrial end-user workshops and user studies carried out in the course of an industrial EU project, to guide the design and development of successor visual query systems.

Keywords

Visual query formulation Ontologies Data retrieval End-user programming Usability 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Arenas, M., et al.: Faceted search over ontology-enhanced RDF data. In: CIKM 2014 (2014)Google Scholar
  2. 2.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison Wesley (1999)Google Scholar
  3. 3.
    Bobed, C., et al.: Enabling keyword search on Linked Data repositories: An ontology-based approach. International Journal of Knowledge-Based and Intelligent Engineering Systems 17(1) (2013)Google Scholar
  4. 4.
    Brooke, J.: Usability evaluation in industry, chap. SUS - A quick and dirty usability scale. Taylor and Francis (1996)Google Scholar
  5. 5.
    Brunetti, J.M., et al.: From overview to facets and pivoting for interactive exploration of semantic web data. International Journal on Semantic Web and Information Systems 9(1) (2013)Google Scholar
  6. 6.
    Brunk, S., Heim, P.: tFacet: hierarchical faceted exploration of semantic data using well-known interaction concepts. In: DCI 2011 (2011)Google Scholar
  7. 7.
    Burnett, M.M.: Visual programming. In: Webster, J.G. (ed.) Wiley Encyclopedia of Electrical and Electronics Engineering. John Wiley & Sons (1999)Google Scholar
  8. 8.
    Catarci, T., et al.: Visual query systems for databases: A survey. Journal of Visual Languages and Computing 8(2) (1997)Google Scholar
  9. 9.
    Catarci, T., et al.: An ontology based visual tool for query formulation support. In: ECAI 2004 (2004)Google Scholar
  10. 10.
    Grau, B.C., et al.: Towards query formulation and query-driven ontology extensions in OBDA systems. In: OWLED 2013 (2013)Google Scholar
  11. 11.
    Damljanovic, D., et al.: Improving habitability of natural language interfaces for querying ontologies with feedback and clarification dialogues. Web Semantics: Science, Services and Agents on the World Wide Web 19, (2013)Google Scholar
  12. 12.
    Dividino, R., Groner, G.: Which of the following SPARQL queries are similar? why? In: LD4IE 2013 (2013)Google Scholar
  13. 13.
    Giese, M., et al.: Scalable end-user access to big data. In: Rajendra, A. (ed.) Big Data Computing. CRC (2013)Google Scholar
  14. 14.
    Giese, M., et al.: Optique - Zooming In on Big Data Access. IEEE Computer 48(3) (2015)Google Scholar
  15. 15.
    Haag, F., et al.: Visual SPARQL querying based on extended filter/flow graphs. In: AVI 2014 (2014)Google Scholar
  16. 16.
    Harth, A.: VisiNav: A system for visual search and navigation on web data. Web Semantics: Science, Services and Agents on the World Wide Web 8(4) (2010)Google Scholar
  17. 17.
    Harth, A., et al.: Graphical representation of RDF queries. In: WWW 2006 (2006)Google Scholar
  18. 18.
    Katifori, A., et al.: Ontology visualization methods - A survey. ACM Computing Surveys 39(4) (2007)Google Scholar
  19. 19.
    Kaufmann, E., Bernstein, A.: Evaluating the usability of natural language query languages and interfaces to Semantic Web knowledge bases. Web Semantics: Science, Services and Agents on the World Wide Web 8(4) (2010)Google Scholar
  20. 20.
    Kawash, J.: Complex Quantification in Structured Query Language (SQL): A Tutorial Using Relational Calculus. Journal of Computers in Mathematics and Science Teaching 23(2) (2004)Google Scholar
  21. 21.
    Kogalovsky, M.R.: Ontology-Based Data Access Systems. Programming and Computer Software 38(4) (2012)Google Scholar
  22. 22.
    Lieberman, H., et al.: End-user development: an emerging paradigm. In: Lieberman, H., Paternó, F., Wulf, V. (eds.) End-User Development. Springer, Netherlands (2006)CrossRefGoogle Scholar
  23. 23.
    Marchionini, G., White, R.: Find what you need, understand what you find. International Journal of Human-Computer Interaction 23(3) (2007)Google Scholar
  24. 24.
    Popov, I.O., Schraefel, M.C., Hall, W., Shadbolt, N.: Connecting the dots: a multi-pivot approach to data exploration. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 553–568. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  25. 25.
    Rodriguez-Muro, M., Calvanese, D.: Quest, a system for ontology based data access. In: OWLED 2012 (2012)Google Scholar
  26. 26.
    Ruiz, F., Hilera, J.R.: Using ontologies in software engineering and technology. In: Calero, C., Ruiz, F., Piattini, M. (eds.) Ontologies for Software Engineering and Software Technology. Springer-Verlag (2006)Google Scholar
  27. 27.
    Schraefel, M.C., et al.: mSpace: improving information access to multimedia domains with multimodal exploratory search. Communications of the ACM 49(4) (2006)Google Scholar
  28. 28.
    Shneiderman, B.: Direct Manipulation: A Step Beyond Programming Languages. Computer 16(8) (1983)Google Scholar
  29. 29.
    Siau, K.L., et al.: Effects of query complexity and learning on novice user query performance with conceptual and logical database interfaces. IEEE Transactions on Systems, Man and Cybernetics - Part A: Systems and Humans 34(2) (2004)Google Scholar
  30. 30.
    Soylu, A., et al.: OptiqueVQS - towards an ontology-based visual query system for big data. In: MEDES 2013 (2013)Google Scholar
  31. 31.
    Soylu, A., Giese, M., Jimenez-Ruiz, E., Kharlamov, E., Zheleznyakov, D., Horrocks, I.: Towards exploiting query history for adaptive ontology-based visual query formulation. In: Closs, S., Studer, R., Garoufallou, E., Sicilia, M.-A. (eds.) MTSR 2014. CCIS, vol. 478, pp. 107–119. Springer, Heidelberg (2014) Google Scholar
  32. 32.
    Soylu, A., et al.: Experiencing OptiqueVQS: A Multi-paradigm and Ontology-based Visual Query System for End Users. Universal Access in the Information Society (2015) (in press)Google Scholar
  33. 33.
    Spanos, D.E., et al.: Bringing relational databases into the Semantic Web: A survey. Semantic Web 3(2) (2012)Google Scholar
  34. 34.
    Sutcliffe, A.: Evaluating the Costs and Benefits of End-user Development. ACM SIGSOFT Software Engineering Notes 30(4), 1–4 (2005)Google Scholar
  35. 35.
    Tran, T., et al.: SemSearchPro - Using semantics throughout the search process. Web Semantics: Science, Services and Agents on the World Wide Web 9(4) (2011)Google Scholar
  36. 36.
    Yen, M.Y.M., Scamell, R.W.: A Human Factors Experimental Comparison of SQL and QBE. IEEE Transactions on Software Engineering 19(4) (1993)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of InformaticsUniversity of OsloOsloNorway
  2. 2.Faculty of Informatics and Media TechnologyGjøVik University CollegeGjøVikNorway

Personalised recommendations