Universal Access in the Information Society

, Volume 15, Issue 1, pp 129–152 | Cite as

Experiencing OptiqueVQS: a multi-paradigm and ontology-based visual query system for end users

  • Ahmet Soylu
  • Martin Giese
  • Ernesto Jimenez-Ruiz
  • Guillermo Vega-Gorgojo
  • Ian Horrocks
Long paper


Data access in an enterprise setting is a determining factor for value creation processes, such as sense-making, decision-making, and intelligence analysis. Particularly, in an enterprise setting, intuitive data access tools that directly engage domain experts with data could substantially increase competitiveness and profitability. In this respect, the use of ontologies as a natural communication medium between end users and computers has emerged as a prominent approach. To this end, this article introduces a novel ontology-based visual query system, named OptiqueVQS, for end users. OptiqueVQS is built on a powerful and scalable data access platform and has a user-centric design supported by a widget-based flexible and extensible architecture allowing multiple coordinated representation and interaction paradigms to be employed. The results of a usability experiment performed with non-expert users suggest that OptiqueVQS provides a decent level of expressivity and high usability and hence is quite promising.


Visual query formulation Visual query systems Ontology-based data access Data retrieval 



This research is funded by the Seventh Framework Programme (FP7) of the European Commission under Grant Agreement 318338, “Optique”. Ian Horroks and Ernesto Jimenez-Ruiz were also supported by the EPSRC projects MaSI3, Score! and DBOnto.


  1. 1.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison Wesley, Reading (1999)Google Scholar
  2. 2.
    Bagosi, T., Calvanese, D., Hardi, J., Komla-Ebri, S., Lanti, D., Rezk, M., Rodriguez-Muro, M., Slusnys, M., Xiao, G.: The ontop framework for ontology based data access. In: Proceedings of the 8th Chinese Semantic Web Symposium and Web Science Conference (CSWS 2014), CCIS, vol. 480, pp. 67–77. Springer (2014). doi: 10.1007/978-3-662-45495-4_6
  3. 3.
    Barzdins, G., Liepins, E., Veilande, M., Zviedris, M.: Ontology enabled graphical database query tool for end-users. In: Proceedings of the 8th International Baltic Conference on Databases and Information Systems (DB&IS 2008), Frontiers in Artificial Intelligence and Applications, vol. 187, pp. 105–116. IOS Press (2009). doi: 10.3233/978-1-58603-939-4-105
  4. 4.
    Bechhofer, S., Stevens, R., Ng, G., Jacoby, A., Goble, C.: Guiding the user: an ontology driven interface. In: Proceedings of the User Interfaces to Data Intensive Systems, pp. 158–161. IEEE Computer Society (1999). doi: 10.1109/UIDIS.1999.791472
  5. 5.
    Berners-Lee, T., Chen, Y., Chilton, L., Connolly, D., Dhanaraj, R., Hollenbach, J., Lerer, A., Sheets, D.: Tabulator: exploring and analyzing linked data on the semantic web. In: Proceedings of the 3rd International Semantic Web User Interaction Workshop (SWUI 2006) (2006)Google Scholar
  6. 6.
    Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web—a new form of web content that is meaningful to computers will unleash a revolution of new possibilities. Sci. Am. 284(5), 34–43 (2001)CrossRefGoogle Scholar
  7. 7.
    Bevan, N., Macleod, M.: Usability measurement in context. Behav. Inf. Technol. 13(1–2), 132–145 (1994). doi: 10.1080/01449299408914592 CrossRefGoogle Scholar
  8. 8.
    Bizer, C., Heath, T., Berners-Lee, T.: Linked data—the story so far. Int. J. Semant. Web Inf. Syst. 5(3), 1–22 (2009). doi: 10.4018/jswis.2009081901 CrossRefGoogle Scholar
  9. 9.
    Bobed, C., Esteban, G., Mena, E.: Enabling keyword search on linked data repositories: an ontology-based approach. Int. J. Knowl. Based Intell. Eng. Syst. 17(1), 67–77 (2013). doi: 10.3233/KES-130255 Google Scholar
  10. 10.
    Brunk, S., Heim, P.: tFacet: hierarchical faceted exploration of semantic data using well-known interaction concepts. In: Proceedings of the International Workshop on Data-Centric Interactions on the Web (DCI 2011), CEUR Workshop Proceedings, vol. 817, pp. 31–36. (2011)Google Scholar
  11. 11.
    Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.): The Adaptive Web: Methods and Strategies of Web Personalization. Springer, Berlin (2007)Google Scholar
  12. 12.
    Burnett, M.M.: Visual programming. In: Webster, J.G. (ed.) Wiley Encyclopedia of Electrical and Electronics Engineering. Wiley, New York (1999). doi: 10.1002/047134608X.W1707 Google Scholar
  13. 13.
    Burnett, M.M., Baker, M.J.: A classification system for visual programming languages. J. Vis. Lang. Comput. 5(3), 287–300 (1994). doi: 10.1006/jvlc.1994.1015 CrossRefGoogle Scholar
  14. 14.
    Cáceres, M.: Packaged web apps (Widgets)—packaging and XML configuration, 2nd edn. W3C Recommendation, W3C (2012).
  15. 15.
    Campinas, S., Perry, T.E., Ceccarelli, D., Delbru, R., Tummarello, G.: Introducing RDF graph summary with application to assisted SPARQL formulation. In: Proceedings of the 23rd International Workshop on Database and Expert Systems Applications (DEXA 2012), pp. 261–266. IEEE Computer Society (2012). doi: 10.1109/DEXA.2012.38
  16. 16.
    Catarci, T.: What happened when database researchers met usability. Inf. Syst. 25(3), 177–212 (2000). doi: 10.1016/S0306-4379(00)00015-6 CrossRefGoogle Scholar
  17. 17.
    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). doi: 10.1006/jvlc.1997.0037 CrossRefGoogle Scholar
  18. 18.
    Catarci, T., Dongilli, P., Di Mascio, T., Franconi, E., Santucci, G., Tessaris, S.: An ontology based visual tool for query formulation support. In: Proceedings of the 16th European Conference on Artificial Intelligence (ECAI 2004), Frontiers in Artificial Intelligence and Applications, vol. 110, pp. 308–312. IOS Press (2004)Google Scholar
  19. 19.
    Claussen, J., Kemper, A., Moerkotte, G., Peithner, K., Steinbrunn, M.: Optimization and evaluation of disjunctive queries. IEEE Trans. Knowl. Data Eng. 12(2), 238–260 (2000). doi: 10.1109/69.842265 CrossRefGoogle Scholar
  20. 20.
    Console, M., Lenzerini, M., Mancini, R., Rosati, R., Ruzzi, M.: Synthesizing extensional constraints in ontology-based data access. In: Proceedings of the 26th International Workshop on Description Logics (DL 2013), CEUR Workshop Proceedings, vol. 1014, pp. 628–639. (2013)Google Scholar
  21. 21.
    Console, M., Mora, J., Rosati, R., Santarelli, V., Fabio Savo, D.: Effective computation of maximal sound approximations of description logic ontologies. In: Proceedings of the 13th International Semantic Web Conference (ISWC 2014), LNCS, vol. 8797, pp. 164–179. Springer (2014). doi: 10.1007/978-3-319-11915-1_11
  22. 22.
    Console, M., Santarelli, V., Savo, D.F.: Efficient approximation in DL-lite of OWL 2 ontologies. In: Proceedings of the 26th International Workshop on Description Logics (DL 2013), CEUR Workshop Proceedings, vol. 1014, pp. 132–143. (2013)Google Scholar
  23. 23.
    Coutaz, J., Crowley, J.L., Dobson, S., Garlan, D.: Context is key. Commun. ACM 48(3), 49–53 (2005). doi: 10.1145/1047671.1047703 CrossRefGoogle Scholar
  24. 24.
    Crompton, J.: Keynote talk, the W3C workshop on semantic web in oil & gas industry. Houston, TX, USA, 9–10 Dec (2008).
  25. 25.
    Damljanovic, D., Agatonovic, M., Cunningham, H., Bontcheva, K.: Improving habitability of natural language interfaces for querying ontologies with feedback and clarification dialogues. Web Semant. Sci. Serv. Agents World Wide Web 19, 1–21 (2013). doi: 10.1016/j.websem.2013.02.002 CrossRefGoogle Scholar
  26. 26.
    Dowse, R., Ehlers, M.: Medicine labels incorporating pictograms: Do they influence understanding and adherence? Patient Educ. Couns. 58, 63–70 (2005). doi: 10.1016/j.pec.2004.06.012 CrossRefGoogle Scholar
  27. 27.
    Epstein, R.G.: The TableTalk query language. J. Vis. Lang. Comput. 2(2), 115–141 (1991). doi: 10.1016/S1045-926X(05)80026-6 CrossRefGoogle Scholar
  28. 28.
    Erwig, M.: Xing: a visual XML query language. J. Vis. Lang. Comput. 14(1), 5–45 (2003). doi: 10.1016/S1045-926X(02)00074-5 CrossRefGoogle Scholar
  29. 29.
    Fadhil, A., Haarslev, V.: GLOO: a graphical query language for OWL ontologies. In: Proceedings of the OWL: Experiences and Directions (OWLED 2006), CEUR Workshop Proceedings, vol. 216. (2006)Google Scholar
  30. 30.
    Ferre, X., Juristo, N., Windl, H., Constantine, L.: Usability basics for software developers. IEEE Softw. 18(1), 22–29 (2001). doi: 10.1109/52.903160 CrossRefGoogle Scholar
  31. 31.
    Gaines, B.R.: Designing visual languages for description logics. J. Log. Lang. Inf. 18(2), 217–250 (2009). doi: 10.1007/s10849-008-9078-1 CrossRefGoogle Scholar
  32. 32.
    Gallud, J.A., Lozano, M.D., Vanderdonckt, J.: Distributed user interfaces: usability and collaboration. Int. J. Hum. Comput. Stud. 72(1), 44 (2014). doi: 10.1016/j.ijhcs.2013.10.006 CrossRefGoogle Scholar
  33. 33.
    Giese, M., Calvanese, D., Horrocks, I., Ioannidis, Y., Klappi, H., Koubarakis, M., Lenzerini, M., Moller, R., Ozcep, O., Rodriguez Muro, M., Rosati, R., Schlatte, R., Soylu, A., Waaler, A.: Scalable end-user access to big data. In: Rajendra, A. (ed.) Big Data Computing. Chapman and Hall/CRC, London (2013)Google Scholar
  34. 34.
    Glimm, B., Horrocks, I., Lutz, C., Sattler, U.: Conjunctive query answering for the description logic SHIQ. J. Artif. Intell. Res. 31(1), 157–204 (2008)MathSciNetzbMATHGoogle Scholar
  35. 35.
    Grau, B.C., Giese, M., Horrocks, I., Hubauer, T., Jimenez-Ruiz, E., Kharlamov, E., Schmidt, M., Soylu, A., Zheleznyakov, D.: Towards query formulation and query-driven ontology extensions in OBDA systems. In: Proceedings of 10th OWL: Experiences and Directions Workshop (OWLED 2013), CEUR Workshop Proceedings, vol. 1080. (2013)Google Scholar
  36. 36.
    Grau, B.C., Horrocks, I., Motik, B., Parsia, B., Patel-Schneider, P., Sattler, U.: OWL 2: the next step for OWL. Web Semant. Sci. Serv. Agents World Wide Web 6(4), 309–322 (2008). doi: 10.1016/j.websem.2008.05.001 CrossRefGoogle Scholar
  37. 37.
    Gulliksen, J., Goransson, B., Boivie, I., Blomkvist, S., Persson, J., Cajander, A.: Key principles for user-centred systems design. Behav. Inf. Technol. 22(6), 397–409 (2003). doi: 10.1080/01449290310001624329 CrossRefGoogle Scholar
  38. 38.
    Haase, P., Schmidt, M., Schwarte, A.: The information workbench as a self-service platform for linked data applications. In: Proceedings of the 2nd International Workshop on Consuming Linked Data (COLD 2011), CEUR Workshop Proceedings, vol. 782. (2011)Google Scholar
  39. 39.
    Harris, S., Seaborne, A.: SPARQL 1.1 query language. W3C Recommendation, W3C (2013).
  40. 40.
    Harth, A.: VisiNav: a system for visual search and navigation on web data. Web Semant. Sci. Serv. Agents World Wide Web 8(4), 348–354 (2010). doi: 10.1016/j.websem.2010.08.001 CrossRefGoogle Scholar
  41. 41.
    Heim, P., Ziegler, J.: Faceted visual exploration of semantic data. In: Proceedings of the 2nd IFIP WG 13.7 Conference on Human–Computer Interaction and Visualization (HCIV 2009), LNCS, vol. 6431, pp. 58–75. Springer (2011). doi: 10.1007/978-3-642-19641-6_5
  42. 42.
    Henderson-Sellers, B.: Bridging metamodels and ontologies in software engineering. J. Syst. Softw. 84(2), 301–313 (2011). doi: 10.1016/j.jss.2010.10.025 CrossRefGoogle Scholar
  43. 43.
    Hogenboom, F., Milea, V., Frasincar, F., Kaymak, U.: RDF-GL: a SPARQL-based graphical query language for RDF. In: Chbeir, R., Badr, Y., Abraham, A., Hassanien A.E. (eds.) Emergent Web Intelligence: Advanced Information Retrieval, Advanced Information and Knowledge Processing, pp. 87–116. Springer (2010). doi: 10.1007/978-1-84996-074-8_4
  44. 44.
    Holcomb, P.J., Grainger, J.: On the time course of visual word recognition: an event-related potential investigation using masked repetition priming. J. Cogn. Neurosci. 18(10), 1631–1643 (2006)CrossRefGoogle Scholar
  45. 45.
    Huynh, D.F., Karger, D.R.: Parallax and companion: set-based browsing for the data web (2009).
  46. 46.
    Huynh, D.F., Karger, D.R., Miller, R.C.: Exhibit: lightweight structured data publishing. In: Proceedings of the 16th International Conference on World Wide Web (WWW 2007), pp. 737–746. ACM (2007). doi: 10.1145/1242572.1242672
  47. 47.
    Katifori, A., Halatsis, C., Lepouras, G., Vassilakis, C., Giannopoulou, E.: Ontology visualization methods—a survey. ACM Comput. Surv. 39(4), 10:1–10:43 (2007). doi: 10.1145/1287620.1287621 CrossRefGoogle Scholar
  48. 48.
    Kaufmann, E., Bernstein, A.: Evaluating the usability of natural language query languages and interfaces to semantic web knowledge bases. Web Semant. Sci. Serv. Agents World Wide Web 8(4), 377–393 (2010). doi: 10.1016/j.websem.2010.06.001 CrossRefGoogle Scholar
  49. 49.
    Kawash, J.: Complex quantification in structured query language (SQL): a tutorial using relational calculus. J. Comput. Math. Sci. Teach. 23(2), 169–190 (2004)Google Scholar
  50. 50.
    Kharlamov, E., Jiménez-Ruiz, E., Zheleznyakov, D., Bilidas, D., Giese, M., Haase, P., Horrocks, I., Kllapi, H., Koubarakis, M., Özçep, O., Rodríguez-Muro, M., Rosati, R., Schmidt, M., Schlatte, R., Soylu, A., Waaler, A.: Optique: towards OBDA systems for industry. In: Proceedings of the Semantic Web: ESWC 2013 Satellite Events, LNCS, vol. 7955, pp. 125–140. Springer (2013). doi: 10.1007/978-3-642-41242-4_11
  51. 51.
    Khoussainova, N., Kwon, Y., Balazinska, M., Suciu, D.: SnipSuggest: context-aware autocompletion for SQL. Proc. VLDB Endow. 4(1), 22–33 (2010)CrossRefGoogle Scholar
  52. 52.
    Kllapi, H., Sitaridi, E., Tsangaris, M.M., Ioannidis, Y.: Schedule optimization for data processing flows on the cloud. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD 2011), pp. 289–300. ACM (2011). doi: 10.1145/1989323.1989355
  53. 53.
    Kobilarov, G., Dickinson, I.: Humboldt: exploring linked data. In: Proceedings of the Linked Data on the Web Workshop (2008)Google Scholar
  54. 54.
    Kogalovsky, M.R.: Ontology-based data access systems. Program. Comput. Softw. 38(4), 167–182 (2012). doi: 10.1134/S0361768812040032 CrossRefMathSciNetGoogle Scholar
  55. 55.
    Krivov, S., Williams, R., Villa, F.: GrOWL: a tool for visualization and editing of OWL ontologies. Web Semant. Sci. Serv. Agents World Wide Web 5(2), 54–57 (2007). doi: 10.1016/j.websem.2007.03.005 CrossRefGoogle Scholar
  56. 56.
    Levie, W.H., Lentz, R.: Effects of text illustrations: a review of research. Educ. Technol. Res. Dev. 30(4), 195–232 (1982). doi: 10.1007/BF02765184 Google Scholar
  57. 57.
    Lieberman, H., Paternó, F., Klann, M., Wulf, V.: End-user development: an emerging paradigm. In: Lieberman, H., Paternó, F., Wulf, V. (eds.) End-User Development, Human–Computer Interaction Series, vol. 9, pp. 1–8. Springer, Netherlands (2006). doi: 10.1007/1-4020-5386-X_1 Google Scholar
  58. 58.
    Lopez, V., Unger, C., Cimiano, P., Motta, E.: Evaluating question answering over linked data. Web Semant. Sci. Serv. Agents World Wide Web 21, 3–13 (2013). doi: 10.1016/j.websem.2013.05.006 CrossRefGoogle Scholar
  59. 59.
    Lopez-Veyna, J.I., Sosa-Sosa, V.J., Lopez-Arevalo, I.: KESOSD: keyword search over structured data. In: Proceedings of the Third International Workshop on Keyword Search on Structured Data (KEYS 2012), pp. 23–31. ACM (2012). doi: 10.1145/2254736.2254743
  60. 60.
    Marchionini, G., White, R.: Find what you need, understand what you find. Int. J. Hum. Comput. Interact. 23(3), 205–237 (2007). doi: 10.1080/10447310701702352 CrossRefGoogle Scholar
  61. 61.
    Martinez-Cruz, C., Blanco, I.J., Amparo Vila, M.: Ontologies versus relational databases: Are they so different? A comparison. Artif. Intell. Rev. 38(4), 271–290 (2012). doi: 10.1007/s10462-011-9251-9 CrossRefGoogle Scholar
  62. 62.
    Motik, B., Grau, B.C., Horrocks, I., Wu, Z., Fokoue, A., Lutz, C.: OWL 2 web ontology language profiles. W3C Recommendation, W3C (2009).
  63. 63.
    Munir, K., Odeh, M., McClatchey, R.: Ontology-driven relational query formulation using the semantic and assertional capabilities of OWL-DL. Knowl. Based Syst. 35, 144–159 (2012). doi: 10.1016/j.knosys.2012.04.020 CrossRefGoogle Scholar
  64. 64.
    Nunamaker, J.F., Briggs, R.O., de Vreede, G.J.: From information technology to value creation technology. In: Dickson, G.W., DeSanctis, G. (eds.) Information Technology and the Future Enterprise: New Models for Managers, pp. 102–124. Prentice-Hall, New York (2001)Google Scholar
  65. 65.
    Pan, J.Z., Thomas, E.: Approximating OWL-DL ontologies. In: Proceedings of the 22nd National Conference on Artificial Intelligence (AAAI 2007), pp. 1434–1439 (2007)Google Scholar
  66. 66.
    Popov, I.O., Schraefel, M.C., Hall, W., Shadbolt, N.: Connecting the dots: a multi-pivot approach to data exploration. In: Proceedings of the 10th International Semantic Web Conference (ISWC 2011), LNCS, vol. 7031, pp. 553–568. Springer (2011). doi: 10.1007/978-3-642-25073-6_35
  67. 67.
    Rodriguez-Muro, M., Calvanese, D.: High performance query answering over DL-lite ontologies. In: Proceedings of the Principles of Knowledge Representation and Reasoning (KR 2012), pp. 308–318. AAAI Press (2012)Google Scholar
  68. 68.
    Rodriguez-Muro, M., Calvanese, D.: Quest, a system for ontology based data access. In: Proceedings of the 9th OWL: Experiences and Directions Workshop (OWLED 2012), CEUR Workshop Proceedings, vol. 849. (2012)Google Scholar
  69. 69.
    Rodriguez-Muro, M., Rezk, M., Hardi, J., Slusnys, M., Bagosi, T., Calvanese, D.: Evaluating SPARQL-to-SQL translation in ontop. In: Proceedings of the 2nd International Workshop on OWL Reasoner Evaluation (ORE 2013), CEUR Workshop Proceedings, vol. 1015, pp. 94–100. (2013)Google Scholar
  70. 70.
    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, pp. 49–102. Springer, Berlin (2006). doi: 10.1007/3-540-34518-3_2 CrossRefGoogle Scholar
  71. 71.
    Schraefel, M.C., Wilson, M., Russell, A., Smith, D.A.: mSpace: improving information access to multimedia domains with multimodal exploratory search. Commun. ACM 49(4), 47–49 (2006). doi: 10.1145/1121949.1121980 CrossRefGoogle Scholar
  72. 72.
    Segev, A., Sheng, Q.Z.: Bootstrapping ontologies for web services. IEEE Trans. Serv. Comput. 5(1), 33–44 (2012). doi: 10.1109/TSC.2010.51 CrossRefGoogle Scholar
  73. 73.
    Shneiderman, B.: Direct manipulation: a step beyond programming languages. Computer 16(8), 57–69 (1983). doi: 10.1109/MC.1983.1654471 CrossRefGoogle Scholar
  74. 74.
    Siau, K.L., Chan, H.C., Wei, K.K.: Effects of query complexity and learning on novice user query performance with conceptual and logical database interfaces. IEEE Trans. Syst. Man Cybern. A Syst. Hum. 34(2), 276–281 (2004). doi: 10.1109/TSMCA.2003.820581 CrossRefGoogle Scholar
  75. 75.
    Smart, P.R., Russell, A., Braines, D., Kalfoglou, Y., Bao, J., Shadbolt, N.: A visual approach to semantic query design using a web-based graphical query designer. In: Proceedings of the 16th International Conference on Knowledge Engineering: Practice and Patterns (EKAW 2008), LNCS, vol. 5268, pp. 275–291. Springer (2008). doi: 10.1007/978-3-540-87696-0_25
  76. 76.
    Soylu, A., Giese, M., Jimenez-Ruiz, E., Kharlamov, E., Zheleznyakov, D., Horrocks, I.: OptiqueVQS—towards an ontology-based visual query system for big data. In: Proceedings of the International Conference on Management of Emergent Digital EcoSystems (MEDES 2013), pp. 119–126. ACM (2013). doi: 10.1145/2536146.2536149
  77. 77.
    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: Proceedings of the 8th International Conference on Metadata and Semantic Research (MTSR 2014), CCIS, vol. 478, pp. 107–119. Springer (2014). doi: 10.1007/978-3-319-13674-5_11
  78. 78.
    Soylu, A., Modritscher, F., De Causmaecker, P.: Ubiquitous web navigation through harvesting embedded semantic data: a mobile scenario. Integr. Comput. Aided Eng. 19(1), 93–109 (2012). doi: 10.3233/ICA-2012-0393 Google Scholar
  79. 79.
    Soylu, A., Moedritscher, F., Wild, F., De Causmaecker, P., Desmet, P.: Mashups by orchestration and widget-based personal environments: key challenges, solution strategies, and an application. Program Electron. Libr. Inf. Syst. 46(4), 383–428 (2012). doi: 10.1109/ICC.2010.5502398 CrossRefGoogle Scholar
  80. 80.
    Soylu, A., Skjæveland, M., Giese, M., Horrocks, I., Jimenez-Ruiz, E., Kharlamov, E., Zheleznyakov, D.: A preliminary approach on ontology-based visual query formulation for big data. In: Proceedings of the 7th International Conference on Metadata and Semantic Research (MTSR 2013), CCIS, vol. 390, pp. 201–212. Springer (2013). doi: 10.1007/978-3-319-03437-9_21
  81. 81.
    Spanos, D.E., Stavrou, P., Mitrou, N.: Bringing relational databases into the semantic web: a survey. Semant. Web 3(2), 169–209 (2012). doi: 10.3233/SW-2011-0055 Google Scholar
  82. 82.
    Spiekermann, S.: User Control in Ubiquitous Computing: Design Alternatives and User Acceptance. Shaker Verlag, Aachen (2008)Google Scholar
  83. 83.
    Staab, S., Studer, R. (eds.): Handbook on Ontologies. International Handbooks on Information Systems. Springer, Berlin (2009)Google Scholar
  84. 84.
    Studer, R., Benjamins, V.R., Fensel, D.: Knowledge engineering: principles and methods. Data Knowl. Eng. 25(1–2), 161–197 (1998). doi: 10.1016/S0169-023X(97)00056-6 CrossRefzbMATHGoogle Scholar
  85. 85.
    Suh, B., Bederson, B.B.: OZONE: a zoomable interface for navigating ontology information. In: Proceedings of the Working Conference on Advanced Visual Interfaces (AVI 2002), pp. 139–143. ACM (2002). doi: 10.1145/1556262.1556284
  86. 86.
    Ter Hofstede, A.H.M., Proper, H.A., Van Der Weide, T.P.: Query formulation as an information retrieval problem. Comput. J. 39(4), 255–274 (1996). doi: 10.1093/comjnl/39.4.255 CrossRefGoogle Scholar
  87. 87.
    Thorpe, S., Fize, D., Marlot, C.: Speed of processing in the human visual system. Nature 381, 520–522 (1996)CrossRefGoogle Scholar
  88. 88.
    Tunkelang, D., Marchionini, G.: Faceted Search. Synthesis Lectures on Information Concepts, Retrieval, and Services. Morgan and Claypool Publishers, UK (2009)Google Scholar
  89. 89.
    Turk, M., Robertson, G.: Perceptual user interfaces (introduction). Commun. ACM 43(3), 32–34 (2000). doi: 10.1145/330534.330535 CrossRefGoogle Scholar
  90. 90.
    van Rijsbergen, C.J.: Information Retrieval, 2nd edn. Butterworth-Heinemann, MA, USA (1979)Google Scholar
  91. 91.
    Valencia-Garcia, R., Garcia-Sanchez, F., Castellanos-Nieves, D., Fernandez-Breis, J.: OWLPath: an OWL ontology-guided query editor. IEEE Trans. Syst. Man Cybern. A Syst. Hum. 41(1), 121–136 (2011). doi: 10.1109/TSMCA.2010.2048029 CrossRefGoogle Scholar
  92. 92.
    Yee, K.P., Swearingen, K., Li, K., Hearst, M.: Faceted metadata for image search and browsing. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2003), pp. 401–408. ACM (2003). doi:  10.1145/642611.642681
  93. 93.
    Zloof, M.M.: Query-by-example: a database language. IBM Syst. J. 16(4), 324–343 (1997). doi: 10.1147/sj.164.0324 CrossRefGoogle Scholar
  94. 94.
    Zviedris, M., Barzdins, G.: ViziQuer: a tool to explore and query SPARQL endpoints. In: Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011), LNCS, vol. 6644, pp. 441–445. Springer (2011). doi: 10.1007/978-3-642-21064-8_31

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Ahmet Soylu
    • 1
    • 2
  • Martin Giese
    • 2
  • Ernesto Jimenez-Ruiz
    • 3
  • Guillermo Vega-Gorgojo
    • 2
  • Ian Horrocks
    • 3
  1. 1.Gjøvik University CollegeGjøvikNorway
  2. 2.Department of InformaticsUniversity of OsloOsloNorway
  3. 3.Department of Computer ScienceUniversity of OxfordOxfordUK

Personalised recommendations