Advertisement

Evaluating Knowledge Anchors in Data Graphs Against Basic Level Objects

  • Marwan Al-TawilEmail author
  • Vania Dimitrova
  • Dhavalkumar Thakker
  • Alexandra Poulovassilis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10360)

Abstract

The growing number of available data graphs in the form of RDF Linked Data enables the development of semantic exploration applications in many domains. Often, the users are not domain experts and are therefore unaware of the complex knowledge structures represented in the data graphs they interact with. This hinders users’ experience and effectiveness. Our research concerns intelligent support to facilitate the exploration of data graphs by users who are not domain experts. We propose a new navigation support approach underpinned by the subsumption theory of meaningful learning, which postulates that new concepts are grasped by starting from familiar concepts which serve as knowledge anchors from where links to new knowledge are made. Our earlier work has developed several metrics and the corresponding algorithms for identifying knowledge anchors in data graphs. In this paper, we assess the performance of these algorithms by considering the user perspective and application context. The paper address the challenge of aligning basic level objects that represent familiar concepts in human cognitive structures with automatically derived knowledge anchors in data graphs. We present a systematic approach that adapts experimental methods from Cognitive Science to derive basic level objects underpinned by a data graph. This is used to evaluate knowledge anchors in data graphs in two application domains - semantic browsing (Music) and semantic search (Careers). The evaluation validates the algorithms, which enables their adoption over different domains and application contexts.

Keywords

Data graphs Basic level objects Knowledge anchors Usable semantic data exploration 

Notes

Acknowledgements

This research uses outputs from the EU/FP7 project Dicode and the UK/JISC project L4All. We are grateful to Riccardo Frosini and Mirko Dimartino in helping us prepare the L4All dataset used for the experiments in this paper. We thank all the participants in the experimental studies.

References

  1. 1.
    Marie, N., Gandon, F.: Survey of linked data based exploration systems. In: IESD@ISWC (2014)Google Scholar
  2. 2.
    Thakker, D., Dimitrova, V., Lau, L., Yang-Turner, F., Despotakis, D.: Assisting user browsing over linked data: requirements elicitation with a user study. In: ICWE 2013 International conference on Web Engineering, pp. 376–383 (2013)Google Scholar
  3. 3.
    Cheng, G., Zhang, Y., Qu, Y.: Explass: exploring associations between entities via top-k ontological patterns and facets. In: ISWC 2013, pp. 422–437 (2014)Google Scholar
  4. 4.
    Thellmann, K., Galkin, M., Orlandi, F., Auer, S.: LinkDaViz – automatic binding of linked data to visualizations. In: ISWC 2013, pp. 147–162 (2015)Google Scholar
  5. 5.
    Lopez, V., Fernández, M., Motta, E., Stieler, N.: PowerAqua: supporting users in querying and exploring the semantic web. Semant. Web. 3, 249–265 (2012)Google Scholar
  6. 6.
    Cheng, G., Qu, Y.: Searching linked objects with falcons: approach, implementation and evaluation. Int. J. Semant. Web Inf. Syst. 5, 49–70 (2009)CrossRefGoogle Scholar
  7. 7.
    Al-Tawil, M., Thakker, D., Dimitrova, V.: Nudging to expand user’s domain knowledge while exploring linked data. In: IESD@ISWC (2015)Google Scholar
  8. 8.
    Ausubel, D.P.: A subsumption theory of meaningful verbal learning and retention. J. Gen. Psychol. 66, 213–224 (1962)CrossRefGoogle Scholar
  9. 9.
    Al-Tawil, M., Dimitrova, V., Thakker, D., Bennett, B.: Identifying knowledge anchors in a data graph. In: HT 2016 - 27th ACM Conference on Hypertext and Social Media (2016)Google Scholar
  10. 10.
    Rosch, E., Mervis, C.B., Gray, W.D., Johnson, D.M., Boyes-Braem, P.: Basic objects in neutral categories. Cogn. Psychol. 8, 382–439 (1976)CrossRefGoogle Scholar
  11. 11.
    Sah, M., Wade, V.: Personalized concept-based search on the linked open data. J. Web Semant. 36, 32–57 (2016)CrossRefGoogle Scholar
  12. 12.
    Zimmer, B., Kerren, A.: Harnessing WebGL and WebSockets for a web-based collaborative graph exploration tool. In: Cimiano, P., Frasincar, F., Houben, G.-J., Schwabe, D. (eds.) ICWE 2015. LNCS, vol. 9114, pp. 583–598. Springer, Cham (2015). doi: 10.1007/978-3-319-19890-3_37 CrossRefGoogle Scholar
  13. 13.
    Zheng, L., Xu, J., Jiang, J., Qu, Y., Cheng, G.: Iterative entity navigation via co-clustering semantic links and entity classes. In: Sack, H., Blomqvist, E., d’Aquin, M., Ghidini, C., Ponzetto, S.P., Lange, C. (eds.) ESWC 2016. LNCS, vol. 9678, pp. 168–181. Springer, Cham (2016). doi: 10.1007/978-3-319-34129-3_11 CrossRefGoogle Scholar
  14. 14.
    Troullinou, G., Kondylakis, H., Daskalaki, E., Plexousakis, D.: RDF digest: efficient summarization of RDF/S KBs. In: Gandon, F., Sabou, M., Sack, H., d’Amato, C., Cudré-Mauroux, P., Zimmermann, A. (eds.) ESWC 2015. LNCS, vol. 9088, pp. 119–134. Springer, Cham (2015). doi: 10.1007/978-3-319-18818-8_8 CrossRefGoogle Scholar
  15. 15.
    Zhang, X., Cheng, G., Qu, Y.: Ontology summarization based on RDF sentence graph. In: Proceedings of the 16th International World Wide Web Conference (2007)Google Scholar
  16. 16.
    Wille, R.: Formal concept analysis as mathematical theory of concepts and concept hierarchies. In: Formal Concept Analysis, pp. 1–33 (2005)Google Scholar
  17. 17.
    Li, N., Motta, E.: Evaluations of user-driven ontology summarization. In: Cimiano, P., Pinto, H.S. (eds.) EKAW 2010. LNCS, vol. 6317, pp. 544–553. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-16438-5_44 CrossRefGoogle Scholar
  18. 18.
    Peroni, S., Motta, E., Aquin, M.: Identifying key concepts in an ontology through the integration of cognitive principles with statistical and topological measures. In: ASWC 2008 (2008)Google Scholar
  19. 19.
    Belohlavek, R., Trnecka, M.: Basic level in formal concept analysis: Interesting concepts and psychological ramifications. In: International Joint Conference on Artificial Intelligence, pp. 1233–1239 (2013)Google Scholar
  20. 20.
    Bizer, C., Heath, T., Berners-Lee, T.: Linked data-the story so far. Int. J. Semant. Web Inf. Syst. (2009)Google Scholar
  21. 21.
    Rosch, E., Lloyd, B.B.: Cognition and Categorization, pp. 27–48. Lloydia Cincinnati (1978)Google Scholar
  22. 22.
    Henry Kucera, W.N.F.: Computational Analysis of Present-Day American English. Am. Doc. 19, 419 (1968)Google Scholar
  23. 23.
    Cappiello, C., Noia, T., Marcu, B.A., Matera, M.: A quality model for linked data exploration. In: Bozzon, A., Cudre-Maroux, P., Pautasso, C. (eds.) ICWE 2016. LNCS, vol. 9671, pp. 397–404. Springer, Cham (2016). doi: 10.1007/978-3-319-38791-8_25 Google Scholar
  24. 24.
    Heath, T., Bizer, C.: Linked data: Evolving the Web into a Global Data Space, 1st edn. (2011)Google Scholar
  25. 25.
    Palmer, C.F., Jones, R.K., Hennessy, B.L., Unze, M.G., Pick, A.D.: How Is a Trumpet known? The “Basic Object Level” concept and perception of musical instruments. Am. J. Psychol. 102, 17–37 (1989)CrossRefGoogle Scholar
  26. 26.
    de Freitas, S., Harrison, I., Magoulas, G., Papamarkos, G., Poulovassilis, A., Van Labeke, N., Mee, A., Oliver, M.: L4All, a web-service based system for lifelong learners. In: Learning Grid Handbook: Concepts, Technologies and Applications, vol. 2, pp. 143–155 (2008)Google Scholar
  27. 27.
    Poulovassilis, A., Al-Tawil, M., Frosini, R., Dimartino, M., Dimitrova, V.: Combining flexible queries and knowledge anchors to facilitate the exploration of knowledge graphs. In: IESD@ISWC (2016)Google Scholar
  28. 28.
    Belohlavek, R., Trnecka, M.: Basic level of concepts in formal concept analysis. In: Domenach, F., Ignatov, D.I., Poelmans, J. (eds.) ICFCA 2012. LNCS, vol. 7278, pp. 28–44. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-29892-9_9 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Marwan Al-Tawil
    • 1
    Email author
  • Vania Dimitrova
    • 1
  • Dhavalkumar Thakker
    • 2
  • Alexandra Poulovassilis
    • 3
  1. 1.School of ComputingUniversity of LeedsLeedsUK
  2. 2.School of Electrical Engineering and Computer ScienceUniversity of BradfordBradfordUK
  3. 3.Knowledge Lab, BirkbeckUniversity of LondonLondonUK

Personalised recommendations