World Wide Web

, Volume 21, Issue 2, pp 421–453 | Cite as

Leveraging link pattern for entity-centric exploration over Linked Data

  • Liang Zheng
  • Yuzhong Qu
  • Gong Cheng


The increasing amount of Linked Data on the Web can be reused to facilitate numerous applications. One of the first steps is to explore these structured data to determine whether there is relevant information. Since an entity-centric model closely reflects the real world, it provides an intuitive way to explore Linked Data. However, large numbers of linked entities and high diversity of links between entities, often make it difficult for users to understand the overall structure, as well as find the entities of interest quickly for further exploration. In this paper, we present a link pattern discovery approach to facilitate entity exploration. Link patterns describe explicit and implicit relationships between entities and can be used to categorize linked entities. On top of link patterns, we construct a hierarchy to allow exploration of linked entities in a hierarchical multiscale fashion. To lighten users’ exploration burden further, we select top-k link patterns from hierarchy as navigation options. The proposed approach is implemented in a Linked Data browser called SView. We compare it with two conventional Linked Data browsers by conducting a task-based user study. The experiment results show that our approach provides effective support for entity exploration.


Linked data Entity-centric exploration Link pattern 



This work is supported in part by the National Science Foundation of China under Grant Nos. 61572247 and 61370019, and in part by the 863 Program under Grant 2015AA015406. We are also grateful to all the participants in the experiments of this work.


  1. 1.
    Alahmari, F., Thom, J.A., Magee, L., Wong, W.: Evaluating semantic browsers for consuming Linked Data. In: Australasian Database Conference, pp. 89–98 (2012)Google Scholar
  2. 2.
    Araujo, S., Schwabe, D., Barbosa, S.: Experimenting with explorator: a direct manipulation generic rdf browser and querying tool. In: Visual Interfaces to the Social and the Semantic Web (2009)Google Scholar
  3. 3.
    Becker, C., Bizer, C.: DBpedia mobile-a location-aware semantic Web client. In: Proceedings of the Semantic Web Challenge (2008)Google Scholar
  4. 4.
    Belohlavek, R., Macko, J.: Selecting important concepts using weights. In: 9th International Conference on Formal Concept Analysis, pp. 65–80 (2011)Google Scholar
  5. 5.
    Berners-Lee, T., et al.: Tabulator: exploring and analyzing linked data on the semantic Web. In: 3rd International Semantic Web User Interaction Workshop (2006)Google Scholar
  6. 6.
    Bikakis, N., Sellis, T.: Exploration and visualization in the Web of big linked data: a survey of the state of the art. In: 6th International Workshop on Linked Web Data Management (2016)Google Scholar
  7. 7.
    Bikakis, N., Skourla, M., Papastefanatos, G.: rdf:SynopsViz: a framework for hierarchical linked data visual exploration and analysis. In: ESWC, pp. 292–297 (2014)Google 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)CrossRefGoogle Scholar
  9. 9.
    Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: DBpedia-a crystallization point for the Web of data. Web Semant. Sci. Serv. Agents World Wide Web 7(3), 154–165 (2009)CrossRefGoogle Scholar
  10. 10.
    Bojars, U., Passant, A., Giasson, F., Breslin, J.: An architecture to discover and query decentralized RDF data. In: 3rd Extended Semantic Web Conference Workshop (2007)Google Scholar
  11. 11.
    Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: SIGMOD, pp. 1247–1250 (2008)Google Scholar
  12. 12.
    Bordat, J.P.: Calcul pratique du treillis de Galois dune correspondance. Math. Sci. Hum. (96), 31–47 (1986)Google Scholar
  13. 13.
    Brooke, J.: SUS-a quick and dirty usability scale. Usability Eval. Indust. 189 (194), 4–7 (1996)Google Scholar
  14. 14.
    Carpineto, C., Romano, G., Bordoni, F.U.: Exploiting the potential of concept lattices for information retrieval with CREDO. J. UCS 10(8), 985–1013 (2004)zbMATHGoogle Scholar
  15. 15.
    Cheng, G., Qu, Y.: Searching linked objects with falcons: Approach, implementation and evaluation. Int. J. Semant. Web Inf. Syst. 5(3), 49–70 (2009)CrossRefGoogle Scholar
  16. 16.
    Chierichetti, F., Kumar, R., Tomkins, A.: Max-cover in map-reduce. In: 19th International World Wide Web Conference, pp. 231–240 (2010)Google Scholar
  17. 17.
    De Berg, M., Cabello, S., Har-Peled, S.: Covering many or few points with unit disks. Theory Comput. Syst. 45(3), 446–469 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Ducrou, J., Eklund, P.: An intelligent user interface for browsing and search MPEG-7 images using concept lattices. Int. J. Found. Comput. Sci. World Sci. 19(2), 359–381 (2008)CrossRefzbMATHGoogle Scholar
  19. 19.
    Ferre, S.: Camelis: a logical information system to organise and browse a collection of documents. Int. J. Gen. Syst. 38(4), 379–403 (2009)CrossRefzbMATHGoogle Scholar
  20. 20.
    Freitas, A., Curry, E., Oliveira, J.G., O’Riain, S.: Querying heterogeneous datasets on the linked data Web: challenges, approaches, and trends. IEEE Internet Comput. 16(1), 24–33 (2012)CrossRefGoogle Scholar
  21. 21.
    Ganter, B., Wille, R.: Formal concept analysis: mathematical foundations. Springer Science and Business Media (2012)Google Scholar
  22. 22.
    Garca, R., Gimeno, J.M., Perdrix, F., Gil, R., et al.: Building a usable and accessible semantic Web interaction platform. World Wide Web 13(12), 143–167 (2010)CrossRefGoogle Scholar
  23. 23.
    Harth, A.: VisiNav: A system for visual search and navigation on Web data. J. Web Sem. 8(4), 348–354 (2010)CrossRefGoogle Scholar
  24. 24.
    Hearst, M.A.: Clustering versus faceted categories for information exploration. Commun. ACM 49(4), 59–61 (2006)CrossRefGoogle Scholar
  25. 25.
    Heath, T., Bizer, C.: Linked data: evolving the Web into a global data space. In: Synthesis Lectures on the Semantic Web: Theory and Technology, Morgan&Claypool Publishers (2011)Google Scholar
  26. 26.
    Heim, P., Ertl, T., Ziegler, J.: Facet graphs: complex semantic querying made easy. In: 7th Extended Semantic Web Conference, pp 288–302 (2010)Google Scholar
  27. 27.
    Heim, P., Lohmann, S., Tsendragchaa, D., Ertl, T.: SemLens: visual analysis of semantic data with scatter plots and semantic lenses. In: I-SEMANTICS, pp. 175–178 (2011)Google Scholar
  28. 28.
    Hildebrand, M., Ossenbruggen, J.V., Hardman, L.: /facet: a browser for heterogeneous semantic Web repositories. In: 14th International Semantic Web Conference, pp. 272–285 (2006)Google Scholar
  29. 29.
    Hogan, A., Harth, A., Umrich, J., Decker, S.: Towards a scalable search and query engine for the Web. In: 16th International World Wide Web Conference, pp. 1301–1302 (2007)Google Scholar
  30. 30.
    Huynh, D.F., Karger, D.: Parallax and companion: set-based browsing for the data Web. In: 18th International World Wide Web Conference, pp. 6–16 (2009)Google Scholar
  31. 31.
    Khuller, S., Moss, A., Naor, J.S.: The budgeted maximum coverage problem. Inf. Process. Lett. 70(1), 39–45 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  32. 32.
    Klyne, G., Carroll, J.J.: Resource Description Framework (RDF): concepts and abstract syntax-W3C Recommendation (2004)Google Scholar
  33. 33.
    Kumar, R., Tomkins, A.: A characterization of online browsing behavior. In: 19th International World Wide Web Conference, pp. 561–570 (2010)Google Scholar
  34. 34.
    Kuznetsov, S.O., Obiedkov, S.A.: Comparing performance of algorithms for generating concept lattices. J. Exp. Theor. Artif. Intell. 14(2-3), 189–216 (2002)CrossRefzbMATHGoogle Scholar
  35. 35.
    Lin, T., Pantel, P., Gamon, M., Kannan, A., Fuxman, A.: Active objects: actions for entity-centric search. In: 21st International World Wide Web Conference, pp. 589–598 (2012)Google Scholar
  36. 36.
    Manning, C.D., Raghavan, P., Schütze, H.: Introduction to information retrieval. Cambridge University Press (2008)Google Scholar
  37. 37.
    Marie, N., Gandon, F.L.: Survey of linked data based exploration systems. In: 3rd International Conference on Intelligent Exploration of Semantic Data (IESD), pp. 66–77 (2014)Google Scholar
  38. 38.
    Oren, E., Delbru, R., Decker, S.: Extending faceted navigation for rdf data. In: 14th International Semantic Web Conference, pp. 559–572 (2006)Google Scholar
  39. 39.
    Popov, I.O., Schraefel, M.C., Hall, W., Shadbolt, N.: Connecting the dots: a multipivot approach to data exploration. In: 10th International Semantic Web Conference, pp 553–568 (2011)Google Scholar
  40. 40.
    Pound, J., Mika, P., Zaragoza, H.: Ad-hoc object retrieval in the Web of data. In: 19th International World Wide Web Conference, pp. 771–780 (2010)Google Scholar
  41. 41.
    Selke, J., Homoceanu, S., Balke, W.T.: Conceptual views for entity-centric search: turning data into meaningful concepts. Comput. Sci.-Res. Dev. 27(1), 65–79 (2012)CrossRefGoogle Scholar
  42. 42.
    Stoica, E., Hearst, M.: Nearly automated metadata hierarchy creation. In: HLT-NAACL, pp. 117–120 (2004)Google Scholar
  43. 43.
    Tummarello, G., Cyganiak, R., Catasta, M., Danielczyk, S., Delbru, R., Decker, S.: live views on the Web of data. J. Web Sem. 8(4), 355–364 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.National Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

Personalised recommendations