Interlinking Documents Based on Semantic Graphs with an Application

  • Bernardo Pereira Nunes
  • Besnik Fetahu
  • Ricardo Kawase
  • Stefan Dietze
  • Marco Antonio Casanova
  • Diana Maynard
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 30)

Abstract

Connectivity and relatedness of Web resources are two concepts that define to what extent different parts are connected or related to one another. Measuring connectivity and relatedness between Web resources is a growing field of research, often the starting point of recommender systems. Although relatedness is liable to subjective interpretations, connectivity is not. Given the Semantic Web’s ability of linking Web resources, connectivity can be measured by exploiting the links between entities. Further, these connections can be exploited to uncover relationships between Web resources. This chapter describes the application and expansion of a relationship assessment methodology from social network theory to measure the connectivity between documents. The connectivity measures are used to identify connected and related Web resources. The approach is able to expose relations that traditional text-based approaches fail to identify. The proposed approaches are validated and assessed through an evaluation on a real-world dataset, where results show that the proposed techniques outperform state of the art approaches. Finally, a Web-based application called Cite4Me that uses the proposed approach is presented.

Keywords

Document Connectivity Semantic Connections Semantic Graphs 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Dietze, S., Maynard, D., Demidova, E., Risse, T., Peters, W., Doka, K., Stavrakas, Y.: Entity extraction and consolidation for social web content preservation. In: Mitschick, A., Loizides, F., Predoiu, L., Nurnberger, A., Ross, S. (eds.) SDA. of CEUR Workshop Proceedings, CEUR-WS, vol. 912, pp. 18–29 (2012)Google Scholar
  2. 2.
    Dumais, S.T.: Latent semantic analysis. Annual Review of Information Science and Technology 38(1), 188–230 (2004)CrossRefGoogle Scholar
  3. 3.
    Sheth, A., Aleman-Meza, B., Arpinar, F.S., Sheth, A., Ramakrishnan, C., Bertram, C., Warke, Y., Anyanwu, K., Aleman-meza, B., Arpinar, I.B., Kochut, K., Halaschek, C., Ramakrishnan, C., Warke, Y., Avant, D., Arpinar, F.S., Anyanwu, K., Kochut, K.: Semantic association identification and knowledge discovery for national security applications. Journal of Database Management 16, 33–53 (2005)CrossRefGoogle Scholar
  4. 4.
    Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)CrossRefMATHGoogle Scholar
  5. 5.
    Pereira Nunes, B., Kawase, R., Dietze, S., Taibi, D., Casanova, M.A., Nejdl, W.: Can entities be friends? In: Rizzo, G., Mendes, P., Charton, E., Hellmann, S., Kalyanpur, A. (eds.) Proceedings of the Web of Linked Entities Workshop in conjuction with the 11th International Semantic Web Conference of CEUR-WS, vol. 906, pp. 45–57 (November 2012)Google Scholar
  6. 6.
    Pereira Nunes, B., Dietze, S., Casanova, M.A., Kawase, R., Fetahu, B., Nejdl, W.: Combining a Co-occurrence-Based and a Semantic Measure for Entity Linking. In: Cimiano, P., Corcho, O., Presutti, V., Hollink, L., Rudolph, S. (eds.) ESWC 2013. LNCS, vol. 7882, pp. 548–562. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Kaldoudi, E., Dovrolis, N., Dietze, S.: Information organization on the internet based on heterogeneous social networks. In: Proceedings of the 29th ACM International Conference on Design of Communication, SIGDOC 2011, pp. 107–114. ACM, New York (2011)Google Scholar
  8. 8.
    Thor, A., Anderson, P., Raschid, L., Navlakha, S., Saha, B., Khuller, S., Zhang, X.-N.: Link prediction for annotation graphs using graph summarization. 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. 714–729. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Potamias, M., Bonchi, F., Gionis, A., Kollios, G.: k-nearest neighbors in uncertain graphs. Proc. VLDB Endow 3(1-2), 997–1008 (2010)CrossRefGoogle Scholar
  10. 10.
    Heim, P., Hellmann, S., Lehmann, J., Lohmann, S., Stegemann, T.: RelFinder: Revealing relationships in RDF knowledge bases. In: Chua, T.-S., Kompatsiaris, Y., Mérialdo, B., Haas, W., Thallinger, G., Bailer, W. (eds.) SAMT 2009. LNCS, vol. 5887, pp. 182–187. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Hasan, M., Zaki, M.: A survey of link prediction in social networks. In: Aggarwal, C.C. (ed.) Social Network Data Analytics, pp. 243–275. Springer, US (2011)Google Scholar
  12. 12.
    Leskovec, J., Huttenlocher, D., Kleinberg, J.: Predicting positive and negative links in online social networks. In: Proceedings of the 19th International Conference on World wide web, WWW 2010, pp. 641–650. ACM (2010)Google Scholar
  13. 13.
    Groß, A., Hartung, M., Kirsten, T., Rahm, E.: Mapping Composition for Matching Large Life Science Ontologies. In: Proceedings of the 2nd International Conference on Biomedical Ontology. ICBO, pp. 109–116 (2011)Google Scholar
  14. 14.
    Vidal, V.M.P., de Macedo, J.A.F., Pinheiro, J.C., Casanova, M.A., Porto, F.: Query processing in a mediator based framework for linked data integration. IJBDCN 7(2), 29–47 (2011)Google Scholar
  15. 15.
    Xu, L., Embley, D.W.: Discovering direct and indirect matches for schema elements. In: Proceedings of the Eighth International Conference on Database Systems for Advanced Applications. DASFAA 2003, p. 39. IEEE Computer Society, Washington (2003)Google Scholar
  16. 16.
    Fang, L., Sarma, A.D., Yu, C., Bohannon, P.: Rex: explaining relationships between entity pairs. Proc. VLDB Endow 5(3), 241–252 (2011)CrossRefGoogle Scholar
  17. 17.
    Graves, A., Adali, S., Hendler, J.: A method to rank nodes in an rdf graph. In: Bizer, C., Joshi, A. (eds.) Proceedings of the Poster and Demonstration Session at the 7th International Semantic Web Conference (ISWC 2008), CEUR Workshop Proceedings, CEUR-WS, Karlsruhe, Germany, October 28, vol. 401 (2008)Google Scholar
  18. 18.
    Damljanovic, D., Stankovic, M., Laublet, P.: Linked Data-Based Concept Recommendation: Comparison of Different Methods in Open Innovation Scenario. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 24–38. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  19. 19.
    Church, K.W., Hanks, P.: Word association norms, mutual information, and lexicography. Comput. Linguist. 16(1), 22–29 (1990)Google Scholar
  20. 20.
    Gligorov, R., ten Kate, W., Aleksovski, Z., van Harmelen, F.: Using google distance to weight approximate ontology matches. In: Proceedings of the 16th International Conference on World Wide Web. WWW 2007, pp. 767–776. ACM, New York (2007)Google Scholar
  21. 21.
    Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using wikipedia-based explicit semantic analysis. In: Proceedings of the 20th International Joint Conference on Artifical intelligence, IJCAI 2007, pp. 1606–1611. Morgan Kaufmann Publishers Inc, San Francisco (2007)Google Scholar
  22. 22.
    Taibi, D., Dietze, S.: Fostering analytics on learning analytics research: the lak dataset. In: d’Aquin, M., Dietze, S., Drachsler, H., Herder, E., Taibi, D. (eds.) Proceedings of the LAK Data Challenge, CEUR Workshop Proceedings, CEUR-WS, Leuven, Belgium, April 9, vol. 974 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Bernardo Pereira Nunes
    • 1
  • Besnik Fetahu
    • 2
  • Ricardo Kawase
    • 2
  • Stefan Dietze
    • 2
  • Marco Antonio Casanova
    • 1
  • Diana Maynard
    • 3
  1. 1.Department of InformaticsPontifical Catholic UniversityRio de JaneiroBrazil
  2. 2.L3S Research CenterLeibniz University HannoverHannoverGermany
  3. 3.Department of Computer ScienceUniversity of SheffieldSheffieldUK

Personalised recommendations