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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Dumais, S.T.: Latent semantic analysis. Annual Review of Information Science and Technology 38(1), 188–230 (2004)
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)
Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)
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)
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)
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)
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)
Potamias, M., Bonchi, F., Gionis, A., Kollios, G.: k-nearest neighbors in uncertain graphs. Proc. VLDB Endow 3(1-2), 997–1008 (2010)
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)
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)
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)
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)
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)
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)
Fang, L., Sarma, A.D., Yu, C., Bohannon, P.: Rex: explaining relationships between entity pairs. Proc. VLDB Endow 5(3), 241–252 (2011)
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)
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)
Church, K.W., Hanks, P.: Word association norms, mutual information, and lexicography. Comput. Linguist. 16(1), 22–29 (1990)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Nunes, B.P., Fetahu, B., Kawase, R., Dietze, S., Casanova, M.A., Maynard, D. (2015). Interlinking Documents Based on Semantic Graphs with an Application. In: Tweedale, J., Jain, L., Watada, J., Howlett, R. (eds) Knowledge-Based Information Systems in Practice. Smart Innovation, Systems and Technologies, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-319-13545-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-13545-8_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13544-1
Online ISBN: 978-3-319-13545-8
eBook Packages: EngineeringEngineering (R0)