Abstract
Tripleset interlinking is one of the main principles of Linked Data. However, the discovery of existing triplesets relevant to be linked with a new tripleset is a non-trivial task in the publishing process. Without prior knowledge about the entire Web of Data, a data publisher must perform an exploratory search, which demands substantial effort and may become impracticable, with the growth and dissemination of Linked Data. Aiming at alleviating this problem, this paper proposes a recommendation approach for this scenario, using a Social Network perspective. The experimental results show that the proposed approach obtains high levels of recall and reduces in up to 90% the number of triplesets to be further inspected for establishing appropriate links.
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References
Berners-Lee, T.: Linked Data - Design Issues. W3C (June 2009), http://www.w3.org/DesignIssues/LinkedData.html (accessed on March 2013)
Bizer, C., Heath, T., Berners-Lee, T.: Linked Data - The Story So Far. International Journal on Semantic Web and Information Systems (IJSWIS) 5(3), 1–22 (2009)
Nikolov, A., d’Aquin, M.: Identifying Relevant Sources for Data Linking using a Semantic Web Index. In: WWW 2011 Workshop on Linked Data on the Web, LDOW. CEUR Workshop Proceedings, vol. 813. CEUR-WS.org (2011)
Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge University Press (2011)
Bergamaschi, S., Guerra, F., Leiba, B.: Guest editors’ introduction: Information overload. IEEE Internet Computing 14(6), 10–13 (2010)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing 7(1), 76–80 (2003)
Schafer, J.B., Konstan, J., Riedl, J.: Recommender systems in e-commerce. In: Proceedings of the 1st ACM Conference on Electronic Commerce, EC 1999, pp. 158–166. ACM, New York (1999)
Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: Fifth ACM Conference on Digital Libraries, DL 2000, pp. 195–204. ACM, New York (2000)
Burke, R.D.: Hybrid recommender systems: Survey and experiments. User Model. User-Adapt. Interact. 12(4), 331–370 (2002)
Golbeck, J., Hendler, J.: Filmtrust: movie recommendations using trust in web-based social networks. In: 3rd IEEE Consumer Communications and Networking Conference, CCNC 2006, vol. 1, pp. 282–286 (2006)
Montaner, M., López, B., de la Rosa, J.L.: A taxonomy of recommender agents on the internet. Artif. Intell. Rev. 19(4), 285–330 (2003)
Meo, P.D., Nocera, A., Rosaci, D., Ursino, D.: Recommendation of reliable users, social networks and high-quality resources in a social internetworking system. AI Commun. 24(1), 31–50 (2011)
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)
Newman, M.E.J.: The structure and function of complex networks. SIAM Review 45(2), 167–256 (2003)
Quercia, D., Capra, L.: Friendsensing: recommending friends using mobile phones. In: Third ACM Conference on Recommender Systems, RecSys 2009, pp. 273–276. ACM, New York (2009)
Nikolov, A., d’Aquin, M., Motta, E.: What should I link to? Identifying relevant sources and classes for data linking. In: Pan, J.Z., Chen, H., Kim, H.-G., Li, J., Wu, Z., Horrocks, I., Mizoguchi, R., Wu, Z. (eds.) JIST 2011. LNCS, vol. 7185, pp. 284–299. Springer, Heidelberg (2012)
Lóscio, B.F., Batista, M.C.M., Souza, D., Salgado, A.C.: Using information quality for the identification of relevant web data sources: a proposal. In: 14th International Conference on Information Integration and Web-based Applications & Services, IIWAS 2012, pp. 36–44. ACM, New York (2012)
de Oliveira, H.R., Tavares, A.T., Lóscio, B.F.: Feedback-based data set recommendation for building linked data applications. In: 8th International Conference on Semantic Systems, I-SEMANTICS 2012, pp. 49–55. ACM, New York (2012)
Halpin, H., Hayes, P.J.: When owl:sameAs isn’t the same: An analysis of identity links on the semantic web. In: Proceedings of the WWW 2012 Workshop: Linked Data on the Web (2010)
Baeza-Yates, R.A., Ribeiro-Neto, B.A.: Modern Information Retrieval - the concepts and technology behind search, 2nd edn. Pearson Education Ltd., Harlow (2011)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press (July 2008)
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Lopes, G.R., Leme, L.A.P.P., Nunes, B.P., Casanova, M.A., Dietze, S. (2013). Recommending Tripleset Interlinking through a Social Network Approach. In: Lin, X., Manolopoulos, Y., Srivastava, D., Huang, G. (eds) Web Information Systems Engineering – WISE 2013. WISE 2013. Lecture Notes in Computer Science, vol 8180. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41230-1_13
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DOI: https://doi.org/10.1007/978-3-642-41230-1_13
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