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Employing Link Differentiation in Linked Data Semantic Distance

  • Sultan AlfarhoodEmail author
  • Susan Gauch
  • Kevin Labille
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 786)

Abstract

The use of Linked Open Data (LOD) has been explored in recommender systems in different ways, primarily through its graphical representation. The graph structure of LOD is utilized to measure inter-resource relatedness via their semantic distance in the graph. The intuition behind this approach is that the more connected resources are to each other, the more related they are. The drawback of this approach is that it treats all inter-resource connections identically rather than prioritizing links that may be more important in semantic relatedness calculations. In this paper, we show that different properties of inter-resource links hold different values for relatedness calculations between resources, and we exploit this observation to introduce improved resource semantic relatedness measures, Weighted Linked Data Semantic Distance (WLDSD) and Weighted Resource Similarity (WResim), which are more accurate than the current state of the art approaches. Exploiting these proposed weighted approaches, we also present two different ways to calculate links weights: Resource-Specific Link Awareness Weights (RSLAW) and Information Theoretic Weights (ITW). To validate the effectiveness of our approaches, we conducted an experiment to identify the relatedness between musical artists in DBpedia, and it demonstrated that approaches that prioritize link properties resulted in more accurate recommendation results.

References

  1. 1.
    Cremonesi, P., Tripodi, A., Turrin, R.: Cross-domain recommender systems. In: Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops, Washington, DC, USA, pp. 496–503 (2011)Google Scholar
  2. 2.
    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). doi: 10.1007/978-3-642-30284-8_9 CrossRefGoogle Scholar
  3. 3.
    Di Noia, T., Ostuni, V.C.: Recommender Systems and Linked Open Data. In: Faber, W., Paschke, A. (eds.) Reasoning Web 2015. LNCS, vol. 9203, pp. 88–113. Springer, Cham (2015). doi: 10.1007/978-3-319-21768-0_4 CrossRefGoogle Scholar
  4. 4.
    Figueroa, C., Vagliano, I., Rocha, O., Morisio, M.: A systematic literature review of Linked Data-based recommender systems. Concurrency Comput. Pract. Exper. 27(17), 4659–4684 (2015)CrossRefGoogle Scholar
  5. 5.
    Passant, A.: Measuring semantic distance on linking data and using it for resources recommendations. In: AAAI Spring Symposium: Linked Data Meets Artificial Intelligence, vol. 77, p. 123 (2010)Google Scholar
  6. 6.
    Piao, G., showkat Ara, S., Breslin, J.: Computing the semantic similarity of resources in DBpedia for recommendation purposes. In: Joint International Semantic Technology Conference, pp. 185–200 (2015)Google Scholar
  7. 7.
    Piao, G., Breslin, J.: Measuring semantic distance for linked open data-enabled recommender systems. In: Proceedings of the 31st Annual ACM Symposium on Applied Computing, pp. 315–320 (2016)Google Scholar
  8. 8.
    Passant, A.: dbrec — music recommendations using DBpedia. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010. LNCS, vol. 6497, pp. 209–224. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-17749-1_14 CrossRefGoogle Scholar
  9. 9.
    Alfarhood, S., Labille, K., Gauch, S.: PLDSD: propagated linked data semantic distance. In: 2017 IEEE 26th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), Poznan, Poland, pp. 278–283 (2017)Google Scholar
  10. 10.
    Di Noia, T., Mirizzi, R., Ostuni, V., Romito, D.: Exploiting the web of data in model-based recommender systems. In: Proceedings of the Sixth ACM Conference on Recommender Systems, pp. 253–256 (2012)Google Scholar
  11. 11.
    Di Noia, T., Mirizzi, R., Ostuni, V., Romito, D., Zanker, M.: Linked open data to support content-based recommender systems. In: Proceedings of the 8th International Conference on Semantic Systems, pp. 1–8 (2012)Google Scholar
  12. 12.
    Nguyen, P., Tomeo, P., Di Noia, T., Di Sciascio, E.: An evaluation of SimRank and personalized PageRank to build a recommender system for the web of data. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1477–1482 (2015)Google Scholar
  13. 13.
    Fernández-Tobías, I., Cantador, I., Kaminskas, M., Ricci, F.: A generic semantic-based framework for cross-domain recommendation. In: Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems, pp. 25–32 (2011)Google Scholar
  14. 14.
    Kaminskas, M., Fernández-Tobías, I., Ricci, F., Cantador, I.: Knowledge-based music retrieval for places of interest. In: Proceedings of the Second International ACM Workshop on Music Information Retrieval with User-Centered and Multimodal Strategies, pp. 19–24 (2012)Google Scholar
  15. 15.
    Meymandpour, R., Davis, J.G.: Enhancing recommender systems using linked open data-based semantic analysis of items. In: Davis, J. (ed.) 3rd Australasian Web Conference (AWC 2015), pp. 11–17 (2015)Google Scholar
  16. 16.
    Heitmann, B., Hayes, C.: Using linked data to build open, collaborative recommender systems. In: AAAI Spring Symposium: Linked Data Meets Artificial Intelligence, pp. 76–81 (2010)Google Scholar
  17. 17.
    Heitmann, B.: An open framework for multi-source, cross-domain personalisation with semantic interest graphs. In: Proceedings of the Sixth ACM Conference on Recommender Systems, pp. 313–316 (2012)Google Scholar
  18. 18.
    Peska, L., Vojtas, P.: Using linked open data in recommender systems. In: Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics, pp. 17:1–17:6 (2015)Google Scholar
  19. 19.
    Salton, G., McGill, M.: Introduction to Modern Information Retrieval. McGraw-Hill Inc., New York (1983)zbMATHGoogle Scholar
  20. 20.
    Jaccard, P.: Etude de la distribution florale dans une portion des Alpes et du Jura. Bulletin de la Societe Vaudoise des Sciences Naturelles 37(142), 547–579 (1901)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.King Saud UniversityRiyadhSaudi Arabia
  2. 2.University of ArkansasFayettevilleUSA

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