Abstract
The Linked Open Data cloud has been increasing in popularity, with DBpedia as a first-class citizen in this cloud that has been widely adopted across many applications. Measuring similarity between resources and identifying their relatedness could be used for various applications such as item-based recommender systems. To this end, several similarity measures such as LDSD (Linked Data Semantic Distance) were proposed. However, some fundamental axioms for similarity measures such as “equal self-similarity”, “symmetry” or “minimality” are violated, and property similarities have been ignored. Moreover, none of the previous studies have provided a comparative study on other similarity measures. In this paper, we present a similarity measure, called Resim (Resource Similarity), based on top of a revised LDSD similarity measure. Resim aims to calculate the similarity of any resources in DBpedia by taking into account the similarity of the properties of these resources as well as satisfying the fundamental axioms. In addition, we evaluate our similarity measure with two state-of-the-art similarity measures (LDSD and Shakti) in terms of calculating the similarities for general resources (i.e., any resources without a domain restriction) in DBpedia and resources for music artist recommendations. Results show that our similarity measure can resolve some of the limitations of state-of-the-art similarity measures and performs better than them for calculating the similarities between general resources and music artist recommendations.
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References
Abel, F., Gao, Q., Houben, G.J., Tao, K.: Analyzing temporal dynamics in twitter profiles for personalized recommendations in the social web. In: Proceedings of the 3rd International Web Science Conference, p. 2. ACM (2011)
Abel, F., Herder, E., Houben, G.J., Henze, N., Krause, D.: Cross-system user modeling and personalization on the social web. User Model. User Adap. Inter. 23(2–3), 169–209 (2013)
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.G.: DBpedia: A nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007)
Brickley, D., Guha, R.V.: RDF vocabulary description language 1.0: RDF schema (2004)
Di Noia, T., Mirizzi, R., Ostuni, V.C., 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. ACM (2012)
Di Noia, T., Mirizzi, R., Ostuni, V.C., Romito, D., Zanker, M.: Linked open data to support content-based recommender systems. In: Proceedings of the 8th International Conference on Semantic Systems, I-SEMANTICS 2012, pp. 1–8, NY, USA (2012). http://doi.acm.org/10.1145/2362499.2362501
Ennis, M.D., Ashby, F.G.: Similarity Measures (2007). http://www.scholarpedia.org/article/Similarity_measures
Finkelstein, L., Gabrilovich, E., Matias, Y., Rivlin, E., Solan, Z., Wolfman, G., Ruppin, E.: Placing search in context: the concept revisited. In: Proceedings of the 10th International Conference on World Wide Web, pp. 406–414. ACM (2001)
Groues, V., Naudet, Y., Kao, O.: Adaptation and evaluation of a semantic similarity measure for dbpedia: a first experiment. In: 2012 Seventh International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), pp. 87–91. IEEE (2012)
Heath, T., Bizer, C.: Linked data: Evolving the web into a global data space. Synth. Lect. Semant. Web: Theor. Technol. 1(1), 1–136 (2011)
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)
Jeh, G., Widom, J.: SimRank: A measure of structural-context similarity. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002, pp. 538–543. ACM, NY, USA, New York (2002)
Leal, J.P., Rodrigues, V., Queirós, R.: Computing semantic relatedness using dbpedia(2012)
Lee, S., Yang, J., Park, S.-Y.: Discovery of hidden similarity on collaborative filtering to overcome sparsity problem. In: Suzuki, E., Arikawa, S. (eds.) DS 2004. LNCS (LNAI), vol. 3245, pp. 396–402. Springer, Heidelberg (2004)
Maedche, A., Zacharias, V.: Clustering ontology-based metadata in the semantic web. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, pp. 348–360. Springer, Heidelberg (2002)
Meng, L., Huang, R., Gu, J.: A review of semantic similarity measures in wordnet. Inter. J. Hybrid Inf. Technol. 6(1), 1–12 (2013)
Miller, G.A.: WordNet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995)
Musto, C., Basile, P., Lops, P., de Gemmis, M., Semeraro, G.: Linked open data-enabled strategies for Top-N recommendations. In: CBRecSys 2014, p. 49 (2014)
Noy, N.F., McGuinness, D.L.: Ontology development 101: A guide to creating your first ontology (2001)
Orlandi, F., Breslin, J., Passant, A.: Aggregated, interoperable and multi-domain user profiles for the social web. In: Proceedings of the 8th International Conference on Semantic Systems (2012)
Ostuni, V.C., Di Noia, T., Di Sciascio, E., Mirizzi, R.: Top-n recommendations from implicit feedback leveraging linked open data. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 85–92. ACM (2013)
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, Part II. LNCS, vol. 6497, pp. 209–224. Springer, Heidelberg (2010)
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)
Strobin, L., Niewiadomski, A.: Evaluating semantic similarity with a new method of path analysis in RDF using genetic algorithms. Comput. Sci. 21(2), 137–152 (2013)
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This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 (Insight Centre for Data Analytics).
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Piao, G., Ara, S.s., Breslin, J.G. (2016). Computing the Semantic Similarity of Resources in DBpedia for Recommendation Purposes. In: Qi, G., Kozaki, K., Pan, J., Yu, S. (eds) Semantic Technology. JIST 2015. Lecture Notes in Computer Science(), vol 9544. Springer, Cham. https://doi.org/10.1007/978-3-319-31676-5_13
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