Abstract
This work introduces a new class of group similarity where different measures are parameterized with respect to a basic similarity defined on the elements of the sets. Group similarity measures are of great interest for many application domains, since they can be used to evaluate similarity of objects in term of the similarity of the associated sets, for example in multimedia collaborative repositories where images, videos and other multimedia are annotated with meaningful tags whose semantics reflects the collective knowledge of a community of users. The group similarity classes are formally defined and their properties are described and discussed. Experimental results, obtained in the domain of images semantic similarity by using search engine based tag similarity, show the adequacy of the proposed approach in order to reflect the collective notion of semantic similarity.
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Miller, G.A.: Wordnet: a lexical database for english. Communications of the ACM 38(11), 39–41 (1995)
Budanitsky, A., Hirst, G.: Semantic distance in wordnet: an experimental, application-oriented evaluation of five measures. In: Proceedings of Workshop on WordNet and Other Lexical Resources, p. 641. North American Chapter of the Association for Computational Linguistics, Pittsburgh (2001)
Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, pp. 448–453 (1995)
Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the 15th International Conference on Machine Learning, pp. 296–304. Morgan Kaufmann (1998)
Strube, M., Ponzetto, S.P.: WikiRelate! computing semantic relatedness using wikipedia. In: Proceedings of the Twenty-First National Conference on Artificial Intelligence. AAAI Press, July 2006
Milne, D., Witten, I.H.: An effective, low-cost measure of semantic relatedness obtained from wikipedia links. In: Proceedings of first AAAI Workshop on Wikipedia and Artificial Intelligence, WIKIAI 2008, Chicago, IL, USA (2008)
Völkel, M., Krötzsch, M., Vrandecic, D., Haller, H., Studer, R.: Semantic wikipedia. In: Proceedings of the 15th International Conference on World Wide Web, WWW 2006, pp. 585–594. ACM, New York (2006)
Wu, L., Hua, X.-S., Yu, N., Ma, W.-Y., Li, S.: Flickr distance. In: Proceedings of the 16th ACM International Conference on Multimedia, MM 2008, New York, NY, USA, pp. 31–40 (2008)
Enser, P.G., Sandom, C.J., Lewis, P.H.: Surveying the reality of semantic image retrieval. In: Bres, S., Laurini, R. (eds.) VISUAL 2005. LNCS, vol. 3736, pp. 177–188. Springer, Heidelberg (2006)
Li, X., Chen, L., Zhang, L., Lin, F., Ma, W.: Image annotation by large-scale content-based image retrieval. In: Proceedings of the 14th Annual ACM International Conference on Multimedia, pp. 607–610 (2006)
Franzoni, V., Milani, A.: PMING Distance: A collaborative semantic proximity measure. In: 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (IAT), vol. 2, pp. 442–449 (2012)
Leung, C.H., Li, Y., Milani, A., Franzoni, V.: Collective evolutionary concept distance based query expansion for effective web document retrieval. In: Murgante, B., Misra, S., Carlini, M., Torre, C.M., Nguyen, H.-Q., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) ICCSA 2013, Part IV. LNCS, vol. 7974, pp. 657–672. Springer, Heidelberg (2013)
Manning, D., Schutze, H.: Foundations of statistical natural language processing. The MIT Press, London (2002)
Turney P.: Mining the web for synonyms: PMI versus LSA on TEOFL. In Proc. ECML (2001)
Chan, A.W.S., Liu, J., et al.: Intelligent Social Media Indexing and Sharing Using an Adaptive Indexing Search Engine. ACM TIST 3(3), 47 (2012). doi:10.1145/2168752.2168761
Li, Y.X.: Semantic Image Similarity Based on Deep Knowledge for Effective Image Retrieval. Research Thesis (2014)
Cheng, V.C., Liu, J., et al.: Probabilistic Aspect Mining Model for Drug Reviews. IEEE Transactions on Knowledge and Data Engineering 99, 1 (2014). doi:10.1109/TKDE.2013.175. vol. 99, no. PrePrints, p. 1
Franzoni, V., Milani, A.: Heuristic semantic walk for concept chaining in collaborative networks. International Journal of Web Information Systems 10(1), 85–103 (2014). doi:10.1108/IJWIS-11-2013-0031
Franzoni, V., Mencacci, M., Mengoni, P., Milani, A.: Heuristics for semantic path search in Wikipedia. In: Murgante, B., et al. (eds.) ICCSA 2014, Part VI. LNCS, vol. 8584, pp. 327–340. Springer, Heidelberg (2014)
Franzoni, V., Milani, A.: Heuristic Semantic Walk. In: Murgante, B., Misra, S., Carlini, M., Torre, C.M., Nguyen, H.-Q., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) ICCSA 2013, Part IV. LNCS, vol. 7974, pp. 643–656. Springer, Heidelberg (2013)
Franzoni V., Milani A.: Semantic Context Extraction from Collaborative Networks. In: IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD), Calabria, Italy (2015)
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Franzoni, V., Leung, C.H.C., Li, Y., Mengoni, P., Milani, A. (2015). Set Similarity Measures for Images Based on Collective Knowledge. In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2015. ICCSA 2015. Lecture Notes in Computer Science(), vol 9155. Springer, Cham. https://doi.org/10.1007/978-3-319-21404-7_30
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DOI: https://doi.org/10.1007/978-3-319-21404-7_30
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