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A Semantic-Based Strategy to Model Multimedia Social Networks

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Transactions on Large-Scale Data- and Knowledge-Centered Systems XLVII

Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 12630))

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Abstract

The social facet of information has a deciding role in our quotidian life. An abstract representation and a proper management of Online Social Networks (OSNs) constitute a new challenge for communities of researchers. In addition, the need of extending OSNs to Multimedia Social Networks (MSNs)come from the fact that the vast majority of data is unstructured and heterogeneous, making the reuse and integration of information effortful. In this chapter we propose a general high-level model to represent and manage MSNs. Our approach is based on property graph represented by a hypergraph structure due to the intrinsic multidimensional nature of social networks and semantic relations to better represent the networks contents. Using the proposed graph structure is helpful to single out several levels of knowledge analysing the relationships defined between nodes of the same or different type. Moreover, the introduction of low-level multimodal features and a formalization of their semantic meanings give a more comprehensive view of the social network structure and content. Using this approach we call the represented network Multimedia Semantic Social Networks (\(MS^2N\)). The proposed data model could be useful for several applications and we propose a case study on cultural heritage domain.

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References

  1. Anandkumar, A., Sedghi, H.: Learning mixed membership community models in social tagging networks through tensor methods. arXiv preprint arXiv:1503.04567 (2015)

  2. Arndt, R., Troncy, R., Staab, S., Hardman, L.: COMM: a core ontology for multimedia annotation. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies. IHIS, pp. 403–421. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-92673-3_18

    Chapter  Google Scholar 

  3. Batko, M., et al.: Building a web-scale image similarity search system. Multimed. Tools Appl. 47(3), 599–629 (2010)

    Article  MathSciNet  Google Scholar 

  4. Benevenuto, F., Rodrigues, T., Cha, M., Almeida, V.: Characterizing user behavior in online social networks. In: Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference. pp. 49–62. ACM (2009)

    Google Scholar 

  5. Bergs, A.: Social networks and historical sociolinguistics: studies in morphosyntactic variation in the Paston letters (1421–1503), vol. 51. Walter de Gruyter (2005)

    Google Scholar 

  6. Berners-Lee, T., Hendler, J., Lassila, O., et al.: The semantic web. Sci. Am. 284(5), 28–37 (2001)

    Article  Google Scholar 

  7. Bliemel, M.J., McCarthy, I.P., Maine, E.: An integrated approach to studying multiplexity in entrepreneurial networks. Entrepreneurship Res. J. 4(4), 367–402 (2014)

    Article  Google Scholar 

  8. Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval. pp. 401–408. ACM (2007)

    Google Scholar 

  9. Brass, D.J., Butterfield, K.D., Skaggs, B.C.: Relationships and unethical behavior: a social network perspective. Acad. Manage. Rev. 23(1), 14–31 (1998)

    Article  Google Scholar 

  10. Bu, J., Tan, S., Chen, C., Wang, C., Wu, H., Zhang, L., He, X.: Music recommendation by unified hypergraph: combining social media information and music content. In: Proceedings of the 18th ACM International Conference on Multimedia. pp. 391–400. ACM (2010)

    Google Scholar 

  11. Caldarola, E., Picariello, A., Rinaldi, A.: Experiences in wordnet visualization with labeled graph databases. Commun. Comput. Inf. Sci. 631, 80–99 (2016)

    Google Scholar 

  12. Caldarola, E., Rinaldi, A.: Big data visualization tools: a survey: the new paradigms, methodologies and tools for large data sets visualization. In: DATA 2017 - Proceedings of the 6th International Conference on Data Science, Technology and Applications. pp. 296–305 (2017)

    Google Scholar 

  13. Caldarola, E., Rinaldi, A.: A multi-strategy approach for ontology reuse through matching and integration techniques. Adv. Intell. Syst. Comput. 561, 63–90 (2018)

    Google Scholar 

  14. Caldarola, E.G., Picariello, A., Rinaldi, A.M.: An approach to ontology integration for ontology reuse in knowledge based digital ecosystems. In: Proceedings of the 7th International Conference on Management of Computational and Collective Intelligence in Digital EcoSystems. pp. 1–8. ACM (2015)

    Google Scholar 

  15. Caldarola, E.G., Picariello, A., Rinaldi, A.M.: Big graph-based data visualization experiences: The wordnet case study. In: 2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K), vol. 1, pp. 104–115. IEEE (2015)

    Google Scholar 

  16. Caldarola, E.G., Rinaldi, A.M.: An approach to ontology integration for ontology reuse. In: 2016 IEEE 17th International Conference on Information Reuse and Integration (IRI), pp. 384–393. IEEE (2016)

    Google Scholar 

  17. Caldarola, E.G., Rinaldi, A.M.: Modelling multimedia social networks using semantically labelled graphs. In: 2017 IEEE International Conference on Information Reuse and Integration (IRI) pp. 493–500 (2017)

    Google Scholar 

  18. Caldarola, E.G., Rinaldi, A.M.: Improving the visualization of wordnet large lexical database through semantic tag clouds. In: 2016 IEEE International Congress on Big Data (BigData Congress), pp. 34–41. IEEE (2016)

    Google Scholar 

  19. Caldarola, E.G., Rinaldi, A.M.: Big data: A survey-the new paradigms, methodologies and tools. In: DATA. pp. 362–370 (2015)

    Google Scholar 

  20. Chang, S.F., Sikora, T., Purl, A.: Overview of the mpeg-7 standard. IEEE Trans. Circuits Syst. Video Technol. 11(6), 688–695 (2001)

    Article  Google Scholar 

  21. Chen, B., Wang, J., Huang, Q., Mei, T.: Personalized video recommendation through tripartite graph propagation. In: Proceedings of the 20th ACM International Conference on Multimedia. pp. 1133–1136. ACM (2012)

    Google Scholar 

  22. Cheng, Y., Park, J., Sandhu, R.: Preserving user privacy from third-party applications in online social networks. In: Proceedings of the 22nd International Conference on World Wide Web. pp. 723–728. WWW 2013 Companion, ACM, New York, USA (2013). https://doi.org/10.1145/2487788.2488032

  23. Cireşan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 411–418. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_51

    Chapter  Google Scholar 

  24. Danesi, M., Perron, P.: Analyzing Cultures. Indiana University Press, Bloomington, Indiana, USA (1999)

    Google Scholar 

  25. Deselaers, T., Keysers, D., Ney, H.: Features for image retrieval: an experimental comparison. Inf. Retrieval 11(2), 77–107 (2008)

    Article  Google Scholar 

  26. Erhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2147–2154 (2014)

    Google Scholar 

  27. Gao, J., Liang, F., Fan, W., Sun, Y., Han, J.: A graph-based consensus maximization approach for combining multiple supervised and unsupervised models. IEEE Trans. Knowl. Data Eng. 25(1), 15–28 (2013). https://doi.org/10.1109/TKDE.2011.206

    Article  Google Scholar 

  28. Ghali, N., Panda, M., Hassanien, A.E., Abraham, A., Snasel, V.: Social networks analysis: tools, measures and visualization, pp. 3–23. Springer, London (2012) https://doi.org/10.1007/978-1-4471-4054-2_1

  29. Ghosh, R., Lerman, K.: Parameterized centrality metric for network analysis. Phys. Rev. E 83(6), 066118 (2011)

    Article  Google Scholar 

  30. Huang, J., Kumar, S.R., Mitra, M., Zhu, W.J., Zabih, R.: Image indexing using color correlograms. In: Proceedings., 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 762–768. IEEE (1997)

    Google Scholar 

  31. Hunter, J.: Enhancing the semantic interoperability of multimedia through a core ontology. IEEE Trans. Circuits Syst. Video Technol. 13(1), 49–58 (2003). https://doi.org/10.1109/TCSVT.2002.808088

    Article  Google Scholar 

  32. Hunter, J.: Adding multimedia to the semantic web: Building an mpeg-7 ontology. In: Proceedings of the First International Conference on Semantic Web Working. pp. 261–283. CEUR-WS. org (2001)

    Google Scholar 

  33. Ji, X., Wang, Q., Chen, B.W., Rho, S., Kuo, C.J., Dai, Q.: Online distribution and interaction of video data in social multimedia network. Multimed. Tools Appl. 75(20), 12941–12954 (2016)

    Article  Google Scholar 

  34. Jin, X., Luo, J., Yu, J., Wang, G., Joshi, D., Han, J.: Reinforced similarity integration in image-rich information networks. IEEE Trans. Knowl. Data Eng. 25(2), 448–460 (2013)

    Article  Google Scholar 

  35. Kannan, P., Bala, P.S., Aghila, G.: A comparative study of multimedia retrieval using ontology for semantic web. In: IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM -2012). pp. 400–405 (2012)

    Google Scholar 

  36. Kompatsiaris, I., Avrithis, Y., Hobson, P., Strintzis, M.G.: Integrating knowledge, semantics and content for user-centred intelligent media services: The acemedia project. In: Proceedings of Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS 2004. pp. 21–23 (2004)

    Google Scholar 

  37. Lee, M., Kim, M., Yeom, J., Lee, K., Suh, Y., Kim, H., Cho, J.: Ontological knowledge base-driven framework for semantic multimedia contents retrieval. In: 2012 14th International Conference on Advanced Communication Technology (ICACT). pp. 1304–1309 (Feb 2012)

    Google Scholar 

  38. Li, L., Li, T.: News recommendation via hypergraph learning: encapsulation of user behavior and news content. In: Proceedings of the sixth ACM International Conference on Web Search and Data Mining. pp. 305–314. ACM (2013)

    Google Scholar 

  39. Li, Q., Lu, Z., Yu, Y., Liang, L.: Multimedia ontology modeling: An approach based on mpeg-7. In: 2011 3rd International Conference on Advanced Computer Control. pp. 351–356 (2011). https://doi.org/10.1109/ICACC.2011.6016430

  40. Liu, D., Ye, G., Chen, C.T., Yan, S., Chang, S.F.: Hybrid social media network. In: Proceedings of the 20th ACM International Conference on Multimedia. pp. 659–668. ACM (2012)

    Google Scholar 

  41. Lu, C., Hu, X., Park, J.R.: Exploiting the social tagging network for web clustering. IEEE Trans. Syst. Man, and Cybernetics-Part A: Systems and Humans 41(5), 840–852 (2011)

    Article  Google Scholar 

  42. Lv, Q., Charikar, M., Li, K.: Image similarity search with compact data structures. In: Proceedings of the Thirteenth ACM International Conference on Information and Knowledge Management. pp. 208–217 (2004)

    Google Scholar 

  43. Madani, K., Russo, C., Rinaldi, A.: Merging large ontologies using bigdata graphdb. In: Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. pp. 2383–2392 (2019)

    Google Scholar 

  44. McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D., Barton, D.: Big data: the management revolution. Harvard Business Rev. 90(10), 60–68 (2012)

    Google Scholar 

  45. Mika, P.: Ontologies are us: A unified model of social networks and semantics. 5, 5–15 (2007)

    Google Scholar 

  46. Milroy, L., Milroy, J.: Social network and social class: toward an integrated sociolinguistic model. Lang. Soc. 21(1), 1–26 (1992)

    Article  Google Scholar 

  47. O’Donovan, F.T., Fournelle, C., Gaffigan, S., Brdiczka, O., Shen, J., Liu, J., Moore, K.E.: Characterizing user behavior and information propagation on a social multimedia network. In: Multimedia and Expo Workshops (ICMEW), 2013 IEEE International Conference on. pp. 1–6. IEEE (2013)

    Google Scholar 

  48. Ohm, J.-R.: The mpeg-7 visual description framework — concepts, accuracy, and applications. In: Skarbek, W. (ed.) CAIP 2001. LNCS, vol. 2124, pp. 2–10. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44692-3_2

    Chapter  MATH  Google Scholar 

  49. Pino, C., Di Salvo, R.: A survey of semantic multimedia retrieval systems. In: Proceedings of the 13th WSEAS International Conference on Mathematical and Computational Methods in Science and Engineering. pp. 353–358. MACMESE 2011, World Scientific and Engineering Academy and Society (WSEAS), Stevens Point, Wisconsin, USA (2011)

    Google Scholar 

  50. Qi, G.J., Aggarwal, C., Tian, Q., Ji, H., Huang, T.: Exploring context and content links in social media: a latent space method. IEEE Trans. Pattern Anal. Mach. Intell. 34(5), 850–862 (2012)

    Article  Google Scholar 

  51. Qi, G.J., Aggarwal, C.C., Huang, T.S.: On clustering heterogeneous social media objects with outlier links. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining. pp. 553–562. ACM (2012)

    Google Scholar 

  52. Rinaldi, A., Russo, C.: A matching framework for multimedia data integration using semantics and ontologies. In: Proceedings - 12th IEEE International Conference on Semantic Computing, ICSC 2018. vol. 2018-January, pp. 363–368 (2018)

    Google Scholar 

  53. Rinaldi, A., Russo, C.: A semantic-based model to represent multimedia big data. In: MEDES 2018–10th International Conference on Management of Digital EcoSystems. pp. 31–38 (2018)

    Google Scholar 

  54. Rinaldi, A., Russo, C.: User-centered information retrieval using semantic multimedia big data. In: Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. pp. 2304–2313 (2019)

    Google Scholar 

  55. Rinaldi, A., Russo, C., Madani, K.: A semantic matching strategy for very large knowledge bases integration. Int. J. Inf. Technol. Web. Eng. 15(2), 1–29 (2020)

    Article  Google Scholar 

  56. Rinaldi, A.M.: A multimedia ontology model based on linguistic properties and audio-visual features. Inf. Sci. 277, 234–246 (2014)

    Article  Google Scholar 

  57. Rinaldi, A.M.: Using multimedia ontologies for automatic image annotation and classification. In: 2014 IEEE International Congress on Big Data (BigData Congress), pp. 242–249. IEEE (2014)

    Google Scholar 

  58. Schreiber, A.T., Dubbeldam, B., Wielemaker, J., Wielinga, B.: Ontology-based photo annotation. IEEE Intell. Syst. 16(3), 66–74 (2001)

    Article  Google Scholar 

  59. Sokhn, M., Mugellini, E., Khaled, O.A., Serhrouchni, A.: End-to-end adaptive framework for multimedia information retrieval. In: Masip-Bruin, X., Verchere, D., Tsaoussidis, V., Yannuzzi, M. (eds.) WWIC 2011. LNCS, vol. 6649, pp. 197–206. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21560-5_17

    Chapter  Google Scholar 

  60. Straccia, U.: An ontology mediated multimedia information retrieval system. In: 2010 40th IEEE International Symposium on Multiple-Valued Logic (ISMVL), pp. 319–324. IEEE (2010)

    Google Scholar 

  61. Szegedy, C., Toshev, A., Erhan, D.: Deep neural networks for object detection. In: Advances in Neural Information Processing Systems. pp. 2553–2561 (2013)

    Google Scholar 

  62. Tousch, A.M., Herbin, S., Audibert, J.Y.: Semantic hierarchies for image annotation: A survey. Pattern Recogn. 45(1), 333–345 (2012). https://doi.org/10.1016/j.patcog.2011.05.017,

  63. Trudgill, P.: Investigations in sociohistorical linguistics: Stories of colonisation and contact. Cambridge University Press (2010)

    Google Scholar 

  64. Wang, P., Smeaton, A.F.: Semantics-based selection of everyday concepts in visual lifelogging. Int. J. Multimed. Inf. Retri. 1(2), 87–101 2012). https://doi.org/10.1007/s13735-012-0010-8

  65. Xie, J., Kelley, S., Szymanski, B.K.: Overlapping community detection in networks: The state-of-the-art and comparative study. ACM Comput. Surv. 45(4), 1–35 (2013). https://doi.org/10.1145/2501654.2501657

  66. Zhang, Z., Wang, K.: A trust model for multimedia social networks. Soc. Netw. Anal. Mining 3(4), 969–979 (2013)

    Article  Google Scholar 

  67. Zhu, Z., Su, J., Kong, L.: Measuring influence in online social network based on the user-content bipartite graph. Comput. Hum. Behav. 52, 184–189 (2015)

    Article  Google Scholar 

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Madani, K., Rinaldi, A.M., Russo, C. (2021). A Semantic-Based Strategy to Model Multimedia Social Networks. In: Hameurlain, A., Tjoa, A.M., Chbeir, R. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XLVII. Lecture Notes in Computer Science(), vol 12630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-62919-2_2

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