Abstract
The collective intelligence that emerges from the collaboration, competition, and co-ordination among individuals in social networks has opened up new opportunities for knowledge extraction. Valuable knowledge is stored and often “hidden” in massive user contributions, challenging researchers to find methods for leveraging these contributions and unfold this knowledge. In this chapter we investigate the problem of knowledge extraction from social media. We provide background information for knowledge extraction methods that operate on social media, and present three methods that use Flickr data to extract different types of knowledge namely, the community structure of tag-networks, the emerging trends and events in users tag activity, and the associations between image regions and tags in user tagged images. Our evaluation results show that despite the noise existing in massive user contributions, efficient methods can be developed to mine the semantics emerging from these data and facilitate knowledge extraction.
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References
Ching-man, Gibbins, N., Yeung, N.S.A.: A study of user profile generation from folksonomies. In: SWKM (2008)
Au Yeung, C.m., Gibbins, N., Shadbolt, N.: Contextualising tags in collaborative tagging systems. In: HT 2009: Proceedings of the 20th ACM Conference on Hypertext and Hypermedia, pp. 251–260. ACM, New York (2009)
Aurnhammer, M., Hanappe, P., Steels, L.: Augmenting navigation for collaborative tagging with emergent semantics. In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., Aroyo, L.M. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 58–71. Springer, Heidelberg (2006)
Becker, H., Naaman, M., Gravano, L.: Learning similarity metrics for event identification in social media. In: WSDM 2010, pp. 291–300. ACM, New York (2010)
Begelman, G.: Automated tag clustering: Improving search and exploration in the tag space. In: Proc. of the Collaborative Web Tagging Workshop at WWW 2006 (2006)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems 30, 107–117 (1998)
Brooks, C.H., Montanez, N.: Improved annotation of the blogosphere via autotagging and hierarchical clustering. In: WWW 2006, pp. 625–632. ACM, New York (2006)
Carneiro, G., Chan, A.B., Moreno, P.J., Vasconcelos, N.: Supervised learning of semantic classes for image annotation and retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 394–410 (2007)
Cattuto, C.: Collaborative tagging as a complex system. talk given at internationl school on semiotic dynamics. In: Language and Complexity, Erice (2005)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), Software, available at http://www.csie.ntu.edu.tw/cjlin/libsvm
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 066,111 (2004)
Cooper, M., Foote, J., Girgensohn, A., Wilcox, L.: Temporal event clustering for digital photo collections. ACM Trans. Multimedia Comput. Commun. Appl. 1(3), 269–288 (2005)
Cour, T., Sapp, B., Jordan, C., Taskar, B.: Learning from ambiguously labeled images. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2009 (2009)
Dhillon, I.S.: Co-clustering documents and words using bipartite spectral graph partitioning. In: Proceedings of KDD 2001, San Francisco, California, pp. 269–274 (2001)
Diederich, J., Iofciu, T.: Finding communities of practice from user profiles based on folksonomies. In: Proceedings of the 1st International Workshop on Building Technology Enhanced Learning Solutions for Communities of Practice, TEL-CoPs 2006 (2006)
Dubinko, M., Kumar, R., Magnani, J., Novak, J., Raghavan, P., Tomkins, A.: Visualizing tags over time. In: Proceedings of WWW 2006, pp. 193–202. ACM, Edinburgh (2006)
Duygulu, P., Barnard, K., de Freitas, J.F.G., Forsyth, D.: Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 97–112. Springer, Heidelberg (2002)
Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database (Language, Speech, and Communication). The MIT Press, Cambridge (1998)
Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315, 972–976 (2007), http://www.psi.toronto.edu/affinitypropagation
Gemmell, J., Shepitsen, A., Mobasher, B., Burke, R.: Personalizing navigation in folksonomies using hierarchical tag clustering. In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2008. LNCS, vol. 5182, pp. 196–205. Springer, Heidelberg (2008)
Ghosh, H., Poornachander, P., Mallik, A., Chaudhury, S.: Learning ontology for personalized video retrieval. In: MS 2007: Workshop on Multimedia Information Retrieval on The Many Faces of Multimedia Semantics, pp. 39–46. ACM, New York (2007)
Giannakidou, E., Kompatsiaris, I., Vakali, A.: Semsoc: Semantic, social and content-based clustering in multimedia collaborative tagging systems. In: ICSC, pp. 128–135 (2008)
Giannakidou, E., Koutsonikola, V.A., Vakali, A., Kompatsiaris, Y.: Co-clustering tags and social data sources. In: WAIM, pp. 317–324 (2008)
Giannakidou, E., Koutsonikola, V.A., Vakali, A., Kompatsiaris, Y.: Exploring temporal aspects in user-tag co-clustering. In: Special session: Interactive Multimedia in Social Networks, WIAMIS (2010)
Halpin, H., Robu, V., Shepherd, H.: The complex dynamics of collaborative tagging. In: Proceedings of WWW 2007, pp. 211–220. ACM, New York (2007)
Hotho, A., Ja”schke, R., Schmitz, C., Stumme, G.: Trend detection in folksonomies. In: Avrithis, Y., Kompatsiaris, Y., Staab, S., O’Connor, N.E. (eds.) SAMT 2006. LNCS, vol. 4306, pp. 56–70. Springer, Heidelberg (2006)
Kennedy, L.S., Chang, S.F., Kozintsev, I.: To search or to label?: predicting the performance of search-based automatic image classifiers. In: Multimedia Information Retrieval, pp. 249–258 (2006)
Kennedy, L.S., Naaman, M., Ahern, S., Nair, R., Rattenbury, T.: How flickr helps us make sense of the world: context and content in community-contributed media collections. ACM Multimedia, 631–640 (2007)
Koutsonikola, V.A., Petridou, S., Vakali, A., Hacid, H., Benatallah, B.: Correlating time-related data sources with co-clustering. In: Bailey, J., Maier, D., Schewe, K.-D., Thalheim, B., Wang, X.S. (eds.) WISE 2008. LNCS, vol. 5175, pp. 264–279. Springer, Heidelberg (2008)
Koutsonikola, V., Vakali, A., Giannakidou, E., Kompatsiaris, I.: Clustering of social tagging system users: A topic and time based approach. In: Vossen, G., Long, D.D.E., Yu, J.X. (eds.) WISE 2009. LNCS, vol. 5802, pp. 75–86. Springer, Heidelberg (2009)
Li, F.F., Fergus, R., Perona, P.: One-shot learning of object categories. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 594–611 (2006)
Li, J., Wang, J.Z.: Real-time computerized annotation of pictures. IEEE Trans. Pattern Anal. Mach. Intell. 30(6), 985–1002 (2008), http://dx.doi.org/10.1109/TPAMI.2007.70847
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Marlow, C., Naaman, M., Boyd, D., Davis, M.: Ht06, tagging paper, taxonomy, flickr, academic article, to read. In: Hypertext, pp. 31–40 (2006)
Mezaris, V., Kompatsiaris, I., Strintzis, M.G.: Still image segmentation tools for object-based multimedia applications. IJPRAI 18(4), 701–725 (2004)
Mika, P.: Ontologies are us: A unified model of social networks and semantics. Web Semant 5(1), 5–15 (2007), http://dx.doi.org/10.1016/j.websem.2006.11.002
Nanopoulos, A., Gabriel, H.H., Spiliopoulou, M.: Spectral clustering in social-tagging systems. In: Vossen, G., Long, D.D.E., Yu, J.X. (eds.) WISE 2009. LNCS, vol. 5802, pp. 87–100. Springer, Heidelberg (2009)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026,113 (2004)
Papadopoulos, S., Kompatsiaris, Y., Vakali, A.: A graph-based clustering scheme for identifying related tags in folksonomies. In: Bach Pedersen, T., Mohania, M.K., Tjoa, A.M. (eds.) DAWAK 2010. LNCS, vol. 6263, pp. 65–76. Springer, Heidelberg (2010)
Papadopoulos, S., Vakali, A., Kompatsiaris, Y.: Community detection in collaborative tagging systems. In: Pardede, E. (ed.) Community-Built Database: Research and Development. Springer, Heidelberg (2010)
Quack, T., Leibe, B., Gool, L.J.V.: World-scale mining of objects and events from community photo collections. In: CIVR, pp. 47–56 (2008)
Rattenbury, T., Good, N., Naaman, M.: Towards automatic extraction of event and place semantics from flickr tags. In: SIGIR 2007: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 103–110. ACM, New York (2007)
Russell, T.: Cloudalicious: Folksonomy over time. In: Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 364–364. ACM, Chapel Hill, NC, USA (2006)
van de Sande, K., Gevers, T., Snoek, C.: Evaluating color descriptors for object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 99(1) (5555)
Schifanella, R., Barrat, A., Cattuto, C., Markines, B., Menczer, F.: Folks in folksonomies: social link prediction from shared metadata. In: WSDM 2010: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 271–280. ACM, New York (2010)
Scholkopf, B., Smola, A., Williamson, R., Bartlett, P.: New support vector algorithms. Neural Networks 22, 1083–1121 (2000)
Segaran, T.: Programming Collective Intelligence. O’Reilly Media Inc., Sebastopol (2007)
Shotton, J., Winn, J.M., Rother, C., Criminisi, A.: textonBoost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 1–15. Springer, Heidelberg (2006)
Simpson, E.: Clustering tags in enterprise and web folksonomies. HP Labs Techincal Reports (2008), http://www.hpl.hp.com/techreports/2008/HPL-2008-18.html
Sivic, J., Zisserman, A.: Video google: A text retrieval approach to object matching in videos. In: ICCV 2003: Proceedings of the Ninth IEEE International Conference on Computer Vision, p. 1470. IEEE Computer Society, Washington, DC, USA (2003)
Specia, L., Motta, E.: Integrating folksonomies with the semantic web. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 624–639. Springer, Heidelberg (2007)
Sun, A., Zeng, D., Li, H., Zheng, X.: Discovering trends in collaborative tagging systems. In: Yang, C.C., Chen, H., Chau, M., Chang, K., Lang, S.-D., Chen, P.S., Hsieh, R., Zeng, D., Wang, F.-Y., Carley, K.M., Mao, W., Zhan, J. (eds.) ISI Workshops 2008. LNCS, vol. 5075, pp. 377–383. Springer, Heidelberg (2008)
Sun, Y., Shimada, S., Taniguchi, Y., Kojima, A.: A novel region-based approach to visual concept modeling using web images. In: ACM Multimedia, pp. 635–638 (2008)
Swan, R., Allan, J.: Extracting significant time varying features from text. In: Proceedings of the Eighth International Conference on Information and Knowledge Management, pp. 38–45 (1999)
Torralba, A., Fergus, R., Freeman, W.T.: 80 million tiny images: A large data set for nonparametric object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 1958–1970 (2008), http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.128
Tsikrika, T., Diou, C., de Vries, A.P., Delopoulos, A.: Image annotation using clickthrough data. In: 8th ACM International Conference on Image and Video Retrieval, Santorini, Greece (2009)
Verbeek, J.J., Triggs, B.: Region classification with markov field aspect models. In: CVPR (2007)
Wu, L., Hua, X.S., Yu, N., Ma, W.Y., Li, S.: Flickr distance. ACM Multimedia, 31–40 (2008)
Wu, Z., Palmer, M.: Verm semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, New Mexiko, USA, pp. 133–138 (1994)
Xu, X., Yuruk, N., Feng, Z., Schweiger, T.A.J.: Scan: a structural clustering algorithm for networks. In: KDD 2007: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 824–833. ACM, New York (2007)
Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: A comprehensive study. Int. J. Comput. Vision 73(2), 213–238 (2007), http://dx.doi.org/10.1007/s11263-006-9794-4
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Nikolopoulos, S., Chatzilari, E., Giannakidou, E., Papadopoulos, S., Kompatsiaris, I., Vakali, A. (2011). Leveraging Massive User Contributions for Knowledge Extraction. In: Bessis, N., Xhafa, F. (eds) Next Generation Data Technologies for Collective Computational Intelligence. Studies in Computational Intelligence, vol 352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20344-2_16
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DOI: https://doi.org/10.1007/978-3-642-20344-2_16
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