An Integrated Approach to Drive Ontological Structure from Folksonomie

Автор: Zahia Marouf, Sidi Mohamed Benslimane

Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs

Статья в выпуске: 12 Vol. 6, 2014 года.

Бесплатный доступ

Web 2.0 is an evolution toward a more social, interactive and collaborative web, where user is at the center of service in terms of publications and reactions. This transforms the user from his old status as a consumer to a new one as a producer. Folksonomies are one of the technologies of Web 2.0 that permit users to annotate resources on the Web. This is done by allowing users to use any keyword or tag that they find relevant. Although folksonomies require a context-independent and inter-subjective definition of meaning, many researchers have proven the existence of an implicit semantics in these unstructured data. In this paper, we propose an improvement of our previous approach to extract ontological structures from folksonomies. The major contributions of this paper are a Normalized Co-occurrences in Distinct Users (NCDU) similarity measure, and a new algorithm to define context of tags and detect ambiguous ones. We compared our similarity measure to a widely used method for identifying similar tags based on the cosine measure. We also compared the new algorithm with the Fuzzy Clustering Algorithm (FCM) used in our original approach. The evaluation shows promising results and emphasizes the advantage of our approach.

Еще

Folksonomies, Collaborative Tagging, Ontologies, Fuzzy Clustering, Similarity Measure

Короткий адрес: https://sciup.org/15012202

IDR: 15012202

Список литературы An Integrated Approach to Drive Ontological Structure from Folksonomie

  • Zimmer, M., Preface: ‘Critical Perspectives on Web 2.0." First Monday, 13(3), 2008.
  • Vander, T: Folksonomy Coinage and Definition, http://www.vanderwal.net/folksonomy.html.
  • Mika. P., Ontologies are us: A unified model of social networks and semantics. In International Semantic Web Conference, LNCS, pages 522–536. Springer, 2005.
  • Hamasaki, M., Matsuo, Y., Nisimura, T. & Takeda, H. Ontology Extraction using Social Network. In International Workshop on Semantic Web for Collaborative Knowledge Acquisition, India, 2007.
  • Begelman G., Keller P., & Smadja F. Automated tag clustering: Improving search and exploration in the tag space. In 15th International World Wide Web Conference, Edinburgh, Scotland, 2006.
  • Kennedy L., Naaman M., Ahern S., Nair R., & Rattenbury T. 2007 How Flickr Helps us Make Sense of the World: Context and Content in Community-Contributed Media Collections. In Proceedings of ACM Multimedia, Augsburg, Germany. 2007.
  • Heymann. P.,and Garcia-Molina. H. Collaborative creation of communal hierarchical taxonomies in social tagging systems. Technical Report 2006-10, Computer Science Department, April 2006.
  • Benz, D., Hotho, A., Stumme, G.: Semantics Made by You and Me: Self-emerging Ontologies can Capture the Diversity of Shared Knowledge. In: Proceedings of the 2nd Web Science Conference (WebSci10), USA. 2010.
  • Marouf, Z., Benslimane, S. M. fuzzy clustering-based approach to derive hierarchical structures from folksonomies. International conference on computer systems and applications, AICCSA 2013, Maroc.
  • Maedche, A., &Staab, S. Ontology Learning for the Semantic Web. IEEE Intelligent Systems 16(2), 72-79. 2001.
  • Bezdek, J.C. Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York. 1981.
  • Schmitz, C., Hotho,A., Jaschke, R., and Stumme .G.: Mining association rules in folksonomies. In the Proc. of the 10th IFCS Conf., Studies in Classification, Data Analysis, and Knowledge Organization, pages 261–270, Berlin, Heidelberg, 2006.
  • Jäschke, R., Hotho, A., Schmitz, C., Ganter, B., Stumme, G., Discovering shared conceptualizations in folksonomies. In Journal of Web Semantics 6(1), 38-53. 2008.
  • Lehmann. F., Wille. F., R., A triadic approach to formal concept analysis, in: G. Ellis, R. Levinson, W. Rich, J. F. Sowa (eds.), Conceptual structures: applications, implementation and theory, vol. 954 of Lecture Notes in Artificial Intelligence, Springer Verlag, 1995.
  • Trabelsi. C., Jelassi. N. and Ben Yahia. S. Scalable Mining of Frequent Tri-concepts from Folksonomies. The 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining,pp 231-242, 2012.
  • Angeletou, S., Sabou, M. & Motta, E. Semantically Enriching Folksonomies with FLOR. In 1stInternational Workshop on Collective Semantics: Collective Intelligence & the Semantic Web (CISWeb 2008), Tenerife, Spain. 2008.
  • Cantador, I., Szomszor, M., Alani, H., Fernandez, M. & Castells, P., Enriching Ontological User Profiles with Tagging History for Multi-Domain Recommendations. In 1st International Workshop on Collective Semantics, (CISWeb 2008), Tenerife, Spain, 2008
  • Garcia-Silva, A., Szomszor, M., Alani, H., &Corcho, O., Preliminary Results in Tag Disambiguation using DBpedia. In 1st International Workshop in Collective Knowledge Capturing and Representation (CKCaR09), California, USA, 2009.
  • Auer S., Bizer C., Kobilarov G., Lehmann. J., Cyganiak R., and Ives. Z.,DBpedia: A Nucleus for a Web of Open Data. 6th International Semantic Web Conference, 2007.
  • Djuana, E., Xu, Y., Li, Y., Learning Personalized Tag Ontology from User Tagging Information. Conferences in Research and Practice in Information Technology (CRPIT), Australia, 2012.
  • Giannakidou, E., Koutsonikola, V., Vakali, A., &Kompatsiaris, Y. Co-Clustering Tags and Social Data Sources. In Proc. 9th International Conference on Web-Age Information Management, 2008.
  • Specia, L., Motta, E.: Integrating Folksonomies with the Semantic Web. In: 4th European Semantic Web Conference, pp. 624-639. 2007
  • Lin, H., Davis, J. and Zhou, Y., An integrated approach to extracting ontological structures from folksonomies. In Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications, page 668. Springer, 2009.
  • Schmitz, P.: Inducing ontology from Flickr tags. Collaborative Web Tagging Workshop, 15th WWW Conference, Edinburgh, 2006.
  • Markines, B., Cattuto, C., Menczer, F., Benz, D., Hotho, A., Stumme, G.: Evaluating Similarity Measures for Emergent Semantics of Social Tagging. In: 18t International Conference on World Wide Web, pp. 641-650. 2009.
  • Angeletou, S., Sabou, M., Motta, E.: Improving Folksonomies Using Formal Knowledge: A Case Study on Search. In: 4th Asian Semantic Web Conference, pp. 276-290. 2009.
  • Weinberger, K. Q., Slaney, M., Van Zwol, R.: Resolving Tag Ambiguity. ACM Multimedia, page 111-120. ACM, (2008).
  • Shepitsen, A., Gemmell, J., Mobasher, B., Burke, R. 2008. Personalized Recommendation in Social Tagging Systems using Hierarchical Clustering. ACM Conf. on Recommender Systems, pp 259-266. 2008.
  • Au Yeung, C. M., Gibbins, N., Shadbolt, N.: Contextualising Tags in Collaborative Tagging Systems. In: 20th Conference on Hypertext and Hypermedia, pp. 251-260. 2009.
  • Pantel, P., and Lin, D,. Document clustering with committees. In Proc. of SIGIR’02, Tampere, Finland, 2002.
  • Strohmaier, M. Helic, D. Benz, D. Körner, C. and Kern. R. Evaluation of folksonomy induction algorithms. ACM Trans. Intell. Syst. Technol., 2012
  • Hoser, B., Hotho, A., Jaschke, R., Schmitz, C., Stumme G., Semantic network analysis of ontologies. In European Semantic Web Conference, Budva, Montenegro, June 2006.
  • Kavitha, A., Rajkumar, N., and Victor, S.P., An Integrated Approach for Measuring Semantic Similarity Between Words and Sentences Using Web Search Engine. The International Journal of Information Technology & Computer Science (IJITCS), 9(3), 68-78.2013.
Еще
Статья научная