Enhancing Navigability: An Algorithm for Constructing Tag Trees
By:
References
- Almoqhim, F., Millard, D.E., & Shadbolt, N. (2013). An approach to building high-quality tag hierarchies from crowdsourced taxonomic tag pairs. In A. Jatowt et al. (Eds.), Social Informatics (pp. 129–138). Berlin: Springer International Publishing.
- Agrawal, R., Gollapudi, S., Halverson, A., & Ieong, S. (2009). Diversifying search results. In Proceedings of the Second ACM International Conference on Web Search and Data Mining (pp. 5–14). New York: ACM.
- Begelman, G., Keller, P., & Smadja, F. (2006). Automated tag clustering: Improving search and exploration in the tag space. In Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland. Retrieved on February 4, 2017, from http://www.ra.ethz.ch/cdstore/www2006/www.rawsugar.com/www2006/20.pdf.
- Benz, D., Hotho, A., Stützer, S., & Stumme, G. (2010). Semantics made by you and me: Selfemerging ontologies can capture the diversity of shared knowledge. In Proceedings of the 2nd Web Science Conference (WebSci10), Raleigh, NC, USA. Retrieved on February 4, 2017, from http://journal.webscience.org/361/.
- Camiña, S.L. (2010). A comparison of taxonomy generation techniques using bibliometric methods: Applied to research strategy formulation. Retrieved on January 10, 2017, from http://dspace.mit.edu/handle/1721.1/62632.MIT.2010.
- Candan, K.S., Di Caro, L., & Sapino, M.L. (2008). Creating tag hierarchies for effective navigation in social media. In Proceedings of the 2008 ACM Workshop on Search in Social Media - SSM ’08 (pp. 75–82). New York: ACM.
- Carbonell, J., & Goldstein, J. (1998). The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 335–336). New York: ACM.
- Chen, C., Luo, P., Liu, X., & Lu, Y. (2012). The structure patterns of Web resources’ hierarchical organization (in Chinese). Journal of Library Science in China, 38(202), 72–80.
- Dellschaft, K., & Staab, S. (2006). On how to perform a gold standard based evaluation of ontology learning. In The Semantic Web-ISWC 2006 (pp. 228–241). Berlin: Springer-Verlag.
- Gemmell, J., Shepitsen, A., Mobasher, B., & Burke, R. (2008). Personalizing navigation in folksonomies using hierarchical tag clustering. In Proceedings of Data Warehousing and Knowledge Discovery (pp. 196–205). Berlin: Springer-Verlag.
- Golub, K., & Lykke, M. (2009). Automated classification of web pages in hierarchical browsing. Journal of Documentation, 65(6), 901–925.
- Halpin, H., Robu, V., & Shepherd, H. (2007). The complex dynamics of collaborative tagging. In Proceedings of the 16th International Conference on World Wide Web (pp. 211–220). New York: ACM.
- Helic, D., Trattner, C., Strohmaier, M., & Andrews, K. (2010). On the navigability of social tagging systems. In 2010 IEEE Second International Conference on Social Computing (pp. 161–168). Washington, DC: IEEE Computer Society.
- Helic, D., Strohmaier, M., Trattner, C., Muhr, M., & Lerman, K. (2011). Pragmatic evaluation of folksonomies. In Proceedings of the 20th International Conference on World Wide Web (pp. 417–426). New York: ACM.
- Helic, D., & Strohmaier, M. (2011). Building directories for social tagging systems. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management (pp. 525–534). New York: ACM.
- Heymann, P., & Garcia-Molina, H. (2006). Collaborative creation of communal hierarchical taxonomies in social tagging systems. Info Lab Technical Report 2006-10. Retrieved on January 10, 2016, from http://ilpubs.stanford.edu:8090/775/1/2006-10.pdf.
- Hirsch, J.E. (2005). An index to quantify an individual’s scientific research output. In Proceedings of the National Academy of Sciences of the United States of America, 102(46), 16569–16572.
- Huang, H., Gao, Y., Chen, L., Li, R., Chiew, K., & He, Q. (2013). Browse with a social web directory. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 865–868). New York: ACM.
- Li, R., Bao, S., Yu, Y., Fei, B., & Su, Z. (2007). Towards effective browsing of large scale social annotations. In Proceedings of the 16th International Conference on World Wide Web (pp. 943–952). New York: ACM.
- Luo, P., & Chen, C. (2013). Resource organization systems from folksonomy to hierarchical: Constructing the tag tree by exploiting clustering information (in Chinese). Journal of Library and Information Service, 57(22), 120–125.
- Markines, B., Cattuto, C., Menczer, F., Benz, D., Hotho, A., & Stumme, G. (2009). Evaluating similarity measures for emergent semantics of social tagging. In Proceedings of the 18th International Conference on World Wide Web (pp. 641–650). New York: ACM.
- Rafiei, D., Bharat, K., & Shukla, A. (2010). Diversifying web search results. In Proceedings of the 19th International Conference on World Wide Web (pp. 781–790). New York: ACM.
- Rousseau, R., & Zhao, S.X. (2015). A general conceptual framework for characterizing the ego in a network. Journal of Informetrics, 9(1), 145–149.
- Si, X., Liu, Z., & Sun, M. (2010). Explore the structure of social tags by subsumption relations. In Proceedings of the 23rd International Conference on Computational Linguistics (pp. 1011–1019). Stroudsburg, PA: Association for Computational Linguistics.
- Sinclair, J., & Cardew-Hall, M. (2008). The folksonomy tag cloud: When is it useful? Journal of Information Science, 34(1), 15–29.
- Song, Y., Qiu, B., & Farooq, U. (2011). Hierarchical tag visualization and application for tag recommendations. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management (pp. 1331–1340). New York: ACM.
- Strohmaier, M., Körner, C., & Kern, R. (2012). Understanding why users tag: A survey of tagging motivation literature and results from an empirical study. Web Semantics: Science, Services and Agents on the World Wide Web, 17, 1–11.
- Strohmaier, M., Helic, D., Benz, D., Körner, C., & Kern, R. (2012). Evaluation of folksonomy induction algorithms. ACM Transactions on Intelligent Systems and Technology (TIST), 3(4), Article No. 74.
- Suchanek, F.M., Vojnovic, M., & Gunawardena, D. (2008, October). Social tags: Meaning and suggestions. In Proceedings of the 17th ACM Conference on Information and Knowledge Management (pp. 223–232). New York: ACM.
- Tsai, F., Cheng, Y., Li, S., & Chen, C. (2009). Heuristic-based approach for constructing hierarchical knowledge structures. In Proceedings of Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (pp. 439–448). Berlin: Springer-Verlag.
- Tsui, E., Wang, W.M., Cheung, C.F., & Lau, A.S.M. (2010). A concept-relationship acquisition and inference approach for hierarchical taxonomy construction from tags. Information Processing & Management, 46(1), 44–57.
- Verma, C., Mahadevan, V., Rasiwasia, N., Aggarwal, G., Kant, R., Jaimes, A., & Dey, S. (2015). Construction and evaluation of ontological tag trees. Expert Systems with Applications, 42(24), 9587–9602.
- Wiesman, F., van den Herik, H.J., & Hasman, A. (2004). Information retrieval by metabrowsing. Journal of the American Society for Information Science and Technology, 55(7), 565–578.
- Zhao, S.X., Rousseau, R., & Ye, F.Y. (2011). h-Degree as a basic measure in weighted networks. Journal of Informetrics, 5(4), 668–677.
- Zhou, M., Bao, S., Wu, X., & Yu, Y. (2007). An unsupervised model for exploring hierarchical semantics from social annotations. In K. Aberer et al. (Eds.), The Semantic Web: The 6th International Semantic Web Conference, the 2nd Asian Semantic Web Conference, (ISWC 2007 & ASWC 2007), Busan, Korea, November 11–15, 2007 (pp. 680–693). Berlin: Springer- Verlag.
Language: English
Page range: 56 - 75
Submitted on: Nov 29, 2016
Accepted on: Feb 22, 2017
Published on: Mar 21, 2017
Published by: Chinese Academy of Sciences, National Science Library
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year
Related subjects:
© 2017 Chong Chen, Pengcheng Luo, published by Chinese Academy of Sciences, National Science Library
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.