Abstract

In this paper, we create an information tree pertaining to the natural user’s communication in the real world to ascertain the user’s interests. This is performed by analysing users’ twitter posts or tweets and comparing them with Wikipedia to generate a graph tree, with nodes pertaining to topics matched. The generated Lifetree is dynamic in nature and is progressed as the continuing users’ communication i.e. is appended to the Lifetree. The various uses of the Lifetree included an overall picture of particular users’ interests and further helps in event allocation, ads customization, etc... Hence, a novel approach for representing users’ data has been proposed, which makes the process of recommendation easier and more accurate. To achieve this, knowledge base and machine learning algorithms have been proposed and utilized.

References

• P. Kapanipathi, P. Jain, C. Venkataramani, and A. Sheth, “User interests identification on twitter using a hierarchical knowledge base,” in European Semantic Web Conference, 2014, pp. 99–113.
• T. Zesch and I. Gurevych, “Analysis of the Wikipedia category graph for NLP applications,” in Proceedings of the Second Workshop on TextGraphs: Graph-Based Algorithms for Natural Language Processing, 2007, pp. 1–8.
• C. Bizer et al., “DBpedia-A crystallization point for the Web of Data,” Web Semantics: science, services and agents on the world wide web, vol. 7, no. 3, pp. 154–165, 2009.
• G. Kasneci, F. Suchanek, and G. Weikum, “Yago-a core of semantic knowledge,” 2006.
• P. Ferragina and U. Scaiella, “Tagme: on-the-fly annotation of short text fragments (by wikipedia entities),” in Proceedings of the 19th ACM international conference on Information and knowledge management, 2010, pp. 1625–1628.
• E. Gabrilovich and S. Markovitch, “Computing semantic relatedness using wikipedia-based explicit semantic analysis.,” in IJcAI, 2007, vol. 7, pp. 1606–1611.
• E. Meij, W. Weerkamp, and M. De Rijke, “Adding semantics to microblog posts,” in Proceedings of the fifth ACM international conference on Web search and data mining, 2012, pp. 563–572.
• I. H. Witten and D. N. Milne, “An effective, low-cost measure of semantic relatedness obtained from Wikipedia links,” 2008.
• C. C. Aggarwal and C. Zhai, Mining text data. Springer Science & Business Media, 2012.
• J. Chen, R. Nairn, L. Nelson, M. Bernstein, and E. Chi, “Short and tweet: experiments on recommending content from information streams,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2010, pp. 1185–1194.
• J. Weng, E.-P. Lim, J. Jiang, and Q. He, “Twitterrank: finding topic-sensitive influential twitterers,” in Proceedings of the third ACM international conference on Web search and data mining, 2010, pp. 261–270.
• M. Oka, H. Abe, and K. Kato, “Extracting topics from weblogs through frequency segments,” in Proc. of the Workshop on the Weblogging Ecosystem: Aggregation, Analysis and Dynamics, 2006.
• C.-Y. Teng and H.-H. Chen, “Detection of bloggers’ interests: using textual, temporal, and interactive features,” in Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, 2006, pp. 366–369.
• Y. Cheng et al., “Model bloggers’ interests based on forgetting mechanism,” in Proceedings of the 17th international conference on World Wide Web, 2008, pp. 1129–1130.
• D. Godoy and A. Amandi, “Modeling user interests by conceptual clustering,” Information Systems, vol. 31, no. 4, pp. 247–265, 2006.
• K. Ramanathan and K. Kapoor, “Creating user profiles using wikipedia,” Conceptual Modeling-ER 2009, pp. 415–427, 2009.
• A. Sieg, B. Mobasher, and R. Burke, “Web search personalization with ontological user profiles,” in Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, 2007, pp. 525–534.
• F. Abel, Q. Gao, G.-J. Houben, and K. Tao, “Semantic enrichment of twitter posts for user profile construction on the social web,” in Extended Semantic Web Conference, 2011, pp. 375–389.
• P. Kapanipathi, F. Orlandi, A. P. Sheth, and A. Passant, “Personalized filtering of the twitter stream,” 2011.
• F. Orlandi, J. Breslin, and A. Passant, “Aggregated, interoperable and multi-domain user profiles for the social web,” in Proceedings of the 8th International Conference on Semantic Systems, 2012, pp. 41–48.
• M. Albakour, C. Macdonald, and I. Ounis, “On sparsity and drift for effective real-time filtering in microblogs,” in Proceedings of the 22nd ACM international conference on Information & Knowledge Management, 2013, pp. 419–428.
• P. Jain, P. Hitzler, A. P. Sheth, K. Verma, and P. Z. Yeh, “Ontology alignment for linked open data,” in International Semantic Web Conference, 2010, pp. 402–417.
• T. Xu and D. W. Oard, “Wikipedia-based topic clustering for microblogs,” Proceedings of the Association for Information Science and Technology, vol. 48, no. 1, pp. 1–10, 2011.
• P. Schönhofen, “Identifying document topics using the Wikipedia category network,” Web Intelligence and Agent Systems: An International Journal, vol. 7, no. 2, pp. 195–207, 2009.