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Fong, A.C.M. (2015)
Languages: English
Types: Other
Integrating sensor networks and human social networks can provide rich data for many consumer applications. Conceptual analysis offers a way to reason about real-world concepts, which can assist in discovering hidden knowledge from the fused data. Knowledge discovered from such data can be used to provide mobile users with location-based, personalized and timely recommendations. Taking a multi-tier approach that separates concerns of data gathering, representation, aggregation and analysis, this paper presents a conceptual analysis framework that takes unified aggregated data as an input and generates semantically meaningful knowledge as an output. Preliminary experiments suggest that a fusion of sensor network and social media data improves the overall results compared to using either source of data alone.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] J. G. Breslin, S. Decker, M. Hauswirth, G. Hynes, D. Le Phuoc, A. Passant, A. Polleres, C. Rabsch, V. Reynolds, "Integrating social networks and sensor networks", W3C Workshop on the Future of Social Networking, 15-16 January 2009, Barcelona, Spain.
    • [2] A.C.M. Fong, B. Zhou, S.C. Hui, G.Y. Hong and T.A. Do, “Web content recommender system based on consumer behavior modeling”, IEEE Trans. Consumer Electronics, Vol. 57/2, pp. 962-969, 2011.
    • [3] A.C.M. Fong, B. Zhou, S.C. Hui, G.Y. Hong and J. Tang, “Generation of personalized ontology based on consumer emotion and behavior analysis”, IEEE Trans. Affective Computing, Vol. 3/2, pp. 152-164, April-June, 2012.
    • [4] M. Gao, V.K. Singh, and R. Jain, “Eventshop: from heterogeneous web streams to personalized situation detection and control”, Proc. 4th Annual ACM Web Science Conference WebSci '12, pp. 105-108, 2012.
    • [5] B. Ganter and R. Wille. Formal Concept Analysis: Mathematical Foundations. Springer, 1999.
    • [6] R. Kruse, E. Schwecke, J. Heinsohn. Uncertainty and vagueness in knowledge-based systems: numerical methods. Springer, 2011.
    • [7] Q.T. Tho, S.C. Hui, A.C.M. Fong and T.H. Cao. “Automatic fuzzy ontology generation for semantic web”, IEEE Trans. Knowledge and Data Engineering, Vol. 18/6, pp. 842-856, June 2006.
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