Calculating the strength of ties of a social network in a semantic search system using hidden Markov models

The Web of information has grown to millions of independently evolved decentralized information repositories. Decentralization of the web has advantages such as no single point of failure and improved scalability. Decentralization introduces challenges such as ontological, communication and negotiation complexity. This has given rise to research to enhance the infrastructure of the Web by adding semantic to the search systems. In this research we view semantic search as an enabling technique for the general Knowledge Management (KM) solutions. We argue that, semantic integration, semantic search and agent technology are fundamental components of an efficient KM solution. This research aims to deliver a proof-of-concept for semantic search. A prototype agent-based semantic search system supported by ontological concept learning and contents annotation is developed. In this prototype, software agents, deploy ontologies to organize contents in their corresponding repositories; improve their own search capability by finding relevant peers and learn new concepts from each other; conduct search on behalf of and deliver customized results to the users; and encapsulate complexity of search and concept learning process from the users. A unique feature of this system is that the semantic search agents form a social network. We use Hidden Markov Model (HMM) to calculate the tie strengths between agents and their corresponding ontologies. The query will be forwarded to those agents with stronger ties and relevant documents are returned. We have shown that this will improve the search quality. In this paper, we illustrate the factors that affect the strength of the ties and how these factors can be used by HMM to calculate the overall tie strength.

[1]  Mark S. Granovetter.  The Strength of Weak Ties , 1973, American Journal of Sociology.

[2]  R. Hanneman.  Introduction to Social Network Methods , 2001 .

[3]  L. Baum,et al.  An inequality and associated maximization technique in statistical estimation of probabilistic functions of a Markov process , 1972 .

[4]  Eric Gilbert,et al.  Predicting tie strength with social media , 2009, CHI.

[5]  Mark S. Granovetter,et al.  Getting a Job: A Study of Contacts and Careers. , 1976 .

[6]  Huaglory Tianfield,et al.  A Study of Multi-agent System for Network Management , 2005 .

[7]  A-L Barabási,et al.  Structure and tie strengths in mobile communication networks , 2006, Proceedings of the National Academy of Sciences.

[8]  Tamás Nepusz,et al.  Measuring tie-strength in virtual social networks , 2006 .

[9]  Steven B. Andrews,et al.  Structural Holes: The Social Structure of Competition , 1995, The SAGE Encyclopedia of Research Design.

[10]  Jörg Denzinger,et al.  Being the new guy in an experienced team: enhancing training on the job , 2002, AAMAS '02.

[11]  Adolfo Guzmán-Arenas,et al.  Concept Similarity Measures the Understanding Between Two Agents , 2004, NLDB.

[12]  L. Baum,et al.  A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains , 1970 .

[13]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[14]  N. Lin,et al.  Social Resources and Strength of Ties: Structural Factors in Occupational Status Attainment , 1981, Social Capital, Social Support and Stratification.

[15]  L. Baum,et al.  Growth transformations for functions on manifolds. , 1968 .

[16]  Eric Moulines,et al.  Inference in hidden Markov models , 2010, Springer series in statistics.

[17]  Adolfo Guzmán-Arenas,et al.  Finding the Most Similar Concepts in Two Different Ontologies , 2004, MICAI.

[18]  L. Baum,et al.  An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology , 1967 .

[19]  Edmund H. Durfee,et al.  Negotiating Task Decomposition and Allocation Using Partial Global Planning , 1989, Distributed Artificial Intelligence.

[20]  Behrouz H. Far,et al.  Semantic Search based on Multi-Agent System and Social Networking , 2010 .

[21]  Leo Obrst,et al.  The Semantic Web: A Guide to the Future of XML, Web Services and Knowledge Management , 2003 .

[22]  Mark S. Granovetter.  T H E S T R E N G T H O F WEAK TIES: A NETWORK THEORY REVISITED , 1983 .

[23]  Behrouz Homayoun Far,et al.  Using social networks in resolving conflicts of concept learning process , 2010, CCECE 2010.

[24]  Tim Berners-Lee,et al.  Weaving The Web: The Original Design And Ultimate Destiny of the World Wide Web , 1999 .