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Distributed agent-mediated knowledge management (AMKM) has attracted much attention as a way to improve knowledge sharing across the world. In AMKM, many systems can potentially interact with each other and share their knowledge while keeping their own ontology such as health care systems that handle problems of distributed experience and search engines that search distributed information in the Internet. The main problem for those systems is to make the agents understand each other adequately. Concept learning is an enabling technique. However, the semantic heterogeneity problem may occur. That is, those concepts may have been defined differently in separate ontologies and conflicts become inevitable. In order to overcome the problem of semantic heterogeneity, we present a mechanism for concept learning based on social networking that can be used to effectively resolve possible conflicts that may occur during the learning process.

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 proofof-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.

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