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Abstract.A multi-agent system is a network individual agent that work together to achieve a goal through communication and collaboration among each other. Standardized infrastructure for information or knowledge sharing is required to make autonomous agents interdependent on each other for effective collaboration in a multi-agent system. In order to enhance productivity of knowledge workers knowledge management tools should support collaborative environments among desktop, web, and even mobile devices. The Semantic Web is the place where software agents perform various intelligent tasks using standard knowledge representational schemes that are named "ontologies." This paper presents a conceptual framework of the social knowledge activities and knowledge processes with regard to the social software agents. Our prototype, called WANT, is a wiki-based semantic tagging system for collaborative and communicative knowledge creation and maintenance by a human or software agent. It can be supported in both desktop and mobile environments.

Recently, Formal Concept Analysis (FCA) have been widely used for various purposes in many different domains such as data mining, machine learning, knowledge management and so on. In this paper, we introduce FCA as the basis for a practical and well founded methodological approach for data analysis which identifies conceptual structures among data sets. As well as, we propose a FCA-based data analysis for discovering association rules by using polarity from social contents. Additionally, we show the experiments that demonstrate how our data analysis approaches can be applied for knowledge discovery by using association rules.

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