Knowledge Integration Based On Semantic Web And Social Network Analysis And Recommendation

Knowledge is so important to a corporation. Both the explicit knowledge and the tacit knowledge are essential sources of the competitive power of a corporation. Knowledge integration and knowledge recommendation are two important parts in a corporation's knowledge management. The decentralization of inter-organizational knowledge sources and the differences in knowledge representation lead to the knowledge integration problem. The lackness of the solution to access the suitable knowledge location instead of the the lackness of knowledge leads to the knowledge location problem, which can be solved by the knowledge recommendation technique.This work proposed a solution based on the concept of "relationship" to solve these two problems, which is the semantic web technology targeting the explicit knowledge and social network analysis (SNA) targeting the tacit knowledge. The semantic web techonology proposed a fine definition of the internet sources and built a perfect platform for the inter-organizational knowledge integration. Meanwhile, SNA can solve the explicit knowledge management problem in the business activities, and promote inter-organizational knowledge recommendation.Therefore, based on the semantic web technology, the knowledge integration theory, social network analysis, the transitive memory system and the knowledge recommendation theory, this work analized the demands of knowledge integration and knowledge recommendation between organizations and propose a uniform framework based on "relationship" to manage the tacit knowledge and the explicit knowledge. As to the explicit knowledge, this work proposed a formalized knowledge integration definition based on the semantic web environment, and testified that this knowledge integration process can produce incremental knowledge. As to the tacit knowledge, this work proposed an index system of expert recommendation, gave the measurance methods of these indexes and realized the prototype system by the Tag technology of Web 2.0 environment.The future work will be focused on the following aspects:(1) simplify the building and registing methods of ontology in the explicit knowledge integration; (2) improve the readability of the explicit knowledge integration result; (3) make further efforts to prove the rationality in theory of index system of the tacit knowledge recommendation.This paper is supported by the National Natural Science Foundation of China (No.70871027).