Uniform Knowledge Graph Based Semantic Collaboration Method In Semantic Web Of Things

Semantic Web of Things is the extension of Internet of Things and Semantic Web, which aims to establish an interoperable system to resolve the heterogeneity in Internet of Things and achieve the expansion of perception. The ultimate goal is to implement Semantic Co-operation. However, Semantic Web of Things both the characteristics of Internet of Things and Semantic Web, the semantic data in it perform many new data characteristics, and Se-mantic Web of Things also have the corresponding characteristics. It is necessary for these characteristics to use the corresponding methods and techniques.Knowledge Graph is a data organization and description specifications, from another perspective, it is data sets follow these specifications and manage storage according to cer-tain organizational structure. It is essentially a semantic network, which treat ontology as conceptual model and logic foundation, to organize resources in Internet and achieve the structured semantic description of objective world. Using Knowledge Graph to organize and describe the data in Semantic Web of Things, not only to meet the characteristics of se-mantic data, but also to construct a relatively completed knowledge graph and Supplement and improve the semantic data.Based on the existing research of Semantic Web of Things and semantic cooperation, this paper analysis the data characteristics in semantic environment, expand the Knowledge graph to uniform knowledge graph and propose the a uniform knowledge graph based se-mantic cooperation method. The method use uniform knowledge graph to organize and describe the knowledge and construct semantic linkage to link various data sets. It provides a good data base. On this basis, it realize the semantic cooperation. Since there are various application scenarios of semantic cooperation, this paper select user query scene to verify the proposed method. In support of uniform knowledge graph, the better analysis and un-derstand the semantics of user requirements, and provide much better results.Finally, based on the above methods, a prototype system is implemented and experi-ments is used to verify the effectiveness of the method. The results show that the prototype system reduce redundancy while taking into account the degree to meet needs of customer. At the same time, in the environment involves multiple data sources or complex query con-ditions, it is possible to maintain stable accuracy, more suitable for complex semantic net-working environment.