关键词: 资源建模, 知识图谱, 服务推荐, 语义建模

Abstract: Faced with complex data, information and knowledge resources, users are easy to get lost and overloaded in the process of learning. Automated machine understanding, automated processing, automatic synthesis, and automatic analysis of natural language, such as the original representation of the resources of these data, information and knowledge, have become a huge challenge. This paper studies resource modeling, resource processing, processing optimization and resource management from the aspects of coping with automatic incremental knowledge and reducing the interaction burden of artificial experts and puts forward a three-tier solution architecture which can automatically abstract and adjust resources based on the concept extension of existing Knowledge Graph. This architecture recursively supports integration of empirical knowledge and efficient automatic semantic analysis of resource elements through frequency focused profiling on data graph and optimal search through abstraction on information graph and knowledge graph. The proposed architecture is supported by the 5W (who/when/where, what and how) to interface users?? learning requirements which can provide users with personalized learning service recommendation.

Key words: resource modeling, knowledge graph, service recommendation, semantic modeling