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A graph-based knowledge representation for intelligent fault diagnosis and early-warning knowledge base

This paper aims to effectively utilize the vast amounts of data generated by data centers, which are used to support fault diagnosis and early warning functions. Due to the large volume and complexity of data, as well as the semantic relationship among data, in this paper, we adopt knowledge graph technology to extract, fuse, process, and update the knowledge in data centers. Then, we provide a feasible method for constructing a knowledge graph for fault diagnosis and early warning in a data center by describing the correlation of data, reasoning and analyzing the data on a reasonable basis. In addition, we also discuss how knowledge is represented.