Abstract
With the application of artificial intelligence technology in electric power industry, knowledge graph will play an important role in power grid dispatching, intelligent operation inspection, customer service question and answer, etc. The problem of low resources is very common in the construction of knowledge graph in the field of electric power operation and maintenance in real application scenarios. How to extract effective information from large-scale, small or even unlabeled power data, fully explore the value of field data, promote the efficient use of information resources, and build a knowledge graph in the power field with high efficiency and low cost is the main concern in the field of knowledge graph in the power field. Based on the knowledge graph construction method in the field of power operation and maintenance with a small sample, this paper proposes a FLAT-based entity relationship joint extraction model and an active sampling strategy based on meta-learning. The problem of knowledge graph construction in the sample domain is to realize the automatic construction and dynamic update of the knowledge graph construction in the power operation and maintenance domain.
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