An event knowledge graph system for the operation and maintenance of power equipment

Abstract

Abstract Power equipment operation and maintenance (O&M) requires plenty of domain knowledge to improve equipment security and power grid reliability. However, most knowledge is implicitly represented by the semantic text, which is hard to be comprehended by algorithms and restricts the intelligence level of equipment O&M. Therefore, this article proposes an event knowledge graph system to automatically extract event knowledge from O&M reports and represent the knowledge by an event knowledge graph (KG). First, a bidirectional long short‐term memory network (BiLSTM) combined with conditional random field (CRF) is employed to recognize the entities. Next, an attention‐based sequence‐to‐sequence model is proposed to detect multiple events and extract document‐level representation vectors. Then, a tree‐based table filling strategy is utilized to complete the artificially designed event tables. Finally, the proposed system renders the event tables into an event KG. Verified by the experiments, the proposed system outperforms the baselines with the F‐measure of 83.5% for event extraction and 94.2% for event detection. When dealing with coexisting events and scattering arguments, the F‐measure of the proposed system achieves 74.8% and 79.0%, respectively. Moreover, the proposed system has positive effects on intelligent retrieval, fault diagnosis, and condition assessment, verifying its potential value for equipment O&M.