GridOnto: Knowledge Representation and Extraction for Fault Events in Power Grid

Abstract

An accurate and comprehensive fault knowledge representation is indispensable for an automated and intelligent processing of power grid failures. Current knowledge graphs are incapable of capturing the complex relations among power grid failures. This paper extends the current knowledge graph representation mechanism through temporal, spatial, and causal representations to facilitate the knowledge representation of power grid faults, allowing for ontology modeling of power event elements and event relationships. During the modeling process, this paper proposes an extraction method which includes Bi-directional Encoder Representation from Transformers (BERT), Bi-directional Long Short-Term Memory (BiLSTM), and Conditional Random Field (CRF) for entities and relationships in power grid faults. The innovation of the method lies in the clever combination of the three, BERT learning semantic representation, BiLSTM further learning semantic features, and CRF joint modeling of labels to improve accuracy, and the results verify the effectiveness and practicality of the method presented in this paper.

Keywords

• Deep learning
• event representation
• knowledge graph
• power grid