Open AccessKnowledge Modeling of power grid regulation based on reasoning map
Author(s) -
Yang Yang,
Yifan Huang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2087/1/012097
Subject(s) - computer science , event (particle physics) , knowledge representation and reasoning , theoretical computer science , artificial intelligence , predicate (mathematical logic) , data mining , physics , quantum mechanics , programming language
To contribute to the intelligence and knowledge of power grid regulation and control operations, this paper presents a method of power grid regulation knowledge modeling based on ELG (Event Logic Graph), which includes an event word extraction based on a predicate-argument model, an event chain extraction and fusion based on event similarity theory, an event generalization based on a soft-pattern algorithm, and an event relationship recognition based on rule pattern matching method and joint constraints. Finally, this paper uses events as nodes and event relationships as directed edges to construct an affair graph stipulated by the power grid regulation and control regulations. The ELG is also called the new generation knowledge graph. But the knowledge graph can only describe the existence of entities and the upper and lower associations between entities. ELG can explain the inheritance, causality between entities and the logic of affair evolution, and the probability of transition between legacy and causality. Therefore, knowledge modeling based on ELG has intelligent advantages. Also, compared with ontology-based knowledge modeling methods, the method proposed in this paper can realize the dynamic representation of control operation knowledge, can express the logic of behavior and logic of operation, and also has higher retrieval accuracy.