A knowledge graph construction method and system

Abstract

This invention discloses a method and system for constructing a knowledge graph. It extracts features from acquired multi-source operation and maintenance text data to generate context vector features, static word vector features, and statistical text features. These three types of features are then weighted and fused to generate a text representation. Temporal and causal ontology serve as the basis for weight adjustment; the greater the similarity between the multi-source operation and maintenance text data and this ontology, the greater the weight coefficient corresponding to the context vector feature. Temporal and causal ontology is a structured knowledge framework about temporal patterns and causal mechanisms. Based on the generated text representation, entities and entity relationships are identified, and then entity alignment and information fusion processing are used to generate the target result. Finally, a knowledge graph is constructed based on the target result. This invention, through multi-dimensional feature extraction combined with a weighted fusion strategy guided by temporal and causal ontology, achieves accurate identification of entities and entity relationships, thereby improving the accuracy of knowledge graph construction.

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