A knowledge graph construction method and system

By employing a weighted fusion strategy guided by multi-dimensional feature extraction and temporal and causal ontology, the problem of low entity recognition accuracy in existing technologies is solved, thereby improving the accuracy of knowledge graphs. This has significant application value, especially in power systems.

CN122021840BActive Publication Date: 2026-06-19STATE GRID ZHEJIANG ELECTRIC POWER CO LTD +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
STATE GRID ZHEJIANG ELECTRIC POWER CO LTD
Filing Date
2026-04-13
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

In existing technologies, knowledge graph-based operation and maintenance knowledge management solutions suffer from low entity recognition accuracy due to the reliance on single feature extraction methods, which in turn affects the accuracy of the knowledge graph.

Method used

A multi-dimensional feature extraction method is adopted, combined with a weighted fusion strategy guided by temporal and causal ontology. By acquiring multi-source operation and maintenance text data, context vector features, static word vector features, and statistical text features are generated and weighted fused to identify entities and entity relationships, and finally construct a knowledge graph.

Benefits of technology

It enables accurate identification of entities and their relationships, improves the accuracy of knowledge graph construction, and supports intelligent decision-making and operation and maintenance applications in power systems.

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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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