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A knowledge graph construction method for compliance review of water conservancy project reports
Zelin Ding1, Zhefei Fan1, Yuanfeng Hao2
1North China University of Water Resources and Electric Power, Zhengzhou City, Henan Province, China.
This study introduces a knowledge graph method to improve water conservancy report compliance reviews. It enhances efficiency and accuracy for "Smart Water Conservancy" digital transformation.
Area of Science:
- Civil Engineering
- Computer Science
- Information Science
Background:
- Manual compliance reviews for water conservancy projects face efficiency and accuracy challenges.
- Digital transformation in water conservancy requires advanced data processing methods.
Purpose of the Study:
- To develop a knowledge graph construction method for automated compliance review of water conservancy project reports.
- To enhance the digital transformation of "Smart Water Conservancy" initiatives.
Main Methods:
- Utilized Natural Language Processing (NLP) with BERT-BiLSTM-CRF for named entity recognition of key parameters and clauses.
- Employed Context-Free Grammar (CFG) for relationship parsing and semantic labeling to create structured knowledge triples.
- Implemented Neo4j graph database and Py2neo toolkit for efficient storage and dynamic updates of the knowledge graph.
Main Results:
- The proposed method accurately identifies engineering parameters and normative clauses.
- Structured knowledge representation of water conservancy information was achieved.
- Demonstrated effective storage and dynamic updating of the knowledge graph using Neo4j and Py2neo.
Conclusions:
- The knowledge graph method significantly improves the efficiency and accuracy of water conservancy project report compliance reviews.
- Provides technical support for the digital transformation of water conservancy projects.
- Contributes to the intelligent development of the water conservancy industry.
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