Research on water conservancy comprehensive knowledge graph construction

Based on the analysis of water conservancy knowledge characteristics, this research proposed the description method of water conservancy comprehensive knowledge graph, including the definition, composition and relevancy of water conservancy knowledge. The construction framework and key technologies of water conservancy knowledge graph were built. With the help of Bi-directional Long Shot-Term Memory Neural Network (BiLSTM) and Conditional Random Fields (CRF) models, the entities transformed from structured data were used to identify the entities in semi-structured and structured text knowledge and internet data. The relationships between entities were extracted with pattern matching and co-occurrence network analysis methods to supplement water related objects and attributes. RiMoM model was used to fuse the water conservancy entities and disciplinary knowledge. Through the graph construction, 136 million water conservancy entities were extracted and more than 300 million relationships were established. The annotation accuracy of water conservancy objects was higher than 80 percent. Based on the graph constructed in this research, both knowledge and relationships could be searched and the hidden relations between different entities could be found. © 2021, China Water Power Press. All right reserved.