Chinese Geological Entity Relation Extraction Based on RoBERTa and Weighted Graph Convolutional Networks
Knowledge is the cornerstone of big data and artificial intelligence.Knowledge graphs offer interpretability and sca-lability advantages,making them crucial in intelligent systems.Intelligent decision has urgent application demand in various fields,providing decision support and application scenarios for knowledge graphs based on data analysis and reasoning.However,constructing and applying knowledge graphs face challenges due to complex domain scenarios,multi-source data,and extensive knowledge dimensions.To address the problem of incomplete domain knowledge patterns during geological domain knowledge graph construction and the limitations of existing entity relationship extraction methods in dealing with non-Euclidean data,a graph structure-based entity relationship extraction model RoGCN-ATT is proposed.This model utilizes RoBERTa-wwm-ext-large,a Chinese pre-trained model,as the sequence encoder combined with BiLSTM to capture richer semantic information.It also employs weighted graph convolutional networks along with attention mechanisms to capture structural dependency information and enhance the extraction performance of relation triplets.Experimental results show that the Fl value reaches 78.56％on the geological dataset.Compared with other models,RoGCN-ATT effectively improves the entity-relationship extraction performance and provides strong support for the construction and application of geological knowledge maps.
万方数据
维普
