Multiple Knowledge Graphs Semantic Interaction based on Graph Embedding and Matching Processing

With the rapid development of natural language processing technology, various fields are constructing domain knowledge graphs and trying to integrate multi-domain knowledge graphs to better grasp the information of each domain. Aiming at this trend, we proposed the GE-CSM model, which is a novel model that combines graph embedding and context semantic matching. By combining entity-relationship graph embedding and context semantic matching, the model is able to understand the relationships between entities more accurately and provide more semantic information based on the context. Finally, through experimental validation, the effectiveness of the model in performing multiple knowledge graph integration is demonstrated, and the response time and semantic richness of the downstream Q&A task are improved, proving its potential in enhancing semantic interaction performance.