Graph Neural Network-Based Entity Extraction and Relationship Reasoning in Complex Knowledge Graphs

The emergence of graph neural networks (GNNs) provides a new technical approach for entity extraction and relationship In the field of natural language processing (NLP) and reasoning in knowledge graphs. GNNs can effectively capture artificial intelligence, entity extraction and relationship the complex dependencies between nodes (entities) and edges reasoning are key tasks in text understanding [1]. With the (relationships) in the graph through multi-layer graph continuous growth of data scale and the increase in complexity, convolutions, thereby providing more accurate contextual traditional rule-based and statistical models seem to be unable information for reasoning. Unlike traditional sequence models, to cope with real-world problems. The introduction of GNNs can propagate information in the global graph structure, knowledge graphs provides new possibilities for the structured so that the representation of each entity can comprehensively expression of information, and the development of graph neural consider the information of its surrounding nodes [7]. In networks (GNNs) has further promoted research in this field addition, GNNs perform well in processing heterogeneous [2]. Knowledge graphs based on a graph neural network can graphs and multi-relational data, and can adapt to many effectively capture complex dependencies and structured different types of relationships and entity types. By introducing information between entities, providing a new perspective and graph neural networks, researchers can build more intelligent more powerful tools for entity extraction and relationship and efficient entity extraction and relationship reasoning reasoning. Research on GNN-based knowledge graph entity extraction and relationship reasoning algorithms is not only of models, effectively solving the problem of entity and Graph-augmented models have further advanced reasoning relationship reasoning in knowledge graphs.