Inductive Knowledge Graph Completion

Inductive Knowledge Graph Completion refers to the process of predicting new entity relationships based on existing knowledge bases through machine learning methods, in order to fill in the missing parts of the knowledge graph. Its aim is to enhance the completeness and accuracy of the knowledge graph, thereby strengthening its reasoning capabilities. This technology holds significant application value in areas such as recommendation systems, search engine optimization, and natural language processing, effectively improving the efficiency and quality of data-driven decision making.