Optimized embedding and open domain representation learning in knowledge graphs

The Knowledge Graph (KG) is a model that represents structured knowledge by capturing entities and their relationships in the real world. It is widely used in search engines, recommendation systems, and natural language processing. Knowledge Representation Learning (KRL) transforms semantic information from knowledge graphs into continuous vector space representations, thereby improving knowledge acquisition and reasoning capabilities. However, current KRL faces issues such as underutilization of entity attribute information and difficulty in handling zero-shot scenarios. This paper proposes the Integrated Embedding Model (IEM) to address these issues. IEM employs a BERT-based attribute encoder and attention mechanism to weigh different attribute types, creating reliable attribute information embedding. During training, IEM merges structure and attribute representations, demonstrating excellent performance in knowledge graph completion tasks. Additionally, this paper introduces the Open Domain Representation Learning Model (ODRLM) for entity and link prediction in open domain knowledge graphs. ODRLM enhances the representation of zero-shot entities and relationships through three stages of optimization. Experimental results show that this model significantly improves both entity and relationship prediction accuracy, effectively addressing challenges in knowledge graph completion, especially in Zero-shot scenarios.