Innovative Entity Semantic Retrieval and Reasoning Algorithm with Knowledge Graph Integration (KGI-ERR)

The rapid growth of data in various domains has made entity semantic retrieval and reasoning increasingly crucial. Traditional retrieval methods often fail to capture the complex relationships and contextual nuances between entities, especially in cases involving polysemy, synonymy, and context-dependent meanings. These methods typically rely on surface-level lexical features, which are inadequate for understanding the deeper semantic relationships between entities. To address this limitation, we propose an innovative approach that integrates knowledge graphs (KGs) with deep learning models for entity semantic retrieval and reasoning. This combination is essential because knowledge graphs provide structured, rich semantic information that explicitly defines relationships between entities, while deep learning models excel at processing unstructured data and understanding context through layers of abstraction. Together, these two approaches address complementary aspects of entity retrieval: KGs provide a foundation of semantic knowledge and relationships, while deep learning models enhance contextual understanding and the ability to handle ambiguity, such as polysemy and synonymy. The proposed KGI-ERR algorithm leverages the rich semantic structure of KGs to enhance entity matching, contextual understanding, and relationship-based reasoning. By incorporating graph-based attention mechanisms and entity linking, our approach improves the accuracy of retrieval results and the depth of inferences, enabling more intelligent and context-aware decision-making. Experimental results show that KGI-ERR significantly outperforms traditional retrieval systems in terms of retrieval accuracy and reasoning efficiency, especially in complex, multi-relational datasets. This method provides a powerful tool for applications in natural language processing, information retrieval, and intelligent decision-making systems.