Building a Semantic Knowledge Graph Search Model for Finding a Causal Answer

Abstract

This research focuses on developing effective algorithms for semantic knowledge graph searches in the context of finding causal answers, which is highly relevant due to the widespread use of semantic networks. The study's primary goal is to examine the construction principles of a semantic knowledge graph search model tailored for causal answers, with a focus on deep neural semantic search characteristics. The research methodology combines system analysis methods for building knowledge graphs with an analytical investigation of deep neural semantic search aspects. The results provide insights into the construction of a semantic knowledge graph search model for causal answers, addressing the challenges and methodologies involved in building such a model. It underscores the significance of knowledge graphs in modern information systems and their potential applications in various domains. However, further scientific research is essential to explore the practical applications of deep neural semantic search knowledge graphs in various information systems, which are used across different aspects of everyday life. The practical significance of this research extends to various applications in information retrieval, knowledge management, and problem-solving, making it a valuable contribution to the advancement of technology and understanding of natural language text.