Knowledge Graphs - Representation and Reasoning: Analyzing knowledge graphs and their role in knowledge representation and reasoning in artificial intelligence

Abstract

Knowledge graphs (KGs) have emerged as a powerful tool for representing and reasoning over complex information in various domains. They organize knowledge in a graph structure, where nodes represent entities, and edges denote relationships between entities. This paper provides a comprehensive overview of knowledge graphs, focusing on their representation and reasoning capabilities in artificial intelligence (AI). We discuss the construction of knowledge graphs, including data acquisition, entity extraction, and relationship extraction. We also explore different reasoning mechanisms, such as deductive reasoning, inductive reasoning, and abductive reasoning, applied to knowledge graphs. Additionally, we examine the applications of knowledge graphs in AI, such as question answering, semantic search, and recommendation systems. Finally, we discuss challenges and future directions in the field of knowledge graphs.