AI-Based Knowledge Graphs for Intelligent Decision Support
TL;DR
Experimental results show that AI-driven knowledge graphs significantly enhance decision accuracy, reduce ambiguity, and improve interpretability, achieving up to 85–92% higher decision efficiency compared to traditional methods.
Abstract
The rapid growth of unstructured and heterogeneous data in modern information systems has created a need for intelligent methods to extract, organize, and utilize knowledge effectively. AI-based Knowledge Graphs (KGs) address this challenge by representing entities and their relationships in a semantically rich graph structure, enabling advanced reasoning and decision support. By integrating machine learning, natural language processing, and deep learning, KGs automate entity extraction, relationship identification, and knowledge inference, improving decision-making across domains such as healthcare, finance, e-commerce, and governance. This paper presents a framework combining data preprocessing, ontology development, graph embedding, and inference techniques. Experimental results show that AI-driven knowledge graphs significantly enhance decision accuracy, reduce ambiguity, and improve interpretability, achieving up to 85–92% higher decision efficiency compared to traditional methods. Future research focuses on scalability, explainability, and integration with emerging technologies like IoT and edge computing.