2024· International Journal of Artificial Intelligence & Digital Transformation· 0 citations
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.
Modern enterprises generate vast amounts of data from diverse sources, including business applications, cloud platforms, IoT devices, social networks, and transactional systems. Integrating and analyzing this heterogeneous data efficiently remains a significant challenge due to data silos, semantic inconsistencies, and complex relationships among entities. Knowledge Graphs (KGs) have emerged as a powerful technology for representing interconnected enterprise data through semantic relationships, enabling enhanced data integration, contextual understanding, and intelligent knowledge discovery. This paper presents a Knowledge Graph–Driven Enterprise Data Integration Framework for Autonomous Decision Intelligence that unifies heterogeneous data sources into a semantically enriched knowledge ecosystem. The proposed framework employs ontology modeling, entity resolution, semantic mapping, graph construction, and intelligent reasoning mechanisms to establish meaningful relationships among enterprise data assets. Advanced graph analytics and machine learning techniques are integrated to support autonomous decision-making by generating contextual insights, identifying hidden patterns, and providing real-time recommendations. The framework further incorporates automated data governance, metadata management, and explainable reasoning capabilities to ensure data quality, transparency, and regulatory compliance. Experimental evaluation demonstrates that the proposed approach significantly improves data integration accuracy, knowledge discovery efficiency, and decision intelligence performance compared with traditional data integration systems. By leveraging knowledge graphs and intelligent reasoning engines, the framework enables organizations to transform fragmented enterprise data into actionable knowledge, thereby enhancing operational efficiency, strategic planning, and autonomous business decision-making. The proposed solution provides a scalable and intelligent foundation for next-generation enterprise analytics and AIdriven decision support systems.
Shashank Akinapalli· American Journal of AI Digit...· 0 citations
Graph-based data engineering has become a powerful approach for managing and analyzing highly interconnected data across enterprise systems, IoT, social media, healthcare, finance, and scientific domains. Unlike traditional relational databases, graph-based models represent data as interconnected nodes and edges, enabling efficient relationship analysis, semantic understanding, and knowledge discovery. This paper surveys recent advances in graph databases, knowledge graphs, graph neural networks (GNNs), and distributed graph analytics, and proposes an integrated framework for scalable graph construction, semantic enrichment, graph analytics, and AI-driven knowledge extraction. The framework emphasizes scalability, semantic consistency, explainable AI, and continuous graph evolution. Experimental evaluation demonstrates improved relationship discovery, query performance, and knowledge extraction compared with conventional relational approaches, making the proposed framework suitable for intelligent applications in healthcare, cybersecurity, finance, smart manufacturing, and enterprise knowledge management.
Mahabala H.N· International Journal of Dat...· 0 citations
This study presents an automated knowledge extraction and prediction system using the advancements in Artificial Intelligence (AI) tools, referred to as APEX-LLM, which is a scalable, domain-independent system which can be customized and applied to health, financial and business sectors, and education.
This paper presents a comprehensive framework integrating graph embeddings, retrieval-augmented generation (RAG), transformer-based reasoning, attention mechanisms, and contextual embedding fusion to improve prediction accuracy, explainability, and robustness.
Mahabala H.N· International Journal of Int...· 0 citations
This paper reviews hybrid KG–LLM frameworks for predictive analytics, highlighting graph embeddings, Retrieval-Augmented Generation (RAG), transformer-based reasoning, and contextual embedding fusion to improve prediction accuracy, interpretability, and robustness.
Meena Krishnan· International Journal of Mac...· 0 citations
To address the problems of complex process knowledge sources, heterogeneous representations, dispersed semantic associations, and limited reusability in the domain of machining distortion of thin-walled parts, this study proposes a knowledge graph construction method for the workpiece machining distortion domain, together with an intelligent decision-making framework driven by the collaboration of knowledge graphs and large language models. First, a domain ontology model is established around core concepts, including workpiece objects, deformation-driving factors, analytical resources, analytical methods, and optimization knowledge, thereby providing a unified semantic foundation for domain knowledge organization. Second, considering the characteristics of domain texts, such as dense technical terminology, ambiguous entity boundaries, and complex relation expressions, a dual-channel knowledge extraction method integrating BERT-BiLSTM-CRF and Universal Information Extraction (UIE) is developed to achieve high-precision extraction of entities and relations from unstructured texts. Knowledge fusion is further carried out through cross-validation, entity disambiguation, coreference resolution, and semantic alignment, and the extracted knowledge is ultimately stored and organized in Neo4j. Furthermore, an intelligent decision-making framework based on the collaboration of knowledge graphs and large language models is constructed. In this framework, a LoRA-tuned Qwen model is employed for user intent recognition and key information extraction, RapidFuzz WRatio is adopted for similar-node retrieval, and local subgraph construction, Label Propagation-based community detection, Betweenness Centrality-based key-node analysis, and evidence fusion are integrated to support process recommendation and intelligent question answering. Based on the proposed framework, an intelligent decision-making system is further developed for process recommendation and intelligent question answering in machining distortion scenarios. Experimental results show that the proposed dual-channel knowledge extraction model achieves an F1-score of 0.88, demonstrating its effectiveness in knowledge acquisition for the machining distortion domain. The constructed knowledge graph contains 4639 entities and 5822 relations, enabling a systematic representation of machining distortion knowledge. Case studies further demonstrate that the proposed method can generate interpretable recommendation results under complex process constraints in real industrial query scenarios. Overall, the proposed approach provides a feasible pathway for the structured organization, intelligent retrieval, and decision support of workpiece machining distortion knowledge.
Deguo Yao, Zhaoze Sun, Jie Gao et al.· Applied System Innovation· 0 citations