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Journal articles on the topic 'AI-driven knowledge graphs'

Author: Grafiati

Published: 7 June 2025

Last updated: 17 July 2025

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1

Saiyam Arora. "Transforming AI Decision Support System with Knowledge Graphs & CAG." International Journal on Engineering Artificial Intelligence Management, Decision Support, and Policies 2, no. 2 (2025): 15–23. https://doi.org/10.63503/j.ijaimd.2025.110.

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Artificial Intelligence (AI) serves as a fundamental component of decision support systems (DSS), enabling organizations to process large-scale data and derive actionable insights. However, traditional AI models utilizing relational databases (RDBMS) exhibit limitations in retaining context and applying knowledge-driven reasoning. This study examines the integration of Knowledge Graphs (KGs) and Context-Aware Graphs (CAGs) to enhance AI-driven decision-making systems. A hybrid framework is proposed in which structured knowledge graphs improve the contextual understanding of large language mode

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Tao, Xia, Weiwei Huang, and Shang Xu. "Research on Algorithm-driven Subject Knowledge Graphs Empowering Graduate Precision Teaching Mode." Higher Education and Practice 2, no. 1 (2025): 138–44. https://doi.org/10.62381/h251122.

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The integration of AI and big data has revitalized precise teaching. Knowledge graphs, driven by algorithms, structure knowledge and integrate teaching resources, offering more precise content for graduate education. Applied to graduate teaching, they can solve the problems of generalized teaching content and difficulty in meeting individual student needs in traditional modes. This paper takes the "Principles of Education" course as an example. It builds and applies knowledge graphs, considers graduate teaching needs, and proposes a precise teaching model based on knowledge graphs. This model

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Dehal, Ramandeep Singh, Mehak Sharma, and Enayat Rajabi. "Knowledge Graphs and Their Reciprocal Relationship with Large Language Models." Machine Learning and Knowledge Extraction 7, no. 2 (2025): 38. https://doi.org/10.3390/make7020038.

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The reciprocal relationship between Large Language Models (LLMs) and Knowledge Graphs (KGs) highlights their synergistic potential in enhancing artificial intelligence (AI) applications. LLMs, with their natural language understanding and generative capabilities, support the automation of KG construction through entity recognition, relation extraction, and schema generation. Conversely, KGs serve as structured and interpretable data sources that improve the transparency, factual consistency and reliability of LLM-based applications, mitigating challenges such as hallucinations and lack of expl

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Kumar Sahoo, Santanu, Manni Sruthi, Varun Ojha, et al. "AI-Powered Knowledge Graphs for Efficient Medical Information Retrieval and Decision Support." Seminars in Medical Writing and Education 3 (December 31, 2024): 517. https://doi.org/10.56294/mw2024517.

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The enormous volume of medical data has resulted in the development of sophisticated systems that facilitate information search and enable clinicians in decision-making process. Driven by artificial intelligence, knowledge graphs (KGs) provide a solid structure for organising and evaluating vast volumes of diverse medical data, therefore enabling wiser question development and improved decision-making. This article presents a whole strategy for integrating knowledge graphs with artificial intelligence-based approaches to improve medical information search and decision support systems performan

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Dr.S.Anusooya, S.M.Kamali, and Kandaneri Ramamoorthy Saravanan. "KGCD: Leveraging Knowledge Graphs for Intelligent Curriculum Design in Education." Recent Trends in Cloud Computing and Web Engineering 7, no. 1 (2024): 1–9. https://doi.org/10.5281/zenodo.13756522.

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<em>Curriculum design is a critical aspect of education, requiring careful consideration of content relevance, student progression, and pedagogical coherence. In recent years, the use of Knowledge Graphs (KG) has gained attention for their ability to represent complex relationships between concepts in a structured format. This paper introduces KGCD (Knowledge Graph-based Curriculum Design), a novel approach to intelligent curriculum design that leverages knowledge graphs to model subject matter interdependencies, skill progression, and student learning paths. By incorporating AI-driven insight

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S., S., Haritima Mishra, and A. Babiyola. "Development Knowledge Graphs for Intelligent Curriculum Design in Education with Artificial Intelligence." International Journal of BIM and Engineering Science 10, no. 1 (2025): 01–06. http://dx.doi.org/10.54216/ijbes.100101.

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Curriculum design is a critical aspect of education, requiring careful consideration of content relevance, student progression, and pedagogical coherence. In recent years, the use of Knowledge Graphs (KG) has gained attention for their ability to represent complex relationships between concepts in a structured format. This paper introduces KGCD (Knowledge Graph-based Curriculum Design), a novel approach to intelligent curriculum design that leverages knowledge graphs to model subject matter interdependencies, skill progression, and student learning paths. By incorporating AI-driven insights, K

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Yahan LI. "Smart Diagnosis Platform for Traditional Chinese Medicine Based on Artificial Intelligence and Big Data Technologies." Medical Research 6, no. 4 (2024): 43–55. https://doi.org/10.6913/mrhk.060405.

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Traditional Chinese Medicine (TCM) has a long history and a comprehensive theoretical system. However, its diagnostic process heavily relies on subjective experience, posing challenges to modernization and standardization. This study explores the integration of artificial intelligence (AI) into TCM, aiming to construct an intelligent diagnosis and treatment platform. By leveraging AI technologies such as deep learning, natural language processing (NLP), and knowledge graphs, the platform enhances the accuracy of syndrome recognition, intelligent consultation, and personalized treatment recomme

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Wang, Xiaochen. "Developing Multimodal Healthcare Foundation Model: From Data-driven to Knowledge-enhanced." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 28 (2025): 29305–6. https://doi.org/10.1609/aaai.v39i28.35230.

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Foundation models in general domains have leveraged multimodal knowledge graphs to great effect, yet the healthcare sector lacks such comprehensive structures, presenting a significant gap in current research. Based on previous exploration with pure data-driven approaches, this proposal describes a two-stage project aiming to enhance multimodal healthcare foundation model with domain knowledge. The first stage is to construct a robust multimodal healthcare knowledge graph based on established healthcare taxonomies, such as UMLS, and enriched with data from multimodal clinical databases like MI

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Anil Kumar. "Neuro Symbolic AI in personalized mental health therapy: Bridging cognitive science and computational psychiatry." World Journal of Advanced Research and Reviews 19, no. 2 (2023): 1663–79. https://doi.org/10.30574/wjarr.2023.19.2.1516.

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Personalized mental health therapy has gained increasing attention as advancements in artificial intelligence (AI) enable tailored treatment strategies based on individual cognitive and emotional profiles. Neuro-symbolic AI, a hybrid approach combining symbolic reasoning and neural networks, offers a promising solution for bridging cognitive science and computational psychiatry. Unlike conventional AI models that rely solely on deep learning, neuro-symbolic AI integrates human-interpretable knowledge representations with data-driven learning, enhancing the adaptability and explainability of AI

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Nagpure, Vaishali. "AI-Driven Network Traffic Optimization and Fault Detection in Enterprise WAN." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 11 (2024): 1–6. http://dx.doi.org/10.55041/ijsrem11493.

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In the contemporary landscape of enterprise-Wide Area Networks (WANs), managing complex interconnections between multiple data centers and branch offices poses significant challenges. This paper explores an innovative AI-driven approach to network traffic optimization and fault detection, utilizing knowledge graphs to enhance network performance and reliability. The proposed framework integrates real-time data collection, reinforcement learning algorithms, and graph-based machine learning to dynamically optimize traffic routing while ensuring low latency and high availability for critical appl

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J. Uma, V. Arun Kumar, R. Karthikeyan, V. Lavanya, and P. Priyadharshini. "Integration of Artificial Intelligence into Software Component Reuse: An Overview of Software Intelligence." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 04 (2025): 1086–88. https://doi.org/10.47392/irjaem.2025.0178.

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Artificial Intelligence (AI) is transforming software component reuse by enhancing automation, efficiency, and intelligent retrieval of reusable software artifacts. Traditional reuse methods face challenges in retrieving, classifying, and recommending components due to the complexity of software repositories. AI-driven techniques such as machine learning (ML), natural language processing (NLP), and knowledge graphs help overcome these limitations by enabling intelligent categorization and recommendation. Software Intelligence (SI) enhances reuse by employing data mining techniques to extract p

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Hu, Fuwen, Chun Wang, and Xuefei Wu. "Generative Artificial Intelligence-Enabled Facility Layout Design Paradigm." Applied Sciences 15, no. 10 (2025): 5697. https://doi.org/10.3390/app15105697.

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Facility layout design (FLD) is critical for optimizing manufacturing efficiency, yet traditional approaches struggle with complexity, dynamic constraints, and fragmented data integration. This study proposes a generative-AI-enabled facility layout design, a novel paradigm aligning with Industry 4.0, to address these challenges by integrating generative artificial intelligence (AI), semantic models, and data-driven optimization. The proposed method evolves from three historical paradigms: experience-based methods, operations research, and simulation-based engineering. The metamodels supporting

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Sufi, Fahim, and Musleh Alsulami. "AI-Driven Chatbot for Real-Time News Automation." Mathematics 13, no. 5 (2025): 850. https://doi.org/10.3390/math13050850.

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The rapid expansion of digital news sources has necessitated intelligent systems capable of filtering, analyzing, and deriving meaningful insights from vast amounts of information in real time. This study presents an AI-driven chatbot designed for real-time news automation, integrating advanced natural language processing techniques, knowledge graphs, and generative AI models to improve news summarization and correlation analysis. The chatbot processes over 1,306,518 news reports spanning from 25 September 2023 to 17 February 2025, categorizing them into 15 primary event categories and extract

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Researcher. "EVOLUTION AND FUTURE OF SEARCH: HOW AI IS TRANSFORMING INFORMATION RETRIEVAL." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 107–17. https://doi.org/10.5281/zenodo.13134112.

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This article examines the transformative impact of artificial intelligence on search engines, enhancing query processing and information retrieval. It addresses the limitations of traditional keyword-based algorithms. It traces the evolution of search engines from early keyword-based models to the integration of AI, enabling semantic understanding and context-aware search. The article delves into crucial AI techniques like Natural Language Processing, deep learning, and reinforcement learning, highlighting their impact on query processing and retrieval accuracy. It further explores how AI faci

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Oladeji, Olamide, Seyed Shahabeddin Mousavi, and Marc Roston. "AI-Driven E-liability Knowledge Graphs: A Comprehensive Framework for Supply Chain Carbon Accounting and Emissions Liability Management." Proceedings of the AAAI Symposium Series 2, no. 1 (2024): 124–34. http://dx.doi.org/10.1609/aaaiss.v2i1.27659.

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While carbon accounting plays a fundamental role in our fight against climate change, it is not without its challenges. We begin the paper with a critique of the conventional carbon accounting practices, after which we proceed to introduce the E-liability carbon accounting methodology and Emissions Liability Management (ELM) originally proposed by Kaplan and Ramanna, highlighting their strengths. Recognizing the immense value of this novel approach for real-world carbon accounting improvement, we introduce a novel data-driven integrative framework that leverages AI and computation, the E-Liabi

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Yu, Qilong. "Enhancing Anti-Money Laundering Systems Using Knowledge Graphs and Graph Neural Networks." Advances in Economics, Management and Political Sciences 118, no. 1 (2024): 280–88. https://doi.org/10.54254/2754-1169/2024.18627.

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In today's increasingly complex financial landscape, traditional anti-money laundering (AML) systems are often inadequate in combating sophisticated financial crimes. This research aims to bridge that gap by integrating knowledge graphs with graph neural networks (GNNs) to enhance AML detection capabilities. The study leverages financial transactional data to construct a knowledge graph, employing GNN architectures, particularly Graph Attention Networks (GAT), to predict and detect potential money laundering activities. Empirical results demonstrate that GNNs are highly effective at uncovering

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Ananthakrishnan, Vasudevan, Srinivas Bangalore Sujayendra Rao, and Sayantan Bhattacharyya. "Generative Knowledge-Graph Assistants for Service-Desk Incident Triage." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 5, no. 1 (2024): 564–80. https://doi.org/10.60087/jaigs.v5i1.380.

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Modern enterprise service desks are burdened with high incident volumes, inconsistent triage accuracy, and extended resolution times. This paper introduces a novel framework combining generative AI with dynamic knowledge graphs to streamline incident triage in IT service management (ITSM) environments. The proposed system leverages real-time graph-based context retrieval from CMDBs, historical tickets, and dependency maps to enable LLM-driven agents to understand, classify, and route incidents with minimal human intervention. By integrating graph embeddings with generative prompting strategies

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Tong, Ran, Ting Xu, Xinxin Ju, and Lanruo Wang. "Progress in Medical AI: Reviewing Large Language Models and Multimodal Systems for Diagonosis." AI Med 1, no. 1 (2025): 1–2. https://doi.org/10.71423/aimed.20250105.

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The rapid advancement of artificial intelligence (AI) in healthcare has significantly enhanced diagnostic accuracy and clinical decision-making processes. This review examines four pivotal studies that highlight the integration of large language models (LLMs) and multimodal systems in medical diagnostics. BioBERT demonstrates the efficacy of domain-specific pretraining on biomedical texts, improving performance in tasks such as named entity recognition, relation extraction, and question answering. Med-PaLM, a large-scale language model tailored for clinical question answering, leverages instru

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Narendra Chennupati. "Zero-touch transformation: AI-driven middleware for autonomous integration of legacy enterprise systems with cloud architectures." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 1444–53. https://doi.org/10.30574/wjaets.2025.15.2.0621.

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This abstract introduces a novel approach to enterprise legacy system migration through the development of Zero-Touch AI-driven middleware that autonomously facilitates the integration of aging enterprise infrastructures with modern cloud architectures. The proposed middleware employs advanced machine learning algorithms, natural language processing, and knowledge graphs to automatically discover, map, and optimize legacy workflows for cloud environments without manual intervention. The article demonstrates how this approach significantly reduces migration complexity, minimizes business disrup

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Satish Manchana. "Advancing Hybrid Cloud Automation: AI-driven Policy Engines and Compliance-Aware Orchestration in Financial Enterprises." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 1106–21. https://doi.org/10.30574/wjaets.2025.15.3.1003.

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The convergence of artificial intelligence and cloud computing is revolutionizing how financial enterprises manage infrastructure, particularly in hybrid environments where regulatory compliance remains paramount. Financial institutions implementing AI-driven governance solutions report reducing compliance incident response time by 78% and decreasing manual audit efforts by 65%. This article explores the evolution of cloud automation in financial services, highlighting the shift from traditional governance approaches to AI-driven policy engines that dynamically enforce regulatory requirements

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Mansor, Mahaliza, Goh Kok Ming, and Dayang Rafidah Syariff M. Fuad. "Leadership in Times of Artificial Intelligence: Social Network Analysis on X Data." International Journal of Research and Innovation in Social Science VIII, no. XIX (2024): 184–95. https://doi.org/10.47772/ijriss.2024.icame2415.

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Introduction: X, the second most popular social networking site in Malaysia, is a platform for sharing news, messages, photos, and short videos with a large audience. X generates vast amounts of data, which can be valuable for understanding online social communities and trending topics. However, there is a gap in understanding how leaders discuss and address these challenges in public discourse, particularly on digital platforms like X (formerly Twitter). Despite the growing relevance of AI in education, little is known about the key influencers driving discussions related to leadership in the

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DAS, SUBHASHIS, and Mayukh Bagchi. "An ontology-driven knowledge graph for tourism information management." Open Research Europe 5 (January 8, 2025): 1. https://doi.org/10.12688/openreseurope.17614.1.

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Background Lao Tzu, the ancient Chinese philosopher, had famously said, “a journey of a thousand miles begins with a single step”. The modern ecosystem of tourism is extremely multi-faceted and multi-context dependent, so much so that tourists, planning on an excursion, have an entire range of queries from as basic as “Where to eat?” to as complex as “List all the eating establishments which are suitable for groups and also provide children menu”. Such a list becomes even more complicated due to the intermixing of different perceptions, languages, cultures and economies in which tourists are e

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Wang, Yongtao, Yinhui Feng, Chengfeng Xi, Bochao Wang, Bo Tang, and Yanzhao Geng. "Development of an Intelligent Coal Production and Operation Platform Based on a Real-Time Data Warehouse and AI Model." Energies 17, no. 20 (2024): 5205. http://dx.doi.org/10.3390/en17205205.

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Smart mining solutions currently suffer from inadequate big data support and insufficient AI applications. The main reason for these limitations is the absence of a comprehensive industrial internet cloud platform tailored for the coal industry, which restricts resource integration. This paper presents the development of an innovative platform designed to enhance safety, operational efficiency, and automation in fully mechanized coal mining in China. This platform integrates cloud edge computing, real-time data processing, and AI-driven analytics to improve decision-making and maintenance stra

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Praveen Kumar Guguloth. "AI-Powered Decision Intelligence in Enterprise Systems Engineering: A Framework for Next-Generation Business Operations." Journal of Computer Science and Technology Studies 7, no. 3 (2025): 677–82. https://doi.org/10.32996/jcsts.2025.7.3.77.

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The integration of AI-powered decision intelligence systems in enterprise environments represents a transformative shift in business operations and strategic planning. This advancement addresses the limitations of traditional decision-making frameworks by introducing sophisticated machine learning algorithms, knowledge graphs, and cognitive automation capabilities. The framework encompasses comprehensive data integration, analytical processing, and decision execution layers, enabling organizations to process vast amounts of structured and unstructured data while maintaining human oversight for

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Kocha, Jeet. "AI- Driven WIOA Compliance Engines: Automating Federal and State Mandate Adherence With 99% Audit Precision." International journal of IoT 5, no. 02 (2025): 1–14. https://doi.org/10.55640/ijiot-05-02-01.

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This research presents the architecture and development of an AI-powered compliance engine tailored for the Workforce Innovation and Opportunity Act (WIOA). The system is designed to automate adherence to complex federal and state mandates with high precision and minimal manual oversight. By integrating machine learning (ML), natural language processing (NLP), and regulatory knowledge graphs, the engine enables real-time compliance monitoring, automated documentation validation, and dynamic error correction. The proposed framework addresses long-standing inefficiencies in the public workforce

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Sakhinana, Sagar Srinivas, Vijay Sri Vaikunth, and Venkataramana Runkana. "Knowledge Graph Modeling-Driven Large Language Model Operating System (LLM OS) for Task Automation in Process Engineering Problem-Solving." Proceedings of the AAAI Symposium Series 4, no. 1 (2024): 222–32. http://dx.doi.org/10.1609/aaaiss.v4i1.31796.

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We present the Process Engineering Operations Assistant (PEOA), an AI-driven framework designed to solve complex problems in the chemical and process industries. The framework employs a modular architecture orchestrated by a meta-agent, which serves as the central coordinator, managing an action generator and instruction-tuned small-scale language models (expert models). The action generator decomposes complex problems into sub-tasks and identifies suitable expert models to execute each, delivering precise solutions for multi-step problem-solving. Key techniques include advanced knowledge mode

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Soumen Chakraborty. "From DataOps to AIOps: How autonomous agents are revolutionizing data engineering." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 1403–14. https://doi.org/10.30574/wjaets.2025.15.2.0650.

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This comprehensive article examines the paradigm shift from traditional DataOps to AI-powered DataOps (AIOps), highlighting how autonomous agents are fundamentally transforming data engineering practices. The evolution represents not merely a technological upgrade but a complete reimagining of data pipeline management—moving from human-centered operations to self-learning, autonomous systems. The article explores the core pillars of AIOps: automated observability that contextually understands metrics beyond simple collection, predictive issue resolution that anticipates and prevents problems b

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Akhil, Chaturvedi, and Singh Taranveer. "Transforming User Experience Research: Leveraging AI Agents and Advanced Technologies for Enhanced Insights and Efficiency." Journal of Scientific and Engineering Research 11, no. 3 (2024): 317–25. https://doi.org/10.5281/zenodo.13592804.

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<strong> </strong>User experience research (UXR) plays a vital role in the development of user-centered products and services. However, traditional UXR methods face challenges such as scalability, efficiency, and resource intensity. This paper presents a comprehensive analysis of how artificial intelligence (AI) agents can revolutionize UXR practices. We delve into the architectures and key components of AI agents, including reasoning, planning, and tool use, and examine how they can be leveraged to enhance specific UXR methods. We propose novel AI-powered solutions for interviews, survey

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Chakraborty, Soumen. "Data Stewardship Co-Pilot: Transforming Enterprise Data Governance with Generative AI and Agentic Frameworks." European Journal of Computer Science and Information Technology 13, no. 20 (2025): 1–15. https://doi.org/10.37745/ejcsit.2013/vol13n20115.

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The convergence of Generative AI and agentic frameworks is fundamentally transforming enterprise data governance, offering a solution to the exponential growth in data complexity that traditional manual methods cannot address. This emerging paradigm shift introduces the concept of a Data Stewardship Co-Pilot—an intelligent, AI-powered partner that guides organizations through comprehensive data governance while democratizing access to complex data environments. The evolution from manual processes to intelligent automation employs specialized horizontal and vertical AI agents that autonomously

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Chakraborty, Soumen. "Data Stewardship Co-Pilot: Transforming Enterprise Data Governance with Generative AI and Agentic Frameworks." European Journal of Computer Science and Information Technology 13, no. 22 (2025): 1–14. https://doi.org/10.37745/ejcsit.2013/vol13n22114.

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The convergence of Generative AI and agentic frameworks is fundamentally transforming enterprise data governance, offering a solution to the exponential growth in data complexity that traditional manual methods cannot address. This emerging paradigm shift introduces the concept of a Data Stewardship Co-Pilot—an intelligent, AI-powered partner that guides organizations through comprehensive data governance while democratizing access to complex data environments. The evolution from manual processes to intelligent automation employs specialized horizontal and vertical AI agents that autonomously

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Papageorgiou, George, Vangelis Sarlis, Manolis Maragoudakis, and Christos Tjortjis. "Hybrid Multi-Agent GraphRAG for E-Government: Towards a Trustworthy AI Assistant." Applied Sciences 15, no. 11 (2025): 6315. https://doi.org/10.3390/app15116315.

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As public institutions increasingly adopt AI-driven virtual assistants to support transparency and citizen engagement, the need for explainable, accurate, and context-aware language systems becomes vital. While traditional retrieval-augmented generation (RAG) frameworks effectively integrate external knowledge into Large Language Models (LLMs), their reliance on flat, unstructured document retrieval limits multi-hop reasoning and interpretability, especially with complex, structured e-government datasets. This study introduces a modular, extensible, multi-agent graph retrieval-augmented genera

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Sun, Xiaochun, Chenmou Wu, and Shuqun Yang. "SFCA: A Scalable Formal Concepts Driven Architecture for Multi-Field Knowledge Graph Completion." Applied Sciences 13, no. 11 (2023): 6851. http://dx.doi.org/10.3390/app13116851.

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With the proliferation of Knowledge Graphs (KGs), knowledge graph completion (KGC) has attracted much attention. Previous KGC methods focus on extracting shallow structural information from KGs or in combination with external knowledge, especially in commonsense concepts (generally, commonsense concepts refer to the basic concepts in related fields that are required for various tasks and academic research, for example, in the general domain, “Country” can be considered as a commonsense concept owned by “China”), to predict missing links. However, the technology of extracting commonsense concep

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Wani H. Bisen. "An Iterative Systematic Analytical Review of Large Language Models for Medical Applications Using GPT-4, BERT Variants, and Vision Transformers." Communications on Applied Nonlinear Analysis 32, no. 9s (2025): 570–88. https://doi.org/10.52783/cana.v32.3965.

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Introduction: The increasing adoption of Large Language Models (LLMs) in healthcare necessitates a comprehensive review of their applications, limitations, and potential. Existing literature lacks a systematic assessment of LLM performance across diverse healthcare tasks and does not adequately address critical aspects such as model-specific optimizations, domain adaptability, and real-world deployment constraints. Objectives : This paper aims to fill the identified gaps by conducting an extensive and structured review of current research on LLM applications in medical reports, diagnostics, an

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Oriyomi Badmus, Olumide Johnson Ikumapayi, Rebecca Olubunmi Toromade, and Abiodun Sunday Adebayo. "Integrating AI-powered knowledge graphs and NLP for intelligent interpretation, summarization, and cross-border financial reporting harmonization." World Journal of Advanced Research and Reviews 27, no. 1 (2025): 042–62. https://doi.org/10.30574/wjarr.2025.27.1.2517.

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In an environment of increasingly complicated and globally interconnected financial systems, challenges related to harmonization in cross-border reporting are magnifying. Differences in regulation, language, data siloing, and the further proliferation of unstructured disclosures remain obstacles to the success of transparency, compliance and efficiency initiatives. In this paper we discuss a new integration of AI-driven Knowledge Graphs (KG) and NLP that we believe can form part of this solution; a new way of thinking about financial interpretation and summarization over jurisdictions. As stru

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Canpolat, Can Sinan. "Leveraging Knowledge Graphs for Enhanced Medical Reasoning in Personalized Medicine for Rare Diseases." Next Frontier For Life Sciences and AI 8, no. 1 (2024): 77. http://dx.doi.org/10.62802/jj57vn49.

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Leveraging knowledge graphs offers a promising approach to enhancing medical reasoning in the personalized treatment of rare diseases. Knowledge graphs provide an effective framework for organizing and interpreting complex biomedical data, facilitating a more comprehensive approach to diagnosis, prognosis, and treatment of rare diseases. By integrating diverse data sources—such as genomic profiles, clinical histories, and pharmacological information—into a cohesive graph structure, the project aims to improve clinical outcomes by enabling patient-specific insights and treatment recommendations

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Lingareddy, Nagulapalli, Sheri Deepika, Sivakumar K, Rose Mary Mathew, Sivakumar Ponnusamy, and Venkatasubramanian R. "Advancements in Machine Learning Algorithms for Predictive Analytics in Healthcare Information Systems Management." ITM Web of Conferences 76 (2025): 01001. https://doi.org/10.1051/itmconf/20257601001.

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The current studies have critical limitations such as lack of real-world deployment, biases in Electronic Health Records (EHR)-based models, and computational ineffectiveness. This paper proposes an advanced ML framework incorporating transformer-based deep learning architectures, fairness-aware training, privacy-preserving federated learning in order to focus on those challenges. In contrast to existing models which target specific disease classes, the proposed system generalises across chronic and acute conditions, while ensuring scalability in low-resource settings. In addition, the study e

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Liu, Shengheng, Tianqi Zhang, Ningning Fu, and Yongming Huang. "Fine-Grained Graph Representation Learning for Heterogeneous Mobile Networks with Attentive Fusion and Contrastive Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 18 (2025): 18933–42. https://doi.org/10.1609/aaai.v39i18.34084.

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AI becomes increasingly vital for telecom industry, as the burgeoning complexity of upcoming mobile communication networks places immense pressure on network operators. While there is a growing consensus that intelligent network self-driving holds the key, it heavily relies on expert experience and knowledge extracted from network data. In an effort to facilitate convenient analytics and utilization of wireless big data, we introduce the concept of knowledge graphs into the field of mobile networks, giving rise to what we term as wireless data knowledge graphs (WDKGs). However, the heterogeneo

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Zhao, Yu, Huali Feng, and Patrick Gallinari. "Embedding Learning with Triple Trustiness on Noisy Knowledge Graph." Entropy 21, no. 11 (2019): 1083. http://dx.doi.org/10.3390/e21111083.

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Embedding learning on knowledge graphs (KGs) aims to encode all entities and relationships into a continuous vector space, which provides an effective and flexible method to implement downstream knowledge-driven artificial intelligence (AI) and natural language processing (NLP) tasks. Since KG construction usually involves automatic mechanisms with less human supervision, it inevitably brings in plenty of noises to KGs. However, most conventional KG embedding approaches inappropriately assume that all facts in existing KGs are completely correct and ignore noise issues, which brings about pote

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Srinivas Kalyan Yellanki, Dwaraka Nath Kummari, Goutham Kumar Sheelam, Sathya Kannan, and Chaitran Chakilam. "Synthetic Cognition Meets Data Deluge: Architecting Agentic AI Models for Self-Regulating Knowledge Graphs in Heterogeneous Data Warehousing." Metallurgical and Materials Engineering 31, no. 4 (2025): 569–86. https://doi.org/10.63278/1487.

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The realities of contemporary data management and representation are evolving at an increasing rate. However, we still lack the broad foundational bridges of core data warehousing principles relating to how high-level reports are generated internally so that users can psychologically intuit where they are in the vast and complex repository of data that resides in a typical data warehouse. IT workers must constantly support users or worry about failed ad-hoc or automated operations or whose results appear without explanation. Data management may not yet exist as a science. We need a more comple

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Sunmola, Funlade, George Baryannis, Albert Tan, Kenneth Co, and Emmanuel Papadakis. "Holistic Framework for Blockchain-Based Halal Compliance in Supply Chains Enabled by Artificial Intelligence." Systems 13, no. 1 (2025): 21. https://doi.org/10.3390/systems13010021.

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The global halal market is growing, driven by rising stakeholder populations and increasing consumer interest in ethical and sustainable food choices. This surge in demand necessitates robust halal compliance throughout complex supply chains. However, there are several challenges, including fragmented information, increased understanding of halal requirements among stakeholders, and difficulties in tracing product provenance. This paper proposes a holistic framework for halal certification and compliance, addressing these challenges through the integration of artificial intelligence (AI) and b

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Yang, Xi. "AI-Enabled Supply Chain: Theoretical Logic and Practical Pathways of Technological Reconfiguration and Value Creation." Economics & Business Management 1, no. 2 (2025): 173. https://doi.org/10.63313/ebm.2007.

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This study takes Huawei's supply chain as a case to reveal the mechanism of AI-driven supply chain reconfiguration based on multidisciplinary theories. Huawei has achieved breakthroughs such as reducing demand forecasting error rate to 8%, optimizing inventory turnover ratio by 25%, etc., through cognitive network architecture and multi-technology integration. Technologies such as spatiotemporal graph neural networks and adversarial learning have demon-strated significant effectiveness in demand perception and resilient supply chain construction. Reparameterization of production functions has

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Tupayachi, Jose, Haowen Xu, Olufemi A. Omitaomu, Mustafa Can Camur, Aliza Sharmin, and Xueping Li. "Towards Next-Generation Urban Decision Support Systems through AI-Powered Construction of Scientific Ontology Using Large Language Models—A Case in Optimizing Intermodal Freight Transportation." Smart Cities 7, no. 5 (2024): 2392–421. http://dx.doi.org/10.3390/smartcities7050094.

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The incorporation of Artificial Intelligence (AI) models into various optimization systems is on the rise. However, addressing complex urban and environmental management challenges often demands deep expertise in domain science and informatics. This expertise is essential for deriving data and simulation-driven insights that support informed decision-making. In this context, we investigate the potential of leveraging the pre-trained Large Language Models (LLMs) to create knowledge representations for supporting operations research. By adopting ChatGPT-4 API as the reasoning core, we outline an

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Hendawi, Rasha, Juan Li, and Souradip Roy. "A Mobile App That Addresses Interpretability Challenges in Machine Learning–Based Diabetes Predictions: Survey-Based User Study." JMIR Formative Research 7 (November 13, 2023): e50328. http://dx.doi.org/10.2196/50328.

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Background Machine learning approaches, including deep learning, have demonstrated remarkable effectiveness in the diagnosis and prediction of diabetes. However, these approaches often operate as opaque black boxes, leaving health care providers in the dark about the reasoning behind predictions. This opacity poses a barrier to the widespread adoption of machine learning in diabetes and health care, leading to confusion and eroding trust. Objective This study aimed to address this critical issue by developing and evaluating an explainable artificial intelligence (AI) platform, XAI4Diabetes, de

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Kohl, Linus, Sarah Eschenbacher, Philipp Besinger, and Fazel Ansari. "Large Language Model-based Chatbot for Improving Human-Centricity in Maintenance Planning and Operations." PHM Society European Conference 8, no. 1 (2024): 12. http://dx.doi.org/10.36001/phme.2024.v8i1.4098.

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The recent advances on utilizing Generative Artificial Intelligence (GenAI) and Knowledge Graphs (KG) enforce a significant paradigm shift in data-driven maintenance management. GenAI and semantic technologies enable comprehensive analysis and exploitation of textual data sets, such as tabular data in maintenance databases, maintenance and inspection reports, and especially machine documentation. Traditional approaches to maintenance planning and execution rely primarily on static, non-adaptive simulation models. These models have inherent limitations in accounting for dynamic environmental ch

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Liu, Yingze, and Yuanbo Guo. "Towards Real-Time Warning and Defense Strategy AI Planning for Cyber Security Systems Aided by Security Ontology." Electronics 11, no. 24 (2022): 4128. http://dx.doi.org/10.3390/electronics11244128.

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Cyber security systems generally have the phenomena of passive defense and low-efficiency early warnings. Aiming at the above problems, this study proposes a real-time warning and plans an AI defense strategy for a cyber security system aided by a security ontology. First, we design a security defense ontology that integrates attack graphs, general purpose and domain-specific knowledge bases, and on this basis, we (1) develop an ontology-driven method of early warnings of real-time attacks, which supports non-intrusive scanning attack detection and (2) combine artificial intelligence planning

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Stănescu (Nicolaie), Georgiana, and Simona-Vasilica Oprea. "Recent Trends and Insights in Semantic Web and Ontology-Driven Knowledge Representation Across Disciplines Using Topic Modeling." Electronics 14, no. 7 (2025): 1313. https://doi.org/10.3390/electronics14071313.

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This research aims to investigate the roles of ontology and Semantic Web Technologies (SWT) in modern knowledge representation and data management. By analyzing a dataset of 10,037 academic articles from Web of Science (WoS) published in the last 6 years (2019–2024) across several fields, such as computer science, engineering, and telecommunications, our research identifies important trends in the use of ontologies and semantic frameworks. Through bibliometric and semantic analyses, Natural Language Processing (NLP), and topic modeling using Latent Dirichlet Allocation (LDA) and BERT-clusterin

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Si, Han, Sanyam Kumar, Sneh Lata, et al. "Abstract 3644: Mechanistically explainable AI model for predicting synergistic cancer therapy combinations." Cancer Research 85, no. 8_Supplement_1 (2025): 3644. https://doi.org/10.1158/1538-7445.am2025-3644.

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Abstract Background: Resistance to single-agent cancer therapies often arises through complex mechanisms such as genetic mutations, compensatory signaling, and tumor microenvironment changes. These adaptive strategies make multi-drug combinations essential to effectively target canonical pathways simultaneously. However, common in vitro and in vivo models often fail to capture these complexities, resulting in limited translational success to human clinical settings. This highlights the need for predictive frameworks that not only identify synergistic drug combinations from human clinical trial

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Zimmermann, Bob, Radosław Bielecki, Roy Ronen, and Janusz Dutkowski. "Abstract LB112: Knowledge graph AI-based prioritization of drug target candidates across 503 cancers." Cancer Research 85, no. 8_Supplement_2 (2025): LB112. https://doi.org/10.1158/1538-7445.am2025-lb112.

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Abstract Background: Drug target discovery is a vital step in drug development. Novel computational methods taking advantage of multimodal data sets and diverse algorithmic approaches are beginning to provide key insights to inform this process. Comprehensive biomedical knowledge graphs provide a particularly promising paradigm for target discovery and prioritization, being able to encode multi-modal relational evidence without the need for feature engineering or selection. Within this context, target prediction can be formulated as a link prediction problem using knowledge graph embeddings (K

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Zhang, Qiang. "Knowledge-driven Scientific Large Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 27 (2025): 28736. https://doi.org/10.1609/aaai.v39i27.35128.

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My research in AI for Science revolves around the development and application of knowledge graphs (KG) and large language models (LLM) for scientific discovery. Leveraging my expertise in AI, I extensively explore disciplinary knowledge, construct knowledge graphs, and develop pre-trained large models for chemical and biological research. The overarching goal is to better capture correlations and patterns between substances by incorporating explicit and implicit knowledge bases into pre-trained large models. I have published in top AI journals and conferences, including Nature Machine Intellig

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Roder, Allison E., Alice M. Walsh, Taylor K. Krebs, et al. "Abstract P33: Leveraging probabilistic causal disease models to understand molecular pathways and target resistance mechanisms in Multiple Myeloma with the CREBBP and EP300 bromodomain inhibitor, pocenbrodib." Blood Cancer Discovery 5, no. 2_Supplement (2024): P33. http://dx.doi.org/10.1158/2643-3249.bcdsymp24-p33.

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Abstract To explore and devise innovative approaches for the treatment of Multiple Myeloma (MM) – a disease often deemed incurable due to the development of resistance mechanisms against available therapies – we adopted an integrative data approach, constructing causal AI models of MM that integrate co-expression relationships, DNA variation, longitudinal clinical data, and expansive knowledge graphs. Diverging from traditional methods, our approach centers on constructing probabilistic causal models to elucidate the regulatory mechanisms of disease and capture existing known causal relationsh

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