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Готові списки джерел за темами / AI-driven knowledge graphs

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Добірка наукової літератури з теми "AI-driven knowledge graphs"

Автор: Grafiati

Опубліковано: 7 червня 2025

Оновлено: 2 серпня 2025

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Статті в журналах з теми "AI-driven knowledge graphs"

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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Анотація:

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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2

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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3

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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4

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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5

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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6

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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Анотація:

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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7

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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8

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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10

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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Більше джерел

Дисертації з теми "AI-driven knowledge graphs"

1

Ishida, Shoichi. "Development of an AI-Driven Organic Synthesis Planning Approach with Retrosynthesis Knowledge." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263605.

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Частини книг з теми "AI-driven knowledge graphs"

1

Holzinger, Andreas, Anna Saranti, Anne-Christin Hauschild, et al. "Human-in-the-Loop Integration with Domain-Knowledge Graphs for Explainable Federated Deep Learning." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40837-3_4.

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AbstractWe explore the integration of domain knowledge graphs into Deep Learning for improved interpretability and explainability using Graph Neural Networks (GNNs). Specifically, a protein-protein interaction (PPI) network is masked over a deep neural network for classification, with patient-specific multi-modal genomic features enriched into the PPI graph’s nodes. Subnetworks that are relevant to the classification (referred to as “disease subnetworks”) are detected using explainable AI. Federated learning is enabled by dividing the knowledge graph into relevant subnetworks, constructing an

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2

Tang, Qianqian. "On the Theoretical Construction of Smart Teaching Mode of Management Curriculum Driven by Knowledge Graph and AI Teaching Assistant." In Atlantis Highlights in Social Sciences, Education and Humanities. Atlantis Press International BV, 2025. https://doi.org/10.2991/978-94-6463-750-2_37.

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3

Mohamed, Saher M., Saleh Farah, Abdelrahman Mahmoud Lotfy, et al. "Knowledge Graphs." In Practice, Progress, and Proficiency in Sustainability. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-7117-6.ch005.

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Анотація:

Knowledge Graphs (KGs) are becoming an essential tool in the organization, linkage, and analysis of complex data across various domains. By structuring information as entities and their interrelationships, KGs enhance data integration and provide advanced capabilities such as reasoning, question-answering, and real-time decision-making. This paper explores methodologies for building and maintaining static and dynamic knowledge graphs, focusing on their applications in fields like virtual assistants, recommendation systems, autonomous systems, and AI-driven decision-making. The study emphasizes

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4

Breteler, Jeroen, Thom van Gessel, Giulia Biagioni, and Robert van Doesburg. "The FLINT Ontology: An Actor-Based Model of Legal Relations." In Knowledge Graphs: Semantics, Machine Learning, and Languages. IOS Press, 2023. http://dx.doi.org/10.3233/ssw230016.

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Recording and documenting human and AI-driven normative decision-making processes has so far been highly challenging. We focus on the challenge of normative coordination: the process by which stakeholders in a community understand and agree what norms they abide by. Our aim is to develop and formalize the FLINT language, which allows a high-level description of normative systems. FLINT enables legal experts to agree on norms, while also serving as a basis for technical implementation. Our contribution consists of the development of an ontology for FLINT and its RDF/OWL implementation which we

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5

Gavrish, Alina, Yang Yang, Julie Loesch, and Michel Dumontier. "Forecasting Banned Substances: Leveraging GNN and Explainable AI for Sports Anti-Doping." In Studies in Health Technology and Informatics. IOS Press, 2025. https://doi.org/10.3233/shti250287.

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Ensuring fairness in competitive sports requires robust mechanisms for detecting prohibited substances. Despite established regulations, challenges persist in accurately identifying new and emerging doping agents. This study introduces the use of Graph Neural Network (GNN) and Explainable AI (XAI) to classify substances as prohibited or non-prohibited, based on molecular and pharmacological data. The study utilizes Knowledge Graphs (KG) of heterogeneous type to develop predictive models. Explainability methods like Integrated Gradients and Saliency provide transparency into the models’ decisio

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6

Cui, Jiazheng, Zhiyuan Lin, Haoyang Liu, Yunxiao Liu, and Mini Han Wang. "Enhancing Dry Eye Disease Detection Through the Application of an Ophthalmic Knowledge Graph." In Advances in Transdisciplinary Engineering. IOS Press, 2025. https://doi.org/10.3233/atde241398.

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Dry eye disease is a multifactorial condition that significantly impacts the quality of life for millions worldwide. Accurate diagnosis and treatment are often challenging due to the complex interplay of symptoms, risk factors, and underlying causes. This study presents an innovative approach to enhance dry eye detection through the development of an ophthalmic knowledge graph. By curating and organizing data from peer-reviewed literature, the knowledge graph captures key entities such as symptoms, treatments, and diagnostic methods, and systematically maps their relationships. The study emplo

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7

Celino, Irene, Mario Scrocca, and Agnese Chiatti. "Mutual Understanding Between People and Systems via Neurosymbolic AI and Knowledge Graphs." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2025. https://doi.org/10.3233/faia250241.

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This chapter investigates the concept of mutual understanding between humans and systems, positing that Neuro-symbolic Artificial Intelligence (NeSy AI) methods can significantly enhance this mutual understanding by leveraging explicit symbolic knowledge representations with data-driven learning models. We start by introducing three critical dimensions to characterize mutual understanding: sharing knowledge, exchanging knowledge, and governing knowledge. Sharing knowledge involves aligning the conceptual models of different agents to enable a shared understanding of the domain of interest. Exc

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8

Baldazzi, Teodoro, Luigi Bellomarini, and Emanuel Sallinger. "Knowledge Graph-Based Reasoning in Large Language Models." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2025. https://doi.org/10.3233/faia250219.

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The integration of Large Language Models (LLMs) with logic-based Knowledge Graphs (KGs) and more generally with Knowledge Representation and Reasoning (KRR) methodologies has rapidly emerged as a pivotal area of research. Such a synergy is aimed at enhancing transparency and accountability in AI-driven applications, which is paramount for big data processing and robust decision-making over high-stakes domains such as finance and biomedicine. Indeed, despite the adaptability and human-centric understanding that LLMs bring, they inherently lack systematic reasoning capabilities, often operating

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9

Baumgart, Andreas, and Amir Madany Mamlouk. "A Knowledge-Model for AI-Driven Tutoring Systems." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia210474.

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Анотація:

A powerful new complement to traditional synchronous teaching is emerging: intelligent tutoring systems. The narrative: A learner interacts with a digital agent. The agent reviews, selects and proposes individually tailored educational resources and processes – i.e. a meaningful succession of instructions, tests or groupwork. The aim is to make personal tutored learning the new norm in higher education – especially in groups with heterogeneous educational backgrounds. The challenge: Today, there are no suitable data that allow computer-agents to learn how to take reasonable decisions. Availabl

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10

Auer, Sören, Jennifer D’Souza, Kheir Eddine Farfar, et al. "Open Research Knowledge Graph: A Large-Scale Neuro-Symbolic Knowledge Organization System." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2025. https://doi.org/10.3233/faia250216.

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This article presents the implementation of a neuro-symbolic system within the Open Research Knowledge Graph (ORKG), a platform designed to collect and organize scientific knowledge in a structured, machine-readable format. Our approach leverages the strengths of symbolic knowledge representation to encode complex relationships and domain-specific rules, combined with the pattern recognition capabilities of neural networks to process large volumes of unstructured data, in particular scientific articles in the form of narrative text documents. With the ORKG, we developed a hybrid system that in

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Тези доповідей конференцій з теми "AI-driven knowledge graphs"

1

Alam Mozumder, Md Shahin, Md Rokibul Hasan, Mohammad Balayet Hossain Sakil, Md Amit Hasan, Arifa Akter Eva, and Jannatul Maua. "AI-Driven Financial Knowledge Graphs: Bridging Traditional Finance and Blockchain Ecosystems with Graph Neural Networks." In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2025. https://doi.org/10.1109/ecce64574.2025.11013486.

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2

Ahmad, Asiyah. "Knowledge Graphs in AI-Driven Biomedical and Chemical Engineering: A Survey of Construction, Applications, and Future Directions." In 2024 Conference on AI, Science, Engineering, and Technology (AIxSET). IEEE, 2024. https://doi.org/10.1109/aixset62544.2024.00050.

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3

Amin, Qazi Khalid, Syed Hussain Ali Shah Gillani, Syed Nasir Mehmood Shah, and Altaf Hussain. "A Generative AI-Driven CTI Framework for IDS using Machine Learning and Knowledge Graph." In 2024 26th International Multitopic Conference (INMIC). IEEE, 2024. https://doi.org/10.1109/inmic64792.2024.11004337.

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4

Meng, Han, Renwen Zhang, Ganyi Wang, et al. "Deconstructing Depression Stigma: Integrating AI-driven Data Collection and Analysis with Causal Knowledge Graphs." In CHI 2025: CHI Conference on Human Factors in Computing Systems. ACM, 2025. https://doi.org/10.1145/3706598.3714255.

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5

Yang, Pengcheng, Fuli Luo, Peng Chen, et al. "Knowledgeable Storyteller: A Commonsense-Driven Generative Model for Visual Storytelling." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/744.

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The visual storytelling (VST) task aims at generating a reasonable and coherent paragraph-level story with the image stream as input. Different from caption that is a direct and literal description of image content, the story in the VST task tends to contain plenty of imaginary concepts that do not appear in the image. This requires the AI agent to reason and associate with the imaginary concepts based on implicit commonsense knowledge to generate a reasonable story describing the image stream. Therefore, in this work, we present a commonsense-driven generative model, which aims to introduce c

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6

Dang, Daniel. "Recommendation of Topics and Practical Labs to Teach the Semantic Web in Current Bachelor of Computing Systems." In CITRENZ 2023 Conference. Unitec ePress, 2024. http://dx.doi.org/10.34074/proc.240108.

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The role of the Semantic Web in Web 3.0, the next evolution of the internet, is significant. Web 3.0 is often referred to as the ‘intelligent web’ or the ‘Semantic Web’. The Semantic Web is an essential topic in Web 3.0 because it enables data integration, enhances data search on the World Wide Web, and promotes the development of knowledge graphs that allow computers to derive new insights and generate knowledge. Teaching the Semantic Web to undergraduate students offers numerous benefits. Students gain a deep understanding of cutting-edge web technologies, technical skills in data integratio

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7

Acharya, Madhvi. "THE ROLE OF MATHEMATICS IN ADVANCING ARTIFICIAL INTELLIGENCE: A THEORETICAL AND APPLIED PERSPECTIVE." In Transforming Knowledge: A Multi-disciplinary Research on Integrative Learning Across Disciplines. BSSS Publication, 2025. https://doi.org/10.51767/ic250511.

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Mathematics is the backbone of AI. It offers the foundation for algorithms, innovations, learning models, and restores efficient decision-making processes in creating intelligent systems. This paper discusses key mathematical areas that support AI, such as linear algebra, the theory of probability, calculus, graph theory, and optimization. It illustrates how basic mathematical principles enable machine learning, deep learning, and data-driven decision-making. Additionally, the paper investigates real-time AI applications in health, finance, and autonomous systems, pointing to the intractable r

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8

Svolou, Stavroula, Fotis Aisopos, Anastasia Krithara, and Georgios Paliouras. "AI-Driven Drug Repurposing: A Knowledge Graph-based Approach for Rare Diseases." In International Drug Repurposing Conference 2025. ScienceOpen, 2025. https://doi.org/10.14293/idr.25.019ss.

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9

Omri, Safa, Wafa Omri, Elena Kalimera, and Fouad Omri. "CHAIN.CARE: A Privacy-Preserving Federated Learning Approach for AI-Driven Oncology Research Assistance." In “Rəqəmsal tibb 4.0: problemlər, imkanlar və perspektivlər” II respublika elmi-praktiki konfransı. İnformasiya Texnologiyaları İnstitutu, 2025. https://doi.org/10.25045/spcdh4.0.2025.10.

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Oncology faces an unprecedented challenge in knowledge management, with clinicians required to process over 4,000 new research publications monthly while administrative tasks consume 30-40% of their time. This paper introduces CHAIN.CARE, a specialized AI-driven medical research assistant built specifically for oncology. The system employs a novel multi-agent architecture underpinned by a semantic reasoning engine and an extensive oncology-specific knowledge graph comprising 20 million entities and 115 million relationships. Privacy and data sovereignty concerns are addressed through a federat

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10

Gemelli, Paolo, Laura Pagani, Mario Ivan Zignego, and Alessandro Bertirotti. "AI Agents as Knowledge Navigators: A Conceptual Framework for Multi-Agent Systems in Scientific Knowledge Management." In 16th International Conference on Applied Human Factors and Ergonomics (AHFE 2025). AHFE International, 2025. https://doi.org/10.54941/ahfe1006231.

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The rapid and continuous expansion of scientific literature has led to an unprecedented increase in the volume of knowledge produced, significantly complicating its organization, retrieval, and effective utilization. Researchers face considerable challenges in managing this vast information landscape, particularly in terms of identifying relevant studies, maintaining contextual integrity, and integrating knowledge across multiple disciplines. Traditional database-driven search engines and static indexing methods often fall short in addressing these issues, as they lack the capacity to dynamica

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