From Prompts to Context: An Ontology-Driven Framework for Human-Generative AI Collaboration
Gamaizer；Université de technologie de Compiègne, CNRS, Heudiasyc（法国图尔学院、CNRS、Heudiasyc）；Sorbonne Université, CNRS UMR 7585, LPMHE（索邦大学、CNRS UMR 7585、LPMHE）
Ngoc Luyen Le, Marie-Hélène Abel, Bertrand Laforge
Collaborations with Generative AI often begin with a short prompt and end with an opaque output, leaving implicit who was involved, what task was being pursued, which resources were used, and which constraints should have shaped the process. This limited contextual explicitness hinders trust, traceability, and accountability, particularly when Generative AI is embedded in information-intensive workflows such as search, querying, and profile management. This paper introduces From Prompts to Context, an ontology-driven framework for representing Human-Generative AI collaboration. Its core component, the Contextual Collaboration AI Ontology (CCAI), models key elements of collaboration - including tasks, agent roles, resources, and constraints - as a shared machine-interpretable vocabulary. By combining populated CCAI instances with SPARQL-based context retrieval in operational workflows, the framework turns otherwise ephemeral prompt-response interactions into structured and queryable collaboration traces linking prompts, outputs, and their surrounding context. The approach is illustrated through a case study involving a software development team building a competency-based education feature for viewing and updating learner competency profiles. The case study shows how the framework can support the representation and documentation of collaboration episodes across requirements analysis, design, implementation, and testing. Within this setting, the results indicate that explicit collaboration modelling helps make task context more explicit, improves the traceability of AI-generated contributions, and supports more transparent and accountable Human-Generative AI practices. We conclude by outlining design principles for future Human-Generative AI systems that emphasise not only output quality, but also the explicit representation of the collaborative context in which outputs are produced.
Zhejiang University（浙江大学）；AZFT Joint Lab for Knowledge Engine（AZFT知识引擎联合实验室）；Hangzhou Innovation Center（杭州创新中心）；Alibaba Group（阿里巴巴集团）；Tecent（腾讯）
Few-shot Learning (FSL) is aimed to make predictions based on a limited number of samples. Structured data such as knowledge graphs and ontology libraries has been leveraged to benefit the few-shot setting in various tasks. However, the priors adopted by the existing methods suffer from challenging knowledge missing, knowledge noise, and knowledge heterogeneity, which hinder the performance for few-shot learning. In this study, we explore knowledge injection for FSL with pre-trained language models and propose ontology-enhanced prompt-tuning (OntoPrompt). Specifically, we develop the ontology …
OntoChatGPT Information System: Ontology-Driven Structured Prompts for ChatGPT Meta-Learning
Glushkov Institute of Cybernetics of the National Academy of Sciences of Ukraine（乌克兰国家科学院格鲁什科夫控制论研究所）；Frantsevic Institute for Problems in Material Science of the National Academy of Sciences of Ukraine（乌克兰国家科学院弗兰采维奇材料科学问题研究所）
Leveraging LLMs for Collaborative Ontology Engineering in Parkinson Disease Monitoring and Alerting
Intelligent Systems Laboratory, Department of Cultural Technology and Communication, University of the Aegean（爱琴海大学智能系统实验室）；Artificial Intelligence Laboratory, Department of Digital Systems, University of Piraeus（比雷埃克斯大学人工智能实验室）
The process of building ontologies is a difficult task that involves collaboration between ontology developers and domain experts and requires an ongoing interaction between them. This collaboration is made more difficult, because they tend to use different tool sets, which can hamper this interaction. In this paper, we propose to decrease this distance between domain experts and ontology developers by creating more readable forms of ontologies, and further to enable editing in normal office environments. Building on a programmatic ontology development environment, such as Tawny-OWL, we are no…
Graz University of Technology（格拉茨工业大学）；GESIS - Leibniz Institute for the Social Sciences（GESIS——莱布尼茨社会科学研究所）；University of Koblenz-Landau（科布伦茨-兰道大学）；Stanford University（斯坦福大学）
Biomedical taxonomies, thesauri and ontologies in the form of the International Classification of Diseases (ICD) as a taxonomy or the National Cancer Institute Thesaurus as an OWL-based ontology, play a critical role in acquiring, representing and processing information about human health. With increasing adoption and relevance, biomedical ontologies have also significantly increased in size. For example, the 11th revision of the ICD, which is currently under active development by the WHO contains nearly 50,000 classes representing a vast variety of different diseases and causes of death. This…
Kansas State University（堪萨斯州立大学）；Atmosphere and Space Technology Research Associates (ASTRA LLC)（大气与空间技术研究协会（ASTRA有限责任公司））；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
The interactions between the Sun, interplanetary space, near Earth space environment, the Earth's surface, and the power grid are, perhaps unsurprisingly, very complicated. The study of such requires the collaboration between many different organizations spanning the public and private sectors. Thus, an important component of studying space weather is the integration and analysis of heterogeneous information. As such, we have developed a modular ontology to drive the core of the data integration and serve the needs of a highly interdisciplinary community. This paper presents our preliminary mo…
Chiang Mai University（清迈大学）；University Lumiere Lyon 2（里昂第二大学）
Knowledge-based economy forces companies in the nation to group together as a cluster in order to maintain their competitiveness in the world market. The cluster development relies on two key success factors which are knowledge sharing and collaboration between the actors in the cluster. Thus, our study tries to propose knowledge management system to support knowledge management activities within the cluster. To achieve the objectives of this study, ontology takes a very important role in knowledge management process in various ways; such as building reusable and faster knowledge-bases, better…
Ontology-based knowledge representation for bone disease diagnosis: a foundation for safe and sustainable medical artificial intelligence systems
University of Science, VNUHCM（胡志明市国家大学科学大学）；Viet Nam National University, Ho Chi Minh City（越南国家大学胡志明市分校）；Ho Chi Minh City Hospital for Traumatology and Orthopedics（胡志明市创伤与骨科医院）
The Open University（开放大学）；Springer-Verlag GmbH（施普林格-维尔格有限公司）
Major academic publishers need to be able to analyse their vast catalogue of products and select the best items to be marketed in scientific venues. This is a complex exercise that requires characterising with a high precision the topics of thousands of books and matching them with the interests of the relevant communities. In Springer Nature, this task has been traditionally handled manually by publishing editors. However, the rapid growth in the number of scientific publications and the dynamic nature of the Computer Science landscape has made this solution increasingly inefficient. We have …
An Integrated Framework of Prompt Engineering and Multidimensional Knowledge Graphs for Legal Dispute Analysis
School of Software, Yunnan University（软件学院，云南大学）；Yunnan Provincial Key Laboratory of Software Engineering（云南省软件工程重点实验室）；School of Law, Yunnan University（法学院，云南大学）
São Carlos Institute of Physics, University of São Paulo（圣保罗大学圣卡洛斯物理研究所）；Cidade Democrática, Instituto Seva（民主城市，塞瓦研究所）；National Secretariat of Social Participation, General Secretariat of the Republic（共和国总秘书处国家社会参与秘书处）；Institute of Mathematical and Computer Sciences, University of São Paulo（圣保罗大学数学与计算机科学研究所）
Participatory democracy advances in virtually all governments and especially in South America which exhibits a mixed culture and social predisposition. This article presents the "Social Participation Ontology" (OPS from the Brazilian name \emph{Ontologia de Participação Social}) implemented in compliance with the Web Ontology Language standard (OWL) for fostering social participation, specially in virtual platforms. The entities and links of OPS were defined based on an extensive collaboration of specialists. It is shown that OPS is instrumental for information retrieval from the contents of t…
EBM WebSourcing；Université de Toulouse-Mines d’Albi（图卢兹-阿尔比矿业大学）
This paper presents an ontology-based approach for the design of a collaborative business process model (CBP). This CBP is considered as a specification of needs in order to build a collaboration information system (CIS) for a network of organisations. The study is a part of a model driven engineering approach of the CIS in a specific enterprise interoperability framework that will be summarised. An adaptation of the Business Process Modeling Notation (BPMN) is used to represent the CBP model. We develop a knowledge-based system (KbS) which is composed of three main parts: knowledge gathering,…
MSLE: An ontology for Materials Science Laboratory Equipment. Large-Scale Devices for Materials Characterization
Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）；Helmholtz Institute Ulm for Electrochemical Energy Storage (HIU)（乌尔姆亥姆霍兹电化学储能研究所）；Technical University Darmstadt（达姆施塔特工业大学）
EPPC-OASIS: Ontology-Aware Adaptation and Structured Inference Refinement for Electronic Patient-Provider Communication Mining in Secure Messages
Yale University（耶鲁大学）；Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland Clinic（克利夫兰医学中心勒纳医学院，克利夫兰医学中心）；Medical Oncology, Yale School of Medicine（耶鲁医学院医学肿瘤学）
Enriching Ontologies with Disjointness Axioms using Large Language Models
Ghent University（根特大学）；University of Tours（图尔大学）；University of Stuttgart（斯图加特大学）；University of Cagliari（卡利亚里大学）；University of Pisa（比萨大学）；University of Bologna（博洛尼亚大学）
Extracting ontology-compliant knowledge from scientific text describing irradiated materials using large language models
IBM Research（IBM研究院）；Univ. of Granada, Spain（格拉纳达大学）；IFMIF DONES, Spain（IFMIF DONES）；Instituto de Ciencia de Materiales de Madrid (ICMM)（马德里材料科学研究所）；CSIC, Spain（西班牙国家研究委员会）；Universidad de Alicante, Spain（阿利坎特大学）
Ontology-Amplified Distillation and Contextuality Auditing for Sovereign Enterprise Language Models: A Combined Proof-of-Mechanism and Negative-Results Method Study
Ontology-aligned structuring and reuse of multimodal materials data and workflows towards automatic reproduction
Interdisciplinary Centre for Advanced Materials Simulation, Ruhr-Universität Bochum（波鸿鲁尔大学先进材料模拟跨学科中心）；Max Planck Institute for Sustainable Materials（马克斯·普朗克可持续材料研究所）；BAM Federal Institute for Materials Research and Testing（BAM联邦材料研究与测试研究所）
