我们引入本体到工具的编译作为一种证明概念的机制，用于将大型语言模型（LLMs）与形式领域知识耦合。在The World Avatar（TWA）中，本体规范被编译成可执行的工具接口，这些接口必须由基于LLM的代理使用以创建和修改知识图谱实例，在生成过程中强制执行语义约束，而不是通过事后验证。在扩展TWA的语义代理组合框架中，模型上下文协议（MCP）及相关代理是知识图谱生态系统的重要组成部分，使生成模型、符号约束和外部资源之间能够进行结构化的交互。基于代理的工作流将本体转换为本体感知的工具，并迭代应用这些工具以从非结构化的科学文本中提取、验证和修复结构化知识。使用金属有机多面体合成文献作为示例案例，我们展示了可执行本体语义如何指导LLM行为并减少手动模式和提示工程，建立了将形式知识嵌入生成系统的一般范式。
英文摘要：
We introduce ontology-to-tools compilation as a proof-of-principle mechanism for coupling large language models (LLMs) with formal domain knowledge. Within The World Avatar (TWA), ontological specifications are compiled into executable tool interfaces that LLM-based agents must use to create and modify knowledge graph instances, enforcing semantic constraints during generation rather than through post-hoc validation. Extending TWA's semantic agent composition framework, the Model Context Protocol (MCP) and associated agents are integral components of the knowledge graph ecosystem, enabling structured interaction between generative models, symbolic constraints, and external resources. An agent-based workflow translates ontologies into ontology-aware tools and iteratively applies them to extract, validate, and repair structured knowledge from unstructured scientific text. Using metal-organic polyhedra synthesis literature as an illustrative case, we show how executable ontological semantics can guide LLM behaviour and reduce manual schema and prompt engineering, establishing a general paradigm for embedding formal knowledge into generative systems.
Transferable knowledge graphs with executable learned operators for algorithm design
Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST（加泰罗尼亚纳米科学与纳米技术研究所（ICN2），CSIC和BIST）；Artificial Intelligence Research Institute (IIIA-CSIC)（人工智能研究所（IIIA-CSIC））
TITAN: Graph-Executable Reasoning for Cyber Threat Intelligence
Sapienza Università di Roma（萨皮恩扎罗马大学）；Institute of Informatics and Telematics (IIT)（信息与电信学研究院）；National Research Council of Italy (CNR)（意大利国家研究委员会）；Department of Excellence in Robotics and AI（机器人与人工智能卓越部门）；TeCIP Institute（TeCIP研究所）；Scuola Superiore Sant'Anna（圣安娜高级学校）
We study confidentiality enforcement in ontologies under the Controlled Query Evaluation framework, where a policy specifies the sensitive information and a censor ensures that query answers that may compromise the policy are not returned. We focus on censors that ensure confidentiality while maximising information access, and consider both Datalog and the OWL 2 profiles as ontology languages.
Corporate Language Model (CLM): Transforming Tacit and Fragmented Enterprise Knowledge into a Sovereign, Auditable, and Executable Corporate Intelligence Layer
Ontology-Constrained Neural Reasoning in Enterprise Agentic Systems: A Neurosymbolic Architecture for Domain-Grounded AI Agents
Golden Gate University, San Francisco Foundation（金门大学，旧金山基金会）；AgenticOS (FAOS)（AgenticOS（FAOS））；Associate Director, Data, Digital & IT Novartis Healthcare Pvt. Ltd.（数据、数字与IT部门，诺华健康有限公司）；Novartis Healthcare Pvt. Ltd., Hyderabad, India（诺华健康有限公司，海得拉巴，印度）
An important use of sensors and actuator networks is to comply with health and safety policies in hazardous environments. In order to deal with increasingly large and dynamic environments, and to quickly react to emergencies, tools are needed to simplify the process of translating high-level policies into executable queries and rules. We present a framework to produce such tools, which uses rules to aggregate low-level sensor data, described using the Semantic Sensor Network Ontology, into more useful and actionable abstractions. Using the schema of the underlying data sources as an input, we …
Intent-Driven Smart Manufacturing Integrating Knowledge Graphs and Large Language Models
1Department of Software；IT Engineering, École de technologie supérieure, Montreal, Canada 2School of Electrical \& Computer Engineering, National Technical University of Athens
Epistemology is the branch of philosophy that deals with gaining knowledge. It is closely related to ontology. The branch that deals with questions like "What is real?" and "What do we know?" as it provides these components. When using modeling and simulation, we usually imply that we are doing so to either apply knowledge, in particular when we are using them for training and teaching, or that we want to gain new knowledge, for example when doing analysis or conducting virtual experiments. This paper looks at the history of science to give a context to better cope with the question, how we ca…
KAPPS: A knowledge-based CPPS Architecture for the Circular Factory
Institute for Material Handling and Logistics (IFL), Karlsruhe Institute of Technology（材料搬运与物流研究所（IFL），卡尔斯鲁厄技术大学）；Department of Production Engineering, KTH Royal Institute of Technology（生产工程系，皇家理工技术大学）；Institute of Production Science (wbk), Karlsruhe Institute of Technology（生产科学研究所（wbk），卡尔斯鲁厄技术大学）；Analytic Computing, Institute for Artificial Intelligence, University of Stuttgart（分析计算，人工智能研究所，斯图加特大学）；Electronics and Computer Science, University of Southampton（电子与计算机科学，南安普顿大学）
PACE: Provenance-Aware Capability Enforcement for Tool-Using LLM Agents
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；University of Macau（澳门大学）；National Institute of Informatics（国立信息学研究所）；University of Electronic Science and Technology of China（电子科技大学）
OntoAR-HAR: Ontology-Guided Agentic RAG with Personalized Memory for Human Activity Recognition
School of Computer Science, University of South China（南华大学计算机学院）；School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；Hunan University（湖南大学）；School of Computing, Ulster University（阿尔斯特大学计算学院）
The LLMbda Calculus: AI Agents, Conversations, and Information Flow
University of Nottingham, UK（诺丁汉大学）；University of Edinburgh, UK（爱丁堡大学）；Chalmers University of Technology（查尔姆斯理工大学）；The University of Gothenburg, Sweden（哥德堡大学）
