Ontology
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LLM-based Triplet Extraction from Financial Reports
2026-02-12 12:36:10Dante Wesslund, Ville Stenstr\"om, Pontus Linde, Alexander HolmbergAbstract
Corporate financial reports are a valuable source of structured knowledge for Knowledge Graph construction, but the lack of annotated ground truth in this domain makes evaluation difficult. We present a semi-automated pipeline for Subject-Predicate-Object triplet extraction that uses ontology-driven proxy metrics, specifically Ontology Conformance and Faithfulness, instead of ground-truth-based evaluation. We compare a static, manually engineered ontology against a fully automated, document-specific ontology induction approach across different LLMs and two corporate annual reports. The automatically induced ontology achieves 100% schema conformance in all configurations, eliminating the ontology drift observed with the manual approach. We also propose a hybrid verification strategy that combines regex matching with an LLM-as-a-judge check, reducing apparent subject hallucination rates from 65.2% to 1.6% by filtering false positives caused by coreference resolution. Finally, we identify a systematic asymmetry between subject and object hallucinations, which we attribute to passive constructions and omitted agents in financial prose.
Abstract (translated)
公司财务报告是构建知识图谱时有价值的结构化信息来源，但由于该领域缺乏标注的真实数据集，使得评估变得困难。我们提出了一种基于本体驱动的代理指标（特别是符合性和忠实性）而非基于真实数据集评价的半自动主题-谓词-客体三元组提取管道。我们在不同的大型语言模型和两份公司年度报告中比较了静态的手工设计本体与完全自动化的文档特定本体生成方法。自动化生成的本体在所有配置下均实现了100%模式符合性，消除了手动方法中的模式漂移问题。 此外，我们还提出了一种混合验证策略，结合正则表达式匹配和使用大型语言模型作为裁判进行检查的方法，通过过滤掉由共指解析引起的假阳性来降低明显主体幻觉率（从65.2%降至1.6%）。 最后，我们发现主题与客体幻觉之间存在系统性不对称现象，并将其归因于财务文本中的被动语态和省略的施动者。
URL
https://arxiv.org/abs/2602.11886
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MultiCube-RAG for Multi-hop Question Answering
2026-02-11 18:54:04Jimeng Shi, Wei Hu, Runchu Tian, Bowen Jin, Wonbin Kweon, SeongKu Kang, Yunfan Kang, Dingqi Ye, Sizhe Zhou, Shaowen Wang, Jiawei HanAbstract
Multi-hop question answering (QA) necessitates multi-step reasoning and retrieval across interconnected subjects, attributes, and relations. Existing retrieval-augmented generation (RAG) methods struggle to capture these structural semantics accurately, resulting in suboptimal performance. Graph-based RAGs structure such information in graphs, but the resulting graphs are often noisy and computationally expensive. Moreover, most methods rely on single-step retrieval, neglecting the need for multi-hop reasoning processes. Recent training-based approaches attempt to incentivize the large language models (LLMs) for iterative reasoning and retrieval, but their training processes are prone to unstable convergence and high computational overhead. To address these limitations, we devise an ontology-based cube structure with multiple and orthogonal dimensions to model structural subjects, attributes, and relations. Built on the cube structure, we propose MultiCube-RAG, a training-free method consisting of multiple cubes for multi-step reasoning and retrieval. Each cube specializes in modeling a class of subjects, so that MultiCube-RAG flexibly selects the most suitable cubes to acquire the relevant knowledge precisely. To enhance the query-based reasoning and retrieval, our method decomposes a complex multi-hop query into a set of simple subqueries along cube dimensions and conquers each of them sequentially. Experiments on four multi-hop QA datasets show that MultiCube-RAG improves response accuracy by 8.9% over the average performance of various baselines. Notably, we also demonstrate that our method performs with greater efficiency and inherent explainability.
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URL
https://arxiv.org/abs/2602.15898
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An Ontology-driven Dynamic Knowledge Base for Uninhabited Ground Vehicles
2026-02-11 06:06:18Hsan Sandar Win, Andrew Walters, Cheng-Chew Lim, Daniel Webber, Seth Leslie, Tan DoanAbstract
In this paper, the concept of Dynamic Contextual Mission Data (DCMD) is introduced to develop an ontology-driven dynamic knowledge base for Uninhabited Ground Vehicles (UGVs) at the tactical edge. The dynamic knowledge base with DCMD is added to the UGVs to: support enhanced situation awareness; improve autonomous decision making; and facilitate agility within complex and dynamic environments. As UGVs are heavily reliant on the a priori information added pre-mission, unexpected occurrences during a mission can cause identification ambiguities and require increased levels of user input. Updating this a priori information with contextual information can help UGVs realise their full potential. To address this, the dynamic knowledge base was designed using an ontology-driven representation, supported by near real-time information acquisition and analysis, to provide in-mission on-platform DCMD updates. This was implemented on a team of four UGVs that executed a laboratory based surveillance mission. The results showed that the ontology-driven dynamic representation of the UGV operational environment was machine actionable, producing contextual information to support a successful and timely mission, and contributed directly to the situation awareness.
Abstract (translated)
在这篇论文中，引入了动态情境任务数据（DCMD）的概念，旨在为战术边缘的无人地面车辆（UGVs）开发一个基于本体论的动态知识库。通过在UGVs上添加具有DCMD的动态知识库，可以支持增强的情景意识；提高自主决策能力；并促进在复杂和多变环境中灵活应对的能力。由于UGVs严重依赖于任务前预先加载的信息，在执行任务过程中出现的意外情况可能会导致识别模糊，并需要增加用户输入量。用情境信息更新这些预先存在的信息可以帮助UGVs充分发挥其潜力。 为解决这个问题，动态知识库采用基于本体论的表示方法设计，该方法通过接近实时的信息获取和分析支持，以在任务进行中提供平台上的DCMD更新。这一方案在一个由四个UGV组成的团队上实施，并执行了一项实验室环境下的监视任务。结果显示，基于本体论的动态表示法能够描述UGVs操作环境，并且这种机器可操作的方法产生了情境信息，支持了任务的成功和及时完成，并直接贡献于情况意识的提高。
URL
https://arxiv.org/abs/2602.10555
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Situation Graph Prediction: Structured Perspective Inference for User Modeling
2026-02-10 20:58:15Jisung Shin, Daniel Platnick, Marjan Alirezaie, Hossein RahnamaAbstract
Perspective-Aware AI requires modeling evolving internal states--goals, emotions, contexts--not merely preferences. Progress is limited by a data bottleneck: digital footprints are privacy-sensitive and perspective states are rarely labeled. We propose Situation Graph Prediction (SGP), a task that frames perspective modeling as an inverse inference problem: reconstructing structured, ontology-aligned representations of perspective from observable multimodal artifacts. To enable grounding without real labels, we use a structure-first synthetic generation strategy that aligns latent labels and observable traces by design. As a pilot, we construct a dataset and run a diagnostic study using retrieval-augmented in-context learning as a proxy for supervision. In our study with GPT-4o, we observe a gap between surface-level extraction and latent perspective inference--indicating latent-state inference is harder than surface extraction under our controlled setting. Results suggest SGP is non-trivial and provide evidence for the structure-first data synthesis strategy.
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URL
https://arxiv.org/abs/2602.13319
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AgentSkiller: Scaling Generalist Agent Intelligence through Semantically Integrated Cross-Domain Data Synthesis
2026-02-10 03:21:42Zexu Sun, Bokai Ji, Hengyi Cai, Shuaiqiang Wang, Lei Wang, Guangxia Li, Xu ChenAbstract
Large Language Model agents demonstrate potential in solving real-world problems via tools, yet generalist intelligence is bottlenecked by scarce high-quality, long-horizon data. Existing methods collect privacy-constrained API logs or generate scripted interactions lacking diversity, which struggle to produce data requisite for scaling capabilities. We propose AgentSkiller, a fully automated framework synthesizing multi-turn interaction data across realistic, semantically linked domains. It employs a DAG-based architecture with explicit state transitions to ensure determinism and recoverability. The pipeline builds a domain ontology and Person-Centric Entity Graph, defines tool interfaces via Service Blueprints for Model Context Protocol servers, and populates environments with consistent databases and strict Domain Policies. A cross-domain fusion mechanism links services to simulate complex tasks. Finally, the pipeline creates user tasks by verifying solution paths, filtering via execution-based validation, and generating queries using a Persona-based Simulator for automated rollout. This produces reliable environments with clear state changes. To demonstrate effectiveness, we synthesized $\approx$ 11K interaction samples; experimental results indicate that models trained on this dataset achieve significant improvements on function calling over baselines, particularly in larger parameter regimes.
Abstract (translated)
大型语言模型代理在通过工具解决现实世界问题方面展示了潜力，但通才智能的发展受限于高质量、长周期数据的稀缺。现有的方法收集受隐私限制的API日志或生成缺乏多样性的脚本化交互，难以产生扩大能力所需的数据。我们提出AgentSkiller，这是一个完全自动化的框架，用于在现实且语义关联的领域中合成多轮互动数据。 该框架采用基于有向无环图（DAG）架构，并通过明确的状态转换确保确定性和可恢复性。整个流程包括构建领域本体论和以人物为中心的实体图，定义工具接口时使用服务蓝图来为模型上下文协议服务器制定标准，并在环境中填充一致的数据库和严格的域策略。跨域融合机制链接服务以模拟复杂任务。最后，该管道通过验证解决方案路径、基于执行验证过滤并通过基于人物的仿真器生成查询来进行用户任务创建。这将产生具有明确状态变化的可靠环境。 为了证明其有效性，我们合成了约1.1万份交互样本；实验结果显示，在此数据集上训练的模型在功能调用方面相对于基线取得了显著改进，特别是在更大的参数范围内表现尤为突出。
URL
https://arxiv.org/abs/2602.09372
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FlyAOC: Evaluating Agentic Ontology Curation of Drosophila Scientific Knowledge Bases
2026-02-09 20:12:38Xingjian Zhang, Sophia Moylan, Ziyang Xiong, Qiaozhu Mei, Yichen Luo, Jiaqi W. MaAbstract
Scientific knowledge bases accelerate discovery by curating findings from primary literature into structured, queryable formats for both human researchers and emerging AI systems. Maintaining these resources requires expert curators to search relevant papers, reconcile evidence across documents, and produce ontology-grounded annotations - a workflow that existing benchmarks, focused on isolated subtasks like named entity recognition or relation extraction, do not capture. We present FlyBench to evaluate AI agents on end-to-end agentic ontology curation from scientific literature. Given only a gene symbol, agents must search and read from a corpus of 16,898 full-text papers to produce structured annotations: Gene Ontology terms describing function, expression patterns, and historical synonyms linking decades of nomenclature. The benchmark includes 7,397 expert-curated annotations across 100 genes drawn from FlyBase, the Drosophila (fruit fly) knowledge base. We evaluate four baseline agent architectures: memorization, fixed pipeline, single-agent, and multi-agent. We find that architectural choices significantly impact performance, with multi-agent designs outperforming simpler alternatives, yet scaling backbone models yields diminishing returns. All baselines leave substantial room for improvement. Our analysis surfaces several findings to guide future development; for example, agents primarily use retrieval to confirm parametric knowledge rather than discover new information. We hope FlyBench will drive progress on retrieval-augmented scientific reasoning, a capability with broad applications across scientific domains.
Abstract (translated)
科学知识库通过将原始文献中的发现整理成结构化、可查询的格式，加速了科研领域的发现过程。这些资源不仅供人类研究人员使用，还为新兴的人工智能系统提供支持。然而，维护这类资源需要专家编目人员来搜索相关论文，跨文档比对证据，并生成基于本体论的注释——这是一项现有基准测试无法完全捕捉的工作流程，因为现有的基准测试主要集中在孤立的任务上，如命名实体识别或关系抽取等。 我们提出了FlyBench这一评估AI代理在从科学文献中进行端到端代理式本体编目的方法。给定一个基因符号后，代理必须从包含16,898篇全文论文的语料库中搜索并阅读，以生成结构化的注释：描述功能、表达模式以及连接几十年命名惯例的历史同义词的Gene Ontology术语。该基准测试包含了来自FlyBase（果蝇知识库）的100个基因中的7397项专家编目的注释。 我们评估了四种基础代理架构：记忆型、固定管道型、单一代理型和多代理型。研究发现，架构选择对性能有显著影响，其中多代理设计优于简单的替代方案，但扩展模型主体的规模带来的收益却逐渐减少。所有基准测试的表现都有改进的空间。 我们的分析揭示了一些指导未来发展的关键点；例如，代理主要依靠检索来确认参数知识而不是发现新的信息。我们希望FlyBench能够推动基于检索的科学推理的进步，这一能力在各个科研领域内都有着广泛的应用前景。
URL
https://arxiv.org/abs/2602.09163
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Grounding Generative Planners in Verifiable Logic: A Hybrid Architecture for Trustworthy Embodied AI
2026-02-09 08:11:36Feiyu Wu, Xu Zheng, Yue Qu, Zhuocheng Wang, Zicheng Feng, Hui LiAbstract
Large Language Models (LLMs) show promise as planners for embodied AI, but their stochastic nature lacks formal reasoning, preventing strict safety guarantees for physical deployment. Current approaches often rely on unreliable LLMs for safety checks or simply reject unsafe plans without offering repairs. We introduce the Verifiable Iterative Refinement Framework (VIRF), a neuro-symbolic architecture that shifts the paradigm from passive safety gatekeeping to active collaboration. Our core contribution is a tutor-apprentice dialogue where a deterministic Logic Tutor, grounded in a formal safety ontology, provides causal and pedagogical feedback to an LLM planner. This enables intelligent plan repairs rather than mere avoidance. We also introduce a scalable knowledge acquisition pipeline that synthesizes safety knowledge bases from real-world documents, correcting blind spots in existing benchmarks. In challenging home safety tasks, VIRF achieves a perfect 0 percent Hazardous Action Rate (HAR) and a 77.3 percent Goal-Condition Rate (GCR), which is the highest among all baselines. It is highly efficient, requiring only 1.1 correction iterations on average. VIRF demonstrates a principled pathway toward building fundamentally trustworthy and verifiably safe embodied agents.
Abstract (translated)
大型语言模型（LLMs）在具身AI的规划方面显示出潜力，但它们随机性的特性缺乏形式化推理能力，这阻碍了其在物理部署中的严格安全保证。当前的方法通常依赖不可靠的LLM来进行安全性检查，或者干脆拒绝不安全的计划而不提供修复方案。我们提出了一个可验证的迭代细化框架（VIRF），这是一种神经符号架构，它将被动的安全守门模式转变为积极的合作模式。我们的核心贡献在于设计了一个导师-学徒对话机制，在这个机制中，确定性的逻辑导师根据形式化的安全性本体论提供因果关系和教学反馈给LLM规划器。这使得智能计划修复成为可能，而不仅仅是简单地避免问题。 我们还引入了一种可扩展的知识获取管道，该管道从现实世界的文档中综合出安全知识库，纠正现有基准中的盲点。在具有挑战性的家庭安全任务中，VIRF实现了完美的0%危险行动率（HAR）和77.3%的目标条件达成率（GCR），这两项指标都优于所有基线方法的表现。此外，VIRF的效率也很高，平均只需要1.1次修正迭代。 总的来说，VIRF展示了一种建立根本上值得信赖且可验证安全的具身代理的基本路径。
URL
https://arxiv.org/abs/2602.08373
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ONTrust: A Reference Ontology of Trust
2026-02-07 18:47:34Glenda Amaral, Tiago Prince Sales, Riccardo Baratella, Daniele Porello, Renata Guizzardi, Giancarlo GuizzardiAbstract
Trust has stood out more than ever in the light of recent innovations. Some examples are advances in artificial intelligence that make machines more and more humanlike, and the introduction of decentralized technologies (e.g. blockchains), which creates new forms of (decentralized) trust. These new developments have the potential to improve the provision of products and services, as well as to contribute to individual and collective well-being. However, their adoption depends largely on trust. In order to build trustworthy systems, along with defining laws, regulations and proper governance models for new forms of trust, it is necessary to properly conceptualize trust, so that it can be understood both by humans and machines. This paper is the culmination of a long-term research program of providing a solid ontological foundation on trust, by creating reference conceptual models to support information modeling, automated reasoning, information integration and semantic interoperability tasks. To address this, a Reference Ontology of Trust (ONTrust) was developed, grounded on the Unified Foundational Ontology and specified in OntoUML, which has been applied in several initiatives, to demonstrate, for example, how it can be used for conceptual modeling and enterprise architecture design, for language evaluation and (re)design, for trust management, for requirements engineering, and for trustworthy artificial intelligence (AI) in the context of affective Human-AI teaming. ONTrust formally characterizes the concept of trust and its different types, describes the different factors that can influence trust, as well as explains how risk emerges from trust relations. To illustrate the working of ONTrust, the ontology is applied to model two case studies extracted from the literature.
Abstract (translated)
近期的创新使信任的重要性愈加凸显。例如，人工智能的进步使得机器越来越像人类，并且去中心化技术（如区块链）的引入创造了新的信任形式。这些新发展有可能改善产品和服务的提供，同时也有助于个人和集体福祉的提升。然而，它们的应用在很大程度上依赖于信任的存在。为了构建可信赖的系统，除了定义适用于新型信任形式的相关法律、法规及合适的治理模式外，还需要准确地界定信任的概念，以便人类与机器都能理解它。本文是一系列长期研究项目的成果，旨在为信任提供坚实的本体论基础，通过创建参考概念模型来支持信息建模、自动化推理、信息集成和语义互操作性任务。 为此，开发了一种基于统一基础本体论，并用OntoUML详细规定的“信任参考本体”（ONTrust）。此本体已经在多个倡议中得到应用，例如用于概念建模和企业架构设计、语言评估与重新设计、信任管理、需求工程以及情感人机团队协作背景下的可信人工智能。ONTrust形式化地描述了信任的概念及其不同种类，并阐述了影响信任的不同因素，同时解释了如何从信任关系中产生风险。 为了展示ONTrust的工作原理，将此本体应用于两个取材于文献的案例研究建模之中。
URL
https://arxiv.org/abs/2602.07662
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LIT-GRAPH: Evaluating Deep vs. Shallow Graph Embeddings for High-Quality Text Recommendation in Domain-Specific Knowledge Graphs
2026-02-07 01:42:54Nirmal Gelal, Chloe Snow, Kathleen M. Jagodnik, Ambyr Rios, Hande K\"u\c{c}\"uk McGintyAbstract
This study presents LIT-GRAPH (Literature Graph for Recommendation and Pedagogical Heuristics), a novel knowledge graph-based recommendation system designed to scaffold high school English teachers in selecting diverse, pedagogically aligned instructional literature. The system is built upon an ontology for English literature, addressing the challenge of curriculum stagnation, where we compare four graph embedding paradigms: DeepWalk, Biased Random Walk (BRW), Hybrid (concatenated DeepWalk and BRW vectors), and the deep model Relational Graph Convolutional Network (R-GCN). Results reveal a critical divergence: while shallow models excelled in structural link prediction, R-GCN dominated semantic ranking. By leveraging relation-specific message passing, the deep model prioritizes pedagogical relevance over raw connectivity, resulting in superior, high-quality, domain-specific recommendations.
Abstract (translated)
这项研究提出了LIT-GRAPH（用于推荐和教学启发的文献图），这是一种基于知识图谱的推荐系统，旨在帮助高中英语教师选择多样且符合教育目标的教学文学作品。该系统建立在英语文学本体论的基础上，解决了课程停滞的问题，并比较了四种图嵌入范式：DeepWalk、偏置随机游走（BRW）、混合型（结合DeepWalk和BRW向量）以及深度模型关系图卷积网络（R-GCN）。研究结果揭示了一个关键的差异：浅层模型在结构链接预测方面表现出色，而R-GCN则在语义排序上占据主导地位。通过利用特定于关系的消息传递，深度模型优先考虑教育相关性而非原始连接度，在领域特定推荐的质量和实用性方面取得了卓越成果。
URL
https://arxiv.org/abs/2602.07307
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NECromancer: Breathing Life into Skeletons via BVH Animation
2026-02-06 09:52:29Mingxi Xu, Qi Wang, Zhengyu Wen, Phong Dao Thien, Zhengyu Li, Ning Zhang, Xiaoyu He, Wei Zhao, Kehong Gong, Mingyuan ZhangAbstract
Motion tokenization is a key component of generalizable motion models, yet most existing approaches are restricted to species-specific skeletons, limiting their applicability across diverse morphologies. We propose NECromancer (NEC), a universal motion tokenizer that operates directly on arbitrary BVH skeletons. NEC consists of three components: (1) an Ontology-aware Skeletal Graph Encoder (OwO) that encodes structural priors from BVH files, including joint semantics, rest-pose offsets, and skeletal topology, into skeletal embeddings; (2) a Topology-Agnostic Tokenizer (TAT) that compresses motion sequences into a universal, topology-invariant discrete representation; and (3) the Unified BVH Universe (UvU), a large-scale dataset aggregating BVH motions across heterogeneous skeletons. Experiments show that NEC achieves high-fidelity reconstruction under substantial compression and effectively disentangles motion from skeletal structure. The resulting token space supports cross-species motion transfer, composition, denoising, generation with token-based models, and text-motion retrieval, establishing a unified framework for motion analysis and synthesis across diverse morphologies. Demo page: this https URL
Abstract (translated)
运动标记化是通用运动模型的关键组成部分，然而现有的大多数方法都局限于特定物种的骨骼结构，从而限制了其在不同形态之间的适用性。为此，我们提出了NECromancer（简称NEC），这是一种能在任意BVH骨骼上直接操作的普适运动标记器。NEC由三个部分组成： 1. **Ontology-aware Skeletal Graph Encoder (OwO)**：这一模块根据来自BVH文件的数据编码结构先验知识到骨骼嵌入中，包括关节语义、静止姿态偏移和骨骼拓扑。 2. **Topology-Agnostic Tokenizer (TAT)**：这个组件将运动序列压缩为一种普遍的、与拓扑无关的离散表示形式。 3. **Unified BVH Universe (UvU)**：这是一个大规模的数据集，它汇集了来自不同形态骨骼的各种BVH动作数据。 实验结果表明，NEC在大幅度压缩的情况下仍能实现高保真度的重建，并且有效地将运动与骨骼结构分离。由此产生的标记空间支持跨物种的动作转移、组合、去噪、基于标记模型生成以及文本-动作检索，为不同形态下动作分析和合成提供了一个统一框架。 演示页面: [此链接](this https URL)
URL
https://arxiv.org/abs/2602.06548
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Performance Evaluation of LLMs in Automated RDF Knowledge Graph Generation
2026-02-06 06:30:35Ioana Ramona Martin, Tudor Cioara, Ionut Anghel, Gabriel ArcasAbstract
Cloud systems generate large, heterogeneous log data containing critical infrastructure, application, and security information. Transforming these logs into RDF triples enables their integration into knowledge graphs, improving interpretability, root-cause analysis, and cross-service reasoning beyond what raw logs allow. Large Language Models (LLMs) offer a promising approach to automate RDF knowledge graph generation; however, their effectiveness on complex cloud logs remains largely unexplored. In this paper, we evaluate multiple LLM architectures and prompting strategies for automated RDF extraction using a controlled framework with two pipelines for systematically processing semi-structured log data. The extraction pipeline integrates multiple LLMs to identify relevant entities and relationships, automatically generating subject-predicate-object triples. These outputs are evaluated using a dedicated validation pipeline with both syntactic and semantic metrics to assess accuracy, completeness, and quality. Due to the lack of public ground-truth datasets, we created a reference Log-to-KG dataset from OpenStack logs using manual annotation and ontology-driven methods, enabling objective baseline. Our analysis shows that Few-Shot learning is the most effective strategy, with Llama achieving a 99.35% F1 score and 100% valid RDF output while Qwen, NuExtract, and Gemma also perform well under Few-Shot prompting, with Chain-of-Thought approaches maintaining similar accuracy. One-Shot prompting offers a lighter but effective alternative, while Zero-Shot and advanced strategies such as Tree-of-Thought, Self-Critique, and Generate-Multiple perform substantially worse. These results highlight the importance of contextual examples and prompt design for accurate RDF extraction and reveal model-specific limitations across LLM architectures.
Abstract (translated)
云系统会产生包含关键基础设施、应用程序及安全信息的大规模异构日志数据。将这些日志转换为RDF三元组可使其融入知识图谱，从而在原始日志的基础上提升可解释性、根因分析能力以及跨服务推理能力。大语言模型（LLMs）为自动化生成RDF知识图谱提供了一种前景可期的方法；然而，其处理复杂云日志的有效性尚待深入探索。本文通过一个受控框架评估了多种LLM架构与提示策略，该框架包含两条流水线，用于系统化处理半结构化日志数据。提取流水线整合多个LLM以识别相关实体与关系，自动生成主谓宾三元组。这些输出通过专门的验证流水线进行评估，该流水线采用句法与语义双重指标，以衡量其准确性、完整性与质量。由于缺乏公开的基准真值数据集，我们基于OpenStack日志，通过人工标注与本体驱动方法构建了参考性的Log-to-KG数据集，从而确立了客观的基线。分析表明，小样本学习是最有效的策略，其中Llama实现了99.35%的F1分数与100%的有效RDF输出；而Qwen、NuExtract与Gemma在小样本提示下同样表现优异，思维链方法也保持了相近的准确率。单样本提示提供了一种更轻量但有效的替代方案，而零样本及树思维、自我批评、生成多项等高级策略则表现显著较差。这些结果凸显了上下文示例与提示设计对准确提取RDF的重要性，并揭示了不同LLM架构中模型特有的局限性。
URL
https://arxiv.org/abs/2603.29878
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PhenoLIP: Integrating Phenotype Ontology Knowledge into Medical Vision-Language Pretraining
2026-02-05 20:44:07Cheng Liang, Chaoyi Wu, Weike Zhao, Ya Zhang, Yanfeng Wang, Weidi XieAbstract
Recent progress in large-scale CLIP-like vision-language models(VLMs) has greatly advanced medical image analysis. However, most existing medical VLMs still rely on coarse image-text contrastive objectives and fail to capture the systematic visual knowledge encoded in well-defined medical phenotype ontologies. To address this gap, we construct PhenoKG, the first large-scale, phenotype-centric multimodal knowledge graph that encompasses over 520K high-quality image-text pairs linked to more than 3,000 phenotypes. Building upon PhenoKG, we propose PhenoLIP, a novel pretraining framework that explicitly incorporates structured phenotype knowledge into medical VLMs through a two-stage process. We first learn a knowledge-enhanced phenotype embedding space from textual ontology data and then distill this structured knowledge into multimodal pretraining via a teacher-guided knowledge distillation objective. To support evaluation, we further introduce PhenoBench, an expert-verified benchmark designed for phenotype recognition, comprising over 7,800 image--caption pairs covering more than 1,000 phenotypes. Extensive experiments demonstrate that PhenoLIP outperforms previous state-of-the-art baselines, improving upon BiomedCLIP in phenotype classification accuracy by 8.85\% and BIOMEDICA in cross-modal retrieval by 15.03%, underscoring the value of integrating phenotype-centric priors into medical VLMs for structured and interpretable medical image understanding.
Abstract (translated)
近期在大规模类似CLIP的视觉-语言模型（VLMs）方面取得的进步极大地推动了医学图像分析的发展。然而，大多数现有的医疗VLM仍然依赖于粗略的图像文本对比目标，并且无法捕捉到在定义明确的医学表型本体论中编码的系统性视觉知识。为了填补这一空白，我们构建了PhenoKG，这是首个大规模、以表型为中心的多模态知识图谱，涵盖了超过520,000张高质量的图像-文本对，并且这些数据链接到了3,000多个不同的表型上。 基于PhenoKG，我们提出了一个新的预训练框架——PhenoLIP。这个框架通过一个两阶段过程明确地将结构化的表型知识整合到医疗VLMs中。首先，我们从文本本体数据中学得增强的表型嵌入空间，并且然后通过一种教师指导的知识蒸馏目标将其结构化知识浓缩进多模态预训练。 为了支持评估，我们进一步引入了PhenoBench，这是一个经过专家验证的基准测试集，专门用于表型识别，包含超过7,800张图像-描述对，涵盖了1,000多个不同的表型。大量的实验表明，PhenoLIP超越了之前最先进的基线模型，在表型分类准确率上比BiomedCLIP高出8.85%，在跨模态检索任务上比BIOMEDICA高出15.03%。这突显出将基于中心的先验知识整合到医疗VLMs中的重要性，以实现结构化和可解释的医学图像理解。
URL
https://arxiv.org/abs/2602.06184
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Generative Ontology: When Structured Knowledge Learns to Create
Abstract
Traditional ontologies excel at describing domain structure but cannot generate novel artifacts. Large language models generate fluently but produce outputs that lack structural validity, hallucinating mechanisms without components, goals without end conditions. We introduce Generative Ontology, a framework that synthesizes these complementary strengths: ontology provides the grammar; the LLM provides the creativity. Generative Ontology encodes domain knowledge as executable Pydantic schemas that constrain LLM generation via DSPy signatures. A multi-agent pipeline assigns specialized roles to different ontology domains: a Mechanics Architect designs game systems, a Theme Weaver integrates narrative, a Balance Critic identifies exploits. Each agent carrying a professional "anxiety" that prevents shallow, agreeable outputs. Retrieval-augmented generation grounds novel designs in precedents from existing exemplars, while iterative validation ensures coherence between mechanisms and components. We demonstrate the framework through GameGrammar, a system for generating complete tabletop game designs. Given a thematic prompt ("bioluminescent fungi competing in a cave ecosystem"), the pipeline produces structurally complete, playable game specifications with mechanisms, components, victory conditions, and setup instructions. These outputs satisfy ontological constraints while remaining genuinely creative. The pattern generalizes beyond games. Any domain with expert vocabulary, validity constraints, and accumulated exemplars (music composition, software architecture, culinary arts) is a candidate for Generative Ontology. We argue that constraints do not limit creativity but enable it: just as grammar makes poetry possible, ontology makes structured generation possible.
Abstract (translated)
传统的本体（Ontologies）擅长描述领域结构，但无法生成新的创意作品。大型语言模型则能够流畅地创作内容，但其产出往往缺乏结构性的有效性，会凭空创造出没有组成部分的机制或没有明确结束条件的目标。为此，我们引入了一种名为“生成式本体”(Generative Ontology) 的框架，该框架融合了这两者的优势：本体提供了语法和结构规则；大型语言模型则提供创造力和创新思维。 **生成式本体**通过将领域知识编码为可执行的Pydantic模式来实现这一点，这些模式使用DSPy签名约束大型语言模型的生成过程。一个基于多代理系统的管道能够分配不同的角色给不同领域的本体：例如，“力学架构师”设计游戏系统、“主题编织者”整合叙事元素、以及“平衡评论家”发现并解决潜在的问题或漏洞。每个代理都带有专业上的“焦虑感”，这确保了它们不会产生浅薄且迎合的输出，而是提供深刻和有价值的贡献。 **检索增强生成（Retrieval-augmented generation）**使新颖的设计基于现有范例进行锚定，并通过迭代验证确保机制与组件之间的连贯性。我们通过一个名为GameGrammar的游戏设计系统演示了该框架的应用：给出一个主题提示（例如“发光真菌在洞穴生态系统中的竞争”），该管道能够生成具有完整结构和可玩性的游戏规范，包括机制、组成部分、胜利条件以及设置说明等。 这些输出不仅满足本体的约束性要求，还保持了真实的创新性和创造力。这种模式的应用范围超出了游戏设计领域：任何具备专家词汇、有效性限制且积累了大量范例的领域（如音乐创作、软件架构或烹饪艺术）都可以采用生成式本体的方法来促进创意的结构性产出。 我们主张，这些约束不仅不会限制创造力，反而会使其成为可能。就像文法使诗歌得以实现一样，本体结构为有组织的设计创新提供了可能性。
URL
https://arxiv.org/abs/2602.05636
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Ontology-Driven Robotic Specification Synthesis
2026-02-05 08:59:23Maksym Figat, Ryan M. Mackey, Michel D. InghamAbstract
This paper addresses robotic system engineering for safety- and mission-critical applications by bridging the gap between high-level objectives and formal, executable specifications. The proposed method, Robotic System Task to Model Transformation Methodology (RSTM2) is an ontology-driven, hierarchical approach using stochastic timed Petri nets with resources, enabling Monte Carlo simulations at mission, system, and subsystem levels. A hypothetical case study demonstrates how the RSTM2 method supports architectural trades, resource allocation, and performance analysis under uncertainty. Ontological concepts further enable explainable AI-based assistants, facilitating fully autonomous specification synthesis. The methodology offers particular benefits to complex multi-robot systems, such as the NASA CADRE mission, representing decentralized, resource-aware, and adaptive autonomous systems of the future.
Abstract (translated)
本文探讨了在安全性和任务关键应用中机器人系统工程的问题，通过连接高层次目标和形式化、可执行规范之间的差距来解决这一问题。提出的方法是“机器人系统任务到模型转换方法”（Robotic System Task to Model Transformation Methodology, 简称RSTM2），这是一种以本体论为驱动的分层方法，使用带有资源的随机定时Petri网，支持在任务、系统和子系统的各个层次上进行蒙特卡洛模拟。一个假设案例研究展示了RSTM2如何支持架构选择、资源配置以及不确定条件下的性能分析。通过运用本体论概念，还可以实现基于解释性人工智能的辅助工具，从而促进完全自主规范合成。该方法特别适用于复杂的多机器人系统，例如NASA的CADRE任务，它代表了未来去中心化、资源感知型和自适应自动系统的趋势。
URL
https://arxiv.org/abs/2602.05456
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Multiclass Hate Speech Detection with RoBERTa-OTA: Integrating Transformer Attention and Graph Convolutional Networks
2026-02-03 23:14:24Mahmoud Abusaqer, Jamil SaquerAbstract
Multiclass hate speech detection across demographic categories remains computationally challenging due to implicit targeting strategies and linguistic variability in social media content. Existing approaches rely solely on learned representations from training data, without explicitly incorporating structured ontological frameworks that can enhance classification through formal domain knowledge integration. We propose RoBERTa-OTA, which introduces ontology-guided attention mechanisms that process textual features alongside structured knowledge representations through enhanced Graph Convolutional Networks. The architecture combines RoBERTa embeddings with scaled attention layers and graph neural networks to integrate contextual language understanding with domain-specific semantic knowledge. Evaluation across 39,747 balanced samples using 5-fold cross-validation demonstrates significant performance gains over baseline RoBERTa implementations and existing state-of-the-art methods. RoBERTa-OTA achieves 96.04\% accuracy compared to 95.02\% for standard RoBERTa, with substantial improvements for challenging categories: gender-based hate speech detection improves by 2.36 percentage points while other hate speech categories improve by 2.38 percentage points. The enhanced architecture maintains computational efficiency with only 0.33\% parameter overhead, providing practical advantages for large-scale content moderation applications requiring fine-grained demographic hate speech classification.
Abstract (translated)
多类别仇恨言论检测在不同的人口统计类别之间仍然面临着计算上的挑战，主要是由于隐性的目标策略和社交媒体内容中的语言变化。现有的方法仅依赖于从训练数据中学到的表示，而没有明确地引入结构化的本体框架来通过正式领域知识的整合来增强分类效果。我们提出了RoBERTa-OTA，这是一种引入了基于本体指导注意机制的方法，该机制处理文本特征的同时结合了结构化知识表示，并通过改进后的图卷积网络实现这一过程。这种架构将RoBERTa嵌入与扩展的注意力层和图形神经网络相结合，从而将上下文语言理解与特定领域的语义知识整合起来。 在使用5倍交叉验证评估的39,747个平衡样本上进行的测试表明，相较于标准RoBERTa实现及现有最先进的方法，RoBERTa-OTA展示了显著的性能提升。RoBERTa-OTA实现了96.04%的准确率，而标准RoBERTa则为95.02%，对于性别仇恨言论检测和其他仇恨言论类别分别提高了2.36和2.38个百分点。 改进后的架构在仅增加0.33%参数开销的情况下保持了计算效率，这为需要进行细致的人口统计仇恨言论分类的大规模内容审核应用提供了实用优势。
URL
https://arxiv.org/abs/2603.04414
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Ethical Asymmetry in Human-Robot Interaction - An Empirical Test of Sparrow's Hypothesis
2026-02-02 19:59:02Minyi Wang, Christoph Bartneck, Michael-John Turp, David KaberAbstract
The ethics of human-robot interaction (HRI) have been discussed extensively based on three traditional frameworks: deontology, consequentialism, and virtue ethics. We conducted a mixed within/between experiment to investigate Sparrow's proposed ethical asymmetry hypothesis in human treatment of robots. The moral permissibility of action (MPA) was manipulated as a subject grouping variable, and virtue type (prudence, justice, courage, and temperance) was controlled as a within-subjects factor. We tested moral stimuli using an online questionnaire with Perceived Moral Permissibility of Action (PMPA) and Perceived Virtue Scores (PVS) as response measures. The PVS measure was based on an adaptation of the established Questionnaire on Cardinal Virtues (QCV), while the PMPA was based on Malle et al. [39] work. We found that the MPA significantly influenced the PMPA and perceived virtue scores. The best-fitting model to describe the relationship between PMPA and PVS was cubic, which is symmetrical in nature. Our study did not confirm Sparrow's asymmetry hypothesis. The adaptation of the QCV is expected to have utility for future studies, pending additional psychometric property assessments.
Abstract (translated)
人类与机器人互动（HRI）的伦理问题已经基于三种传统框架进行了广泛的讨论：义务论、功利主义和美德伦理。我们进行了一项混合的组内/组间实验，以探讨Sparrow提出的关于人们对待机器人的伦理不对称假设。通过操纵道德行为的可接受性（MPA）作为分组变量，并控制美德类型（审慎、正义、勇气和节制）作为组内因素，我们使用在线问卷测试了道德刺激，其中采用了行动的道德许可度感知（PMPA）和感知美德得分（PVS）作为响应措施。PVS测量基于对公认的美德量表（QCV）进行了改编，而PMPA则是基于Malle等人[39]的工作进行的。 我们发现，MPA显著影响了PMPA和感知到的美德得分。描述PMPA与PVS之间关系的最佳拟合模型是三次方程，这种关系具有对称性特征。我们的研究没有证实Sparrow提出的不对称假设。预期QCV的改编版本将对未来的研究有应用价值，但需进行进一步的心理测量属性评估以确认其有效性。
URL
https://arxiv.org/abs/2602.02745
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DOGMA: Weaving Structural Information into Data-centric Single-cell Transcriptomics Analysis
2026-02-02 09:10:09Ru Zhang, Xunkai Li, Yaxin Deng, Sicheng Liu, Daohan Su, Qiangqiang Dai, Hongchao Qin, Rong-Hua Li, Guoren Wang, Jia LiAbstract
Recently, data-centric AI methodology has been a dominant paradigm in single-cell transcriptomics analysis, which treats data representation rather than model complexity as the fundamental bottleneck. In the review of current studies, earlier sequence methods treat cells as independent entities and adapt prevalent ML models to analyze their directly inherited sequence data. Despite their simplicity and intuition, these methods overlook the latent intercellular relationships driven by the functional mechanisms of biological systems and the inherent quality issues of the raw sequence data. Therefore, a series of structured methods has emerged. Although they employ various heuristic rules to capture intricate intercellular relationships and enhance the raw sequencing data, these methods often neglect biological prior knowledge. This omission incurs substantial overhead and yields suboptimal graph representations, thereby hindering the utility of ML models. To address them, we propose DOGMA, a holistic data-centric framework designed for the structural reshaping and semantic enhancement of raw data through multi-level biological prior knowledge. Transcending reliance on stochastic heuristics, DOGMA redefines graph construction by integrating Statistical Anchors with Cell Ontology and Phylogenetic Trees to enable deterministic structure discovery and robust cross-species alignment. Furthermore, Gene Ontology is utilized to bridge the feature-level semantic gap by incorporating functional priors. In complex multi-species and multi-organ benchmarks, DOGMA achieves SOTA performance, exhibiting superior zero-shot robustness and sample efficiency while operating with significantly lower computational cost.
Abstract (translated)
URL
https://arxiv.org/abs/2602.01839
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LingLanMiDian: Systematic Evaluation of LLMs on TCM Knowledge and Clinical Reasoning
2026-02-02 08:02:25Rui Hua, Yu Wei, Zixin Shu, Kai Chang, Dengying Yan, Jianan Xia, Zeyu Liu, Hui Zhu, Shujie Song, Mingzhong Xiao, Xiaodong Li, Dongmei Jia, Zhuye Gao, Yanyan Meng, Naixuan Zhao, Yu Fu, Haibin Yu, Benman Yu, Yuanyuan Chen, Fei Dong, Zhizhou Meng, Pengcheng Yang, Songxue Zhao, Lijuan Pei, Yunhui Hu, Kan Ding, Jiayuan Duan, Wenmao Yin, Yang Gu, Runshun Zhang, Qiang Zhu, Jian Yu, Jiansheng Li, Baoyan Liu, Wenjia Wang, Xuezhong ZhouAbstract
Large language models (LLMs) are advancing rapidly in medical NLP, yet Traditional Chinese Medicine (TCM) with its distinctive ontology, terminology, and reasoning patterns requires domain-faithful evaluation. Existing TCM benchmarks are fragmented in coverage and scale and rely on non-unified or generation-heavy scoring that hinders fair comparison. We present the LingLanMiDian (LingLan) benchmark, a large-scale, expert-curated, multi-task suite that unifies evaluation across knowledge recall, multi-hop reasoning, information extraction, and real-world clinical decision-making. LingLan introduces a consistent metric design, a synonym-tolerant protocol for clinical labels, a per-dataset 400-item Hard subset, and a reframing of diagnosis and treatment recommendation into single-choice decision recognition. We conduct comprehensive, zero-shot evaluations on 14 leading open-source and proprietary LLMs, providing a unified perspective on their strengths and limitations in TCM commonsense knowledge understanding, reasoning, and clinical decision support; critically, the evaluation on Hard subset reveals a substantial gap between current models and human experts in TCM-specialized reasoning. By bridging fundamental knowledge and applied reasoning through standardized evaluation, LingLan establishes a unified, quantitative, and extensible foundation for advancing TCM LLMs and domain-specific medical AI research. All evaluation data and code are available at this https URL and this http URL.
Abstract (translated)
URL
https://arxiv.org/abs/2602.01779
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LLM-Driven Ontology Construction for Enterprise Knowledge Graphs
2026-02-01 15:13:30Abdulsobur Oyewale, Tommaso SoruAbstract
Enterprise Knowledge Graphs have become essential for unifying heterogeneous data and enforcing semantic governance. However, the construction of their underlying ontologies remains a resource-intensive, manual process that relies heavily on domain expertise. This paper introduces OntoEKG, a LLM-driven pipeline designed to accelerate the generation of domain-specific ontologies from unstructured enterprise data. Our approach decomposes the modelling task into two distinct phases: an extraction module that identifies core classes and properties, and an entailment module that logically structures these elements into a hierarchy before serialising them into standard RDF. Addressing the significant lack of comprehensive benchmarks for end-to-end ontology construction, we adopt a new evaluation dataset derived from documents across the Data, Finance, and Logistics sectors. Experimental results highlight both the potential and the challenges of this approach, achieving a fuzzy-match F1-score of 0.724 in the Data domain while revealing limitations in scope definition and hierarchical reasoning.
Abstract (translated)
URL
https://arxiv.org/abs/2602.01276
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From Prompt to Graph: Comparing LLM-Based Information Extraction Strategies in Domain-Specific Ontology Development
2026-01-31 12:50:23Xuan Liu, Ziyu Li, Mu He, Ziyang Ma, Xiaoxu Wu, Gizem Yilmaz, Yiyuan Xia, Bingbing Li, He Tan, Jerry Ying Hsi Fuh, Wen Feng Lu, Anders E. W. Jarfors, Per JanssonAbstract
Ontologies are essential for structuring domain knowledge, improving accessibility, sharing, and reuse. However, traditional ontology construction relies on manual annotation and conventional natural language processing (NLP) techniques, making the process labour-intensive and costly, especially in specialised fields like casting manufacturing. The rise of Large Language Models (LLMs) offers new possibilities for automating knowledge extraction. This study investigates three LLM-based approaches, including pre-trained LLM-driven method, in-context learning (ICL) method and fine-tuning method to extract terms and relations from domain-specific texts using limited data. We compare their performances and use the best-performing method to build a casting ontology that validated by domian expert.
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URL
https://arxiv.org/abs/2602.00699
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