Ontology
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Toward Culturally Aligned LLMs through Ontology-Guided Multi-Agent Reasoning
2026-01-29 13:31:45Wonduk Seo, Wonseok Choi, Junseo Koh, Juhyeon Lee, Hyunjin An, Minhyeong Yu, Jian Park, Qingshan Zhou, Seunghyun Lee, Yi BuAbstract
Large Language Models (LLMs) increasingly support culturally sensitive decision making, yet often exhibit misalignment due to skewed pretraining data and the absence of structured value representations. Existing methods can steer outputs, but often lack demographic grounding and treat values as independent, unstructured signals, reducing consistency and interpretability. We propose OG-MAR, an Ontology-Guided Multi-Agent Reasoning framework. OG-MAR summarizes respondent-specific values from the World Values Survey (WVS) and constructs a global cultural ontology by eliciting relations over a fixed taxonomy via competency questions. At inference time, it retrieves ontology-consistent relations and demographically similar profiles to instantiate multiple value-persona agents, whose outputs are synthesized by a judgment agent that enforces ontology consistency and demographic proximity. Experiments on regional social-survey benchmarks across four LLM backbones show that OG-MAR improves cultural alignment and robustness over competitive baselines, while producing more transparent reasoning traces.
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URL
https://arxiv.org/abs/2601.21700
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AIdentifyAGE Ontology for Decision Support in Forensic Dental Age Assessment
2026-01-28 11:42:32Renato Marcelo, Ana Rodrigues, Cristiana Palmela Pereira, Ant\'onio Figueiras, Rui Santos, Jos\'e Rui Figueira, Alexandre P Francisco, C\'atia VazAbstract
Age assessment is crucial in forensic and judicial decision-making, particularly in cases involving undocumented individuals and unaccompanied minors, where legal thresholds determine access to protection, healthcare, and judicial procedures. Dental age assessment is widely recognized as one of the most reliable biological approaches for adolescents and young adults, but current practices are challenged by methodological heterogeneity, fragmented data representation, and limited interoperability between clinical, forensic, and legal information systems. These limitations hinder transparency and reproducibility, amplified by the increasing adoption of AI- based methods. The AIdentifyAGE ontology is domain-specific and provides a standardized, semantically coherent framework, encompassing both manual and AI-assisted forensic dental age assessment workflows, and enabling traceable linkage between observations, methods, reference data, and reported outcomes. It models the complete medico-legal workflow, integrating judicial context, individual-level information, forensic examination data, dental developmental assessment methods, radiographic imaging, statistical reference studies, and AI-based estimation methods. It is being developed together with domain experts, and it builds on upper and established biomedical, dental, and machine learning ontologies, ensuring interoperability, extensibility, and compliance with FAIR principles. The AIdentifyAGE ontology is a fundamental step to enhance consistency, transparency, and explainability, establishing a robust foundation for ontology-driven decision support systems in medico-legal and judicial contexts.
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URL
https://arxiv.org/abs/2602.16714
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SynCABEL: Synthetic Contextualized Augmentation for Biomedical Entity Linking
2026-01-27 14:47:17Adam Remaki, Christel G\'erardin, Eul\`alia Farr\'e-Maduell, Martin Krallinger, Xavier TannierAbstract
We present SynCABEL (Synthetic Contextualized Augmentation for Biomedical Entity Linking), a framework that addresses a central bottleneck in supervised biomedical entity linking (BEL): the scarcity of expert-annotated training data. SynCABEL leverages large language models to generate context-rich synthetic training examples for all candidate concepts in a target knowledge base, providing broad supervision without manual annotation. We demonstrate that SynCABEL, when combined with decoder-only models and guided inference establish new state-of-the-art results across three widely used multilingual benchmarks: MedMentions for English, QUAERO for French, and SPACCC for Spanish. Evaluating data efficiency, we show that SynCABEL reaches the performance of full human supervision using up to 60% less annotated data, substantially reducing reliance on labor-intensive and costly expert labeling. Finally, acknowledging that standard evaluation based on exact code matching often underestimates clinically valid predictions due to ontology redundancy, we introduce an LLM-as-a-judge protocol. This analysis reveals that SynCABEL significantly improves the rate of clinically valid predictions. Our synthetic datasets, models, and code are released to support reproducibility and future research.
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URL
https://arxiv.org/abs/2601.19667
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Latent Knowledge as a Predictor of Fact Acquisition in Fine-Tuned Large Language Models
2026-01-26 13:15:23Daniel B. Hier, Tayo Obafemi-AjayiAbstract
Large language models store biomedical facts with uneven strength after pretraining: some facts are present in the weights but are not reliably accessible under deterministic decoding (latent knowledge), while others are scarcely represented. We fine tuned Llama 3.1 8B Instruct to learn ontology term identifier mappings from the Human Phenotype Ontology (800 pairs) and the Gene Ontology (400 training pairs), withholding 400 GO pairs to test generalization. Treating learning as a time to event process across 20 epochs, we used stochastic decoding to detect latent knowledge at baseline and Cox proportional hazards models to identify predictors of acquisition, generalization, and degradation. Baseline deterministic recall for HPO was 2.8%, rising to 71.9% after fine-tuning. Latent knowledge was the strongest predictor of faster fact acquisition (HR 2.6) and was associated with earlier, higher peak learning rates and faster convergence; identifier frequency and curated annotation counts had smaller effects. Generalization to withheld GO facts was uncommon (5.8%) but more likely when latent knowledge was present. Previously correct GO mappings degraded more often for withheld (unseen) terms than for trained (seen) terms, suggesting a protective effect of reinforcement during training. These results show that latent knowledge predicts both the speed of factual learning during fine-tuning and the limited generalization of unseen ontology facts, while resistance to degradation depends on whether facts are reinforced.
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URL
https://arxiv.org/abs/2601.18468
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Substrate Stability Under Persistent Disagreement: Structural Constraints for Neutral Ontological Substrates
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Modern data systems increasingly operate under conditions of persistent legal, political, and analytic disagreement. In such settings, interoperability cannot rely on shared interpretation, negotiated semantics, or centralized authority. Instead, representations must function as neutral substrates that preserve stable reference across incompatible extensions. This paper investigates the structural constraints imposed on ontological design by this requirement. Building on a neutrality framework that treats interpretive non-commitment and stability under extension as explicit design constraints, we ask what minimal ontological structure is forced if accountability relationships are to remain referable and comparable under disagreement. Minimality here is not mere parsimony: a reduction is admissible only if it does not reintroduce stability-critical distinctions as hidden roles, flags, or contextual predicates. We establish a conditional lower-bound result: any ontology capable of supporting accountability under persistent disagreement must realize at least six distinct identity-and-persistence regimes. We further show that a construction with exactly six such regimes is sufficient to satisfy the stated requirements without embedding causal or normative commitments in the substrate. The result is not a proposal for a universal ontology, but a constraint on what is possible when neutrality and stable reference are treated as non-negotiable design goals.
Abstract (translated)
现代数据系统越来越多地在持续存在的法律、政治和分析分歧条件下运行。在这种环境下，互操作性不能依赖于共享解释、协商语义或中央权威。相反，表示必须作为中立的基础结构来保持稳定引用，在不兼容的扩展下依然有效。本文探讨了这种需求对本体设计施加的结构性限制。 基于一个将解释非承诺和在扩展下的稳定性视为显式设计约束的中立框架，我们研究了如果问责关系要在分歧条件下仍然可参照且可比较的话，最小化的本体结构必须是什么样的。这里的“最小化”并非简单的简约性：只有当这种简化的结果不重新引入影响稳定性的关键区别作为隐藏角色、标志或上下文谓词时才是可以接受的。 我们证明了一个条件下的下限结果：任何能够支持持续分歧条件下问责制的本体都必须实现至少六种不同的身份和持久性制度。此外，我们还表明，具有恰好六种这样的制度结构足以满足既定要求而不将因果或规范承诺嵌入基础架构中。这一结果不是对通用本体的一种提议，而是在将中立性和稳定引用视为不可谈判的设计目标时可能实现的限制条件。
URL
https://arxiv.org/abs/2601.16152
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To Neuro-Symbolic Classification and Beyond by Compiling Description Logic Ontologies to Probabilistic Circuits
2026-01-21 11:30:14Nicolas Lazzari, Valentina Presutti, Antonio VergariAbstract
Background: Neuro-symbolic methods enhance the reliability of neural network classifiers through logical constraints, but they lack native support for ontologies. Objectives: We aim to develop a neuro-symbolic method that reliably outputs predictions consistent with a Description Logic ontology that formalizes domain-specific knowledge. Methods: We encode a Description Logic ontology as a circuit, a feed-forward differentiable computational graph that supports tractable execution of queries and transformations. We show that the circuit can be used to (i) generate synthetic datasets that capture the semantics of the ontology; (ii) efficiently perform deductive reasoning on a GPU; (iii) implement neuro-symbolic models whose predictions are approximately or provably consistent with the knowledge defined in the ontology. Results We show that the synthetic dataset generated using the circuit qualitatively captures the semantics of the ontology while being challenging for Machine Learning classifiers, including neural networks. Moreover, we show that compiling the ontology into a circuit is a promising approach for scalable deductive reasoning, with runtimes up to three orders of magnitude faster than available reasoners. Finally, we show that our neuro-symbolic classifiers reliably produce consistent predictions when compared to neural network baselines, maintaining competitive performances or even outperforming them. Conclusions By compiling Description Logic ontologies into circuits, we obtain a tighter integration between the Deep Learning and Knowledge Representation fields. We show that a single circuit representation can be used to tackle different challenging tasks closely related to real-world applications.
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背景：神经符号方法通过逻辑约束增强神经网络分类器的可靠性，但它们缺乏对本体论的原生支持。 目标：我们的目标是开发一种神经符号方法，能够可靠地输出与形式化了特定领域知识的描述逻辑本体相一致的预测结果。 方法：我们将一个描述逻辑本体编码为电路——这是一种前馈可微计算图，支持查询和转换的可执行操作。我们证明该电路可用于（i）生成捕获本体语义的合成数据集；（ii）在GPU上高效地进行演绎推理；（iii）实现神经符号模型，其预测结果与本体中定义的知识大致一致或可以证明的一致。 结果：使用电路生成的合成数据集在定性上准确地捕捉到本体的语义，并且对机器学习分类器（包括神经网络）构成了挑战。此外，我们还展示了将本体编译为电路是进行可扩展演绎推理的一种有前景的方法，在运行时间方面比现有的推理器快了三个数量级。最后，我们将我们的神经符号分类器与神经网络基准进行了比较，证明它们可以可靠地生成一致的预测结果，并保持了竞争性甚至优于基准的表现。 结论：通过将描述逻辑本体编译为电路，我们实现了深度学习和知识表示领域之间的更紧密集成。我们展示了单一电路表示可用于解决多种与实际应用密切相关且具有挑战性的任务。
URL
https://arxiv.org/abs/2601.14894
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Ontology-aligned structuring and reuse of multimodal materials data and workflows towards automatic reproduction
2026-01-18 20:51:23Sepideh Baghaee Ravari, Abril Azocar Guzman, Sarath Menon, Stefan Sandfeld, Tilmann Hickel, Markus StrickerAbstract
Reproducibility of computational results remains a challenge in materials science, as simulation workflows and parameters are often reported only in unstructured text and tables. While literature data are valuable for validation and reuse, the lack of machine-readable workflow descriptions prevents large-scale curation and systematic comparison. Existing text-mining approaches are insufficient to extract complete computational workflows with their associated parameters. An ontology-driven, large language model (LLM)-assisted framework is introduced for the automated extraction and structuring of computational workflows from the literature. The approach focuses on density functional theory-based stacking fault energy (SFE) calculations in hexagonal close-packed magnesium and its binary alloys, and uses a multi-stage filtering strategy together with prompt-engineered LLM extraction applied to method sections and tables. Extracted information is unified into a canonical schema and aligned with established materials ontologies (CMSO, ASMO, and PLDO), enabling the construction of a knowledge graph using atomRDF. The resulting knowledge graph enables systematic comparison of reported SFE values and supports the structured reuse of computational protocols. While full computational reproducibility is still constrained by missing or implicit metadata, the framework provides a foundation for organizing and contextualizing published results in a semantically interoperable form, thereby improving transparency and reusability of computational materials data.
Abstract (translated)
计算结果的可重复性在材料科学中仍是一个挑战，因为模拟工作流程和参数通常仅以非结构化的文本和表格形式报告。尽管文献数据对于验证和重用非常重要，但缺乏机器可读的工作流描述阻碍了大规模的数据整理与系统的比较研究。现有的基于文本挖掘的方法不足以提取完整包含相关参数的计算工作流。 本文介绍了一种新的框架，该框架采用本体驱动、大型语言模型（LLM）辅助的方式，从文献中自动抽取和结构化计算工作流。该方法专注于以密度泛函理论为基础的堆积层错能（SFE）在六方紧密堆叠镁及其二元合金中的计算，并采用了多阶段过滤策略以及针对方法部分和表格进行了提示工程化的LLM提取技术。所提取的信息被统一为一个规范模式，并与现有材料本体论（CMSO、ASMO 和 PLDO）对齐，从而可以使用atomRDF构建知识图谱。 这种知识图谱允许系统性地比较已报告的SFE值，并支持计算协议的结构化重用。尽管完全重现计算结果仍然受限于缺失或隐含的元数据，该框架为组织和解释发表成果提供了一个语义互操作的基础形式，从而提高了计算材料数据的透明度和可重用性。
URL
https://arxiv.org/abs/2601.12582
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Construct, Align, and Reason: Large Ontology Models for Enterprise Knowledge Management
2026-01-18 05:30:03Yao Zhang, Hongyin ZhuAbstract
Enterprise-scale knowledge management faces significant challenges in integrating multi-source heterogeneous data and enabling effective semantic reasoning. Traditional knowledge graphs often struggle with implicit relationship discovery and lack sufficient semantic understanding for complex question answering. To address these limitations, we introduce a unified construct--align--reason framework, the large ontology model (LOM). We first build a dual-layer enterprise ontology from structured databases and unstructured text, subsequently fusing these sources into a comprehensive enterprise ontology. To enable instruction-aligned reasoning, we propose a unified three-stage training pipeline: ontology instruction fine-tuning to improve structural understanding; text-ontology grounding to strengthen node semantic encoding; and multi-task instruction tuning on ontology-language pairs with curriculum learning to enhance semantic reasoning and generation. We also construct comprehensive training and evaluation datasets covering diverse ontology reasoning tasks. On this benchmark, our 4B-parameter LOM achieves 89.47% accuracy and outperforms DeepSeek-V3.2 on complex graph reasoning, indicating effective fusion of ontology structure and language.
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URL
https://arxiv.org/abs/2602.00029
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Development of Ontological Knowledge Bases by Leveraging Large Language Models
2026-01-15 14:30:42Le Ngoc Luyen, Marie-H\'el\`ene Abel, Philippe GouspillouAbstract
Ontological Knowledge Bases (OKBs) play a vital role in structuring domain-specific knowledge and serve as a foundation for effective knowledge management systems. However, their traditional manual development poses significant challenges related to scalability, consistency, and adaptability. Recent advancements in Generative AI, particularly Large Language Models (LLMs), offer promising solutions for automating and enhancing OKB development. This paper introduces a structured, iterative methodology leveraging LLMs to optimize knowledge acquisition, automate ontology artifact generation, and enable continuous refinement cycles. We demonstrate this approach through a detailed case study focused on developing a user context profile ontology within the vehicle sales domain. Key contributions include significantly accelerated ontology construction processes, improved ontological consistency, effective bias mitigation, and enhanced transparency in the ontology engineering process. Our findings highlight the transformative potential of integrating LLMs into ontology development, notably improving scalability, integration capabilities, and overall efficiency in knowledge management systems.
Abstract (translated)
本体知识库（OKB）在构建特定领域的知识结构方面发挥着重要作用，并为有效的知识管理系统奠定了基础。然而，传统的手动开发方式面临着可扩展性、一致性和适应性的重大挑战。近期，在生成式人工智能领域，尤其是大型语言模型（LLM），为自动化和增强OKB的开发提供了有前景的解决方案。本文介绍了一种利用LLM进行优化的知识获取、自动本体文件生成以及持续改进周期的结构化迭代方法。通过详细案例研究车辆销售领域的用户上下文配置文件本体开发，我们展示了这种方法的应用。 关键贡献包括： - 显著加快了本体构建过程。 - 提高了本体的一致性。 - 有效地减轻偏差。 - 增强了本体工程过程中透明度。 我们的研究成果强调了将LLM集成到本体开发中的变革潜力，特别是在知识管理系统中提高了可扩展性、集成能力和总体效率。
URL
https://arxiv.org/abs/2601.10436
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Contrastive Bi-Encoder Models for Multi-Label Skill Extraction: Enhancing ESCO Ontology Matching with BERT and Attention Mechanisms
2026-01-14 03:43:45Yongming SunAbstract
Fine-grained labor market analysis increasingly relies on mapping unstructured job advertisements to standardized skill taxonomies such as ESCO. This mapping is naturally formulated as an Extreme Multi-Label Classification (XMLC) problem, but supervised solutions are constrained by the scarcity and cost of large-scale, taxonomy-aligned annotations--especially in non-English settings where job-ad language diverges substantially from formal skill definitions. We propose a zero-shot skill extraction framework that eliminates the need for manually labeled job-ad training data. The framework uses a Large Language Model (LLM) to synthesize training instances from ESCO definitions, and introduces hierarchically constrained multi-skill generation based on ESCO Level-2 categories to improve semantic coherence in multi-label contexts. On top of the synthetic corpus, we train a contrastive bi-encoder that aligns job-ad sentences with ESCO skill descriptions in a shared embedding space; the encoder augments a BERT backbone with BiLSTM and attention pooling to better model long, information-dense requirement statements. An upstream RoBERTa-based binary filter removes non-skill sentences to improve end-to-end precision. Experiments show that (i) hierarchy-conditioned generation improves both fluency and discriminability relative to unconstrained pairing, and (ii) the resulting multi-label model transfers effectively to real-world Chinese job advertisements, achieving strong zero-shot retrieval performance (F1@5 = 0.72) and outperforming TF--IDF and standard BERT baselines. Overall, the proposed pipeline provides a scalable, data-efficient pathway for automated skill coding in labor economics and workforce analytics.
Abstract (translated)
精细粒度的劳动力市场分析越来越依赖于将非结构化的职位广告映射到诸如ESCO（欧洲技能/能力、资格框架）等标准化的技能分类体系。这种映射自然可以被形式化为一个极多标签分类（XMLC）问题，但监督解决方案由于大规模、与分类体系对齐标注数据的稀缺和高昂成本而受到限制——特别是在非英语环境下，职位广告语言与正式技能定义之间存在显著差异的情况下更是如此。我们提出了一种零样本技能提取框架，该框架消除了手动标记职位广告训练数据的需求。该框架使用大型语言模型（LLM）从ESCO定义中合成训练实例，并引入基于ESCO第二级类别的分层约束多技能生成机制，以在多标签上下文中提高语义一致性。在此人工合成的语料库之上，我们训练了一个对比双编码器来将职位广告句子与ESCO技能描述对齐到共享嵌入空间；该编码器通过添加双向长短时记忆（BiLSTM）和注意力池化层增强BERT骨干模型，以更好地处理长且信息密集的要求陈述。一个上游的RoBERTa基二元过滤器用于去除非技能语句，从而提高整体精度。 实验表明： (i) 分层条件生成提高了相对于无约束配对而言的语言流畅性和可区分性； (ii) 所得到的多标签模型能够有效转移到真实世界中的中文职位广告上，并取得了强大的零样本检索性能（F1@5 = 0.72），并且优于TF-IDF和标准BERT基线。 总体来说，所提出的处理流程为劳动力经济学和工作力量分析领域的自动化技能编码提供了一条可扩展且数据高效的途径。
URL
https://arxiv.org/abs/2601.09119
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Spectral Generative Flow Models: A Physics-Inspired Replacement for Vectorized Large Language Models
2026-01-13 12:50:24Andrew KirulutaAbstract
We introduce Spectral Generative Flow Models (SGFMs), a physics-inspired alternative to transformer-based large language models. Instead of representing text or video as sequences of discrete tokens processed by attention, SGFMs treat generation as the evolution of a continuous field governed by constrained stochastic dynamics in a multiscale wavelet basis. This formulation replaces global attention with local operators, spectral projections, and Navier--Stokes-like transport, yielding a generative mechanism grounded in continuity, geometry, and physical structure. Our framework provides three key innovations: (i) a field-theoretic ontology in which text and video are unified as trajectories of a stochastic partial differential equation; (ii) a wavelet-domain representation that induces sparsity, scale separation, and computational efficiency; and (iii) a constrained stochastic flow that enforces stability, coherence, and uncertainty propagation. Together, these components define a generative architecture that departs fundamentally from autoregressive modeling and diffusion-based approaches. SGFMs offer a principled path toward long-range coherence, multimodal generality, and physically structured inductive bias in next-generation generative models.
Abstract (translated)
我们介绍了Spectral Generative Flow Models（SGFM），这是一种受物理启发的大语言模型的替代方案，不同于基于变压器的语言模型。SGFMs 不是将文本或视频表示为通过注意力处理的一系列离散标记，而是将其视为由多尺度小波基中约束随机动力学驱动的连续场的演变过程。这一形式化方法用局部操作符、谱投影和类似于Navier-Stokes传输的过程取代了全局注意力机制，从而构建了一种基于连续性、几何和物理结构的基础生成机制。 我们的框架提供了三个关键创新： (i) 一种将文本和视频统一为随机偏微分方程轨迹的场论本体； (ii) 引入稀疏性和计算效率的小波域表示方法，并实现了尺度分离； (iii) 施加了稳定性、连贯性和不确定性传播约束的受限随机流。 这些组件共同定义了一种根本不同于自回归模型和基于扩散的方法的生成架构。SGFMs 为下一代生成模型提供了一个原则性的路径，以实现长程一致性、多模态泛化以及物理结构化的归纳偏差。
URL
https://arxiv.org/abs/2601.08893
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Mathematical Knowledge Graph-Driven Framework for Equation-Based Predictive and Reliable Additive Manufacturing
2026-01-08 10:38:03Yeongbin Cha, Namjung KimAbstract
Additive manufacturing (AM) relies critically on understanding and extrapolating process-property relationships; however, existing data-driven approaches remain limited by fragmented knowledge representations and unreliable extrapolation under sparse data conditions. In this study, we propose an ontology-guided, equation-centric framework that tightly integrates large language models (LLMs) with an additive manufacturing mathematical knowledge graph (AM-MKG) to enable reliable knowledge extraction and principled extrapolative modeling. By explicitly encoding equations, variables, assumptions, and their semantic relationships within a formal ontology, unstructured literature is transformed into machine-interpretable representations that support structured querying and reasoning. LLM-based equation generation is further conditioned on MKG-derived subgraphs, enforcing physically meaningful functional forms and mitigating non-physical or unstable extrapolation trends. To assess reliability beyond conventional predictive uncertainty, a confidence-aware extrapolation assessment is introduced, integrating extrapolation distance, statistical stability, and knowledge-graph-based physical consistency into a unified confidence score. Results demonstrate that ontology-guided extraction significantly improves the structural coherence and quantitative reliability of extracted knowledge, while subgraph-conditioned equation generation yields stable and physically consistent extrapolations compared to unguided LLM outputs. Overall, this work establishes a unified pipeline for ontology-driven knowledge representation, equation-centered reasoning, and confidence-based extrapolation assessment, highlighting the potential of knowledge-graph-augmented LLMs as reliable tools for extrapolative modeling in additive manufacturing.
Abstract (translated)
增材制造（AM）严重依赖于对工艺-性能关系的理解和外推；然而，现有的数据驱动方法仍受限于知识表示的碎片化以及在稀疏数据条件下不可靠的外推。在这项研究中，我们提出了一种以本体论为导向、以方程为中心的框架，该框架紧密整合了大型语言模型（LLMs）与增材制造数学知识图谱（AM-MKG），以实现可靠的知识提取和原则性的外推建模。通过在一个正式的本体论内显式编码方程式、变量、假设及其语义关系，非结构化的文献被转化为支持结构化查询和推理的机器可解释表示形式。基于LLM的方程生成进一步受到从MKG导出的子图条件制约，从而强制执行物理上有意义的功能形式，并减少非物理或不稳定的外推趋势。 为了在传统预测不确定性之外评估可靠性，引入了一种基于置信度的外推评估方法，该方法将外推距离、统计稳定性以及基于知识图谱的物理一致性综合为统一的置信分数。结果表明，本体论引导的知识提取显著改善了所提取知识的结构一致性和定量可靠性，而子图条件制约下的方程生成与未指导的LLM输出相比，产生了稳定且物理上一致的外推。 总体而言，这项工作建立了一个统一的工作流程，用于基于本体论的知识表示、以方程式为中心的推理以及基于置信度的外推评估，突显了增强型知识图谱在增材制造领域作为可靠工具进行外推建模的巨大潜力。
URL
https://arxiv.org/abs/2601.05298
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The Ontological Neutrality Theorem: Why Neutral Ontological Substrates Must Be Pre-Causal and Pre-Normative
Abstract
Modern data systems must support accountability across persistent legal, political, and analytic disagreement. This requirement imposes strict constraints on the design of any ontology intended to function as a shared substrate. We establish an impossibility result for ontological neutrality: neutrality, understood as interpretive non-commitment and stability under incompatible extensions, is incompatible with the inclusion of causal or normative commitments at the foundational layer. Any ontology that asserts causal or deontic conclusions as ontological facts cannot serve as a neutral substrate across divergent frameworks without revision or contradiction. It follows that neutral ontological substrates must be pre-causal and pre-normative, representing entities, together with identity and persistence conditions, while externalizing interpretation, evaluation, and explanation. This paper does not propose a specific ontology or protocol; rather, it establishes the necessary design constraints for any system intended to maintain a shared, stable representation of reality across conflicting interpretive frameworks.
Abstract (translated)
现代数据系统必须在持久的法律、政治和分析分歧中支持问责制。这一要求对任何旨在作为共享基础的设计施加了严格的限制。我们建立了一个关于本体论中立性的不可能性结果：理解为解释上的非承诺性和不兼容扩展下的稳定性，这种中立性与包含因果或规范性承诺的基础层是相互冲突的。任何断言因果关系或义务结论作为本体事实的本体都无法在没有修改或矛盾的情况下，在不同框架之间充当中立基础。因此，中立本体论基础必须是前因果和前规范性的，表示实体以及同一性和持久性条件，并将解释、评估和说明外部化。本文并未提出具体的本体论或协议；相反，它为任何旨在在相互冲突的解读框架下维护共享稳定现实表示的设计约束建立了必要性。
URL
https://arxiv.org/abs/2601.14271
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Name That Part: 3D Part Segmentation and Naming
2026-01-07 18:59:42Soumava Paul, Prakhar Kaushik, Ankit Vaidya, Anand Bhattad, Alan YuilleAbstract
We address semantic 3D part segmentation: decomposing objects into parts with meaningful names. While datasets exist with part annotations, their definitions are inconsistent across datasets, limiting robust training. Previous methods produce unlabeled decompositions or retrieve single parts without complete shape annotations. We propose ALIGN-Parts, which formulates part naming as a direct set alignment task. Our method decomposes shapes into partlets - implicit 3D part representations - matched to part descriptions via bipartite assignment. We combine geometric cues from 3D part fields, appearance cues from multi-view vision features, and semantic knowledge from language-model-generated affordance descriptions. Text-alignment loss ensures partlets share embedding space with text, enabling a theoretically open-vocabulary matching setup, given sufficient data. Our efficient and novel, one-shot, 3D part segmentation and naming method finds applications in several downstream tasks, including serving as a scalable annotation engine. As our model supports zero-shot matching to arbitrary descriptions and confidence-calibrated predictions for known categories, with human verification, we create a unified ontology that aligns PartNet, 3DCoMPaT++, and Find3D, consisting of 1,794 unique 3D parts. We introduce two novel metrics appropriate for the named 3D part segmentation task. We also show examples from our newly created TexParts dataset.
Abstract (translated)
我们解决了语义三维部件分割问题：将物体分解为具有有意义名称的部件。尽管已有包含部件注释的数据集，但不同数据集中对部件定义不一致的问题限制了模型在这些定义上的稳健训练。以往的方法会产生未标记的分解或者从没有完整形状注释的情况下检索单个部件。我们提出了ALIGN-Parts方法，将部件命名作为直接集合对齐任务进行建模。该方法通过双部图分配的方式，将形状分解为“partlet”（隐式的三维部件表示）与部件描述相匹配。我们的方法结合了从3D部件字段获取的几何线索、多视角视觉特征中的外观线索以及来自语言模型生成的功能描述中的语义知识。文本对齐损失确保了这些部分表示与文本共享嵌入空间，从而在有足够的数据支持下，理论上可以实现开放词汇表的匹配设置。我们提出的方法是一种高效且新颖的一次性三维部件分割和命名方法，在包括作为可扩展注释引擎在内的多个下游任务中都有应用价值。由于我们的模型支持对任意描述进行零样本匹配，并能够为已知类别提供校准置信度的预测，结合人类验证后，我们可以创建一个统一的知识体系框架，将PartNet、3DCoMPaT++和Find3D中的1,794个独特的三维部件统一起来。我们还引入了两个适合命名三维部件分割任务的新指标，并展示了从新创建的TexParts数据集中选取的例子。 这段翻译详细介绍了所提出的方法（ALIGN-Parts）以及它如何解决现有问题，同时指出了该方法的应用价值和创新点，包括其在下游任务中的应用、零样本匹配能力和通过人类验证建立统一的知识体系框架。
URL
https://arxiv.org/abs/2512.18003
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Clinical Data Goes MEDS? Let's OWL make sense of it
2026-01-07 18:25:02Alberto Marfoglia, Jong Ho Jhee, Adrien CouletAbstract
The application of machine learning on healthcare data is often hindered by the lack of standardized and semantically explicit representation, leading to limited interoperability and reproducibility across datasets and experiments. The Medical Event Data Standard (MEDS) addresses these issues by introducing a minimal, event-centric data model designed for reproducible machine-learning workflows from health data. However, MEDS is defined as a data-format specification and does not natively provide integration with the Semantic Web ecosystem. In this article, we introduce MEDS-OWL, a lightweight OWL ontology that provides formal concepts and relations to enable representing MEDS datasets as RDF graphs. Additionally, we implemented meds2rdf, a Python conversion library that transforms MEDS events into RDF graphs, ensuring conformance with the ontology. We demonstrate the approach on a synthetic clinical dataset that describes patient care pathways for ruptured intracranial aneurysms and validate the resulting graph using SHACL constraints. The first release of MEDS-OWL comprises 13 classes, 10 object properties, 20 data properties, and 24 OWL axioms. Combined with meds2rdf, it enables data transformation into FAIR-aligned datasets, provenance-aware publishing, and interoperability of event-based clinical data. By bridging MEDS with the Semantic Web, this work contributes a reusable semantic layer for event-based clinical data and establishes a robust foundation for subsequent graph-based analytics.
Abstract (translated)
在医疗数据上应用机器学习往往因缺乏标准化和语义明确的数据表示而受阻，导致跨数据集和实验的互操作性和可重复性有限。《医学事件数据标准》（MEDS）通过引入一个最小化、以事件为中心的数据模型来解决这些问题，该模型旨在从健康数据中实现可重复的机器学习工作流程。然而，MEDS仅被定义为一种数据格式规范，并未原生提供与语义网生态系统的集成。本文介绍了MEDS-OWL，这是一种轻量级的OWL本体，提供了正式的概念和关系，以支持将MEDS数据集表示为RDF图。此外，我们实现了meds2rdf，这是一个Python转换库，能够将MEDS事件转换成符合该本体规范的RDF图。 我们在一个合成临床数据集上展示了这种方法的应用，该数据集描述了颅内动脉瘤破裂后的患者治疗路径，并通过SHACL约束验证了生成的图。MEDS-OWL的第一个版本包括13个类、10个对象属性、20个数据属性和24条OWL公理。 结合meds2rdf使用时，它能够将数据转换成与FAIR原则对齐的数据集，支持带有出处意识的数据发布，并促进基于事件的临床数据之间的互操作性。通过将MEDS与语义网连接起来，这项工作提供了一个可重用的基于事件的临床数据语义层，并为后续图基分析奠定了坚实的基础。
URL
https://arxiv.org/abs/2601.04164
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ChaosBench-Logic: A Benchmark for Logical and Symbolic Reasoning on Chaotic Dynamical Systems
2026-01-05 10:36:40Noel ThomasAbstract
Large language models (LLMs) excel at natural language tasks but remain brittle in domains requiring precise logical and symbolic reasoning. Chaotic dynamical systems provide an especially demanding test because chaos is deterministic yet often misinterpreted as randomness or complexity. We introduce ChaosBench-Logic, a benchmark that evaluates LLM reasoning across 30 diverse dynamical systems using a unified first-order logic (FOL) ontology. Each system is annotated with truth assignments for 11 semantic predicates, and 621 questions are generated across seven reasoning categories, including multi-hop implications, cross-system analogies, counterfactual reasoning, bias probes, and multi-turn dialogues. We define metrics for logical accuracy, implication consistency, dialogue coherence, and contradiction, and we release an open-source evaluation pipeline. Initial experiments show that frontier LLMs such as GPT-4, Claude 3.5 Sonnet, Gemini 2.5 Flash, and the open-source LLaMA-3 70B achieve 91-94% per-item accuracy, yet still score 0% on compositional items and exhibit fragile global coherence. Dialogue-level accuracy ranges from 53.1% (GPT-4 CoT) to 75.5% (LLaMA-3 zero-shot). ChaosBench-Logic provides a rigorous testbed for diagnosing such failures and a foundation for developing neuro-symbolic approaches that improve scientific reasoning in LLMs.
Abstract (translated)
大型语言模型（LLMs）在处理自然语言任务方面表现出色，但在需要精确逻辑和符号推理的领域中仍显得脆弱。混沌动力系统提供了一个特别具有挑战性的测试环境，因为尽管混沌是确定性的，但常常被误认为是随机性或复杂性。我们引入了ChaosBench-Logic，这是一个基准测试工具，它使用统一的一阶逻辑（FOL）本体论，在30个不同的动态系统上评估LLM的推理能力，并涵盖了11种语义谓词的真实赋值以及七个推理类别的621个问题生成。这些推理类别包括多步推断、跨系统的类比推理、反事实推理、偏差探测和多轮对话。 我们定义了逻辑准确性、推论一致性、对话连贯性和矛盾的度量标准，并发布了开源评估管道。初步实验显示，前沿LLM如GPT-4、Claude 3.5 Sonnet、Gemini 2.5 Flash以及开源LLaMA-3 70B在单项准确率上达到了91%-94%，但在组合性项目中得分为0，并且整体连贯性表现脆弱。对话级别的准确性范围从GPT-4 CoT的53.1%到LLaMA-3零样本模型的75.5%。 ChaosBench-Logic为诊断此类失败提供了一个严格的测试平台，同时也为开发能够改善大型语言模型中科学推理的神经符号方法奠定了基础。
URL
https://arxiv.org/abs/2601.01982
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Clinical Knowledge Graph Construction and Evaluation with Multi-LLMs via Retrieval-Augmented Generation
2026-01-05 07:16:29Udiptaman Das, Krishnasai B. Atmakuri, Duy Ho, Chi Lee, Yugyung LeeAbstract
Large language models (LLMs) offer new opportunities for constructing knowledge graphs (KGs) from unstructured clinical narratives. However, existing approaches often rely on structured inputs and lack robust validation of factual accuracy and semantic consistency, limitations that are especially problematic in oncology. We introduce an end-to-end framework for clinical KG construction and evaluation directly from free text using multi-agent prompting and a schema-constrained Retrieval-Augmented Generation (KG-RAG) strategy. Our pipeline integrates (1) prompt-driven entity, attribute, and relation extraction; (2) entropy-based uncertainty scoring; (3) ontology-aligned RDF/OWL schema generation; and (4) multi-LLM consensus validation for hallucination detection and semantic refinement. Beyond static graph construction, the framework supports continuous refinement and self-supervised evaluation, enabling iterative improvement of graph quality. Applied to two oncology cohorts (PDAC and BRCA), our method produces interpretable, SPARQL-compatible, and clinically grounded knowledge graphs without relying on gold-standard annotations. Experimental results demonstrate consistent gains in precision, relevance, and ontology compliance over baseline methods.
Abstract (translated)
大型语言模型（LLM）为从非结构化临床叙述中构建知识图谱（KG）提供了新的机会。然而，现有的方法通常依赖于结构化的输入，并且缺乏对事实准确性和语义一致性进行稳健验证的能力，在肿瘤学领域尤其成问题。我们引入了一种端到端框架，该框架可以直接从自由文本使用多代理提示和约束模式的检索增强生成（KG-RAG）策略构建并评估临床知识图谱。我们的流水线整合了以下四个关键步骤：(1) 提示驱动下的实体、属性和关系抽取；(2) 基于熵的不确定性评分；(3) 与本体一致的RDF/OWL模式生成；以及 (4) 多LLM共识验证以检测幻觉并进行语义细化。除了静态图谱构建外，该框架还支持持续改进和自我监督评估，从而能够不断优化图的质量。在两个肿瘤学队列（胰腺导管腺癌PDAC 和 BRCA）的应用中，我们的方法无需依赖金标准注释，即可生成可解释、SPARQL兼容且具有临床依据的知识图谱。实验结果表明，在精确度、相关性和符合本体规范方面，相较于基线方法，该方法始终表现出显著的优势。
URL
https://arxiv.org/abs/2601.01844
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Structured Decomposition for LLM Reasoning: Cross-Domain Validation and Semantic Web Integration
2026-01-04 17:19:20Albert Sadowski, Jaros{\l}aw A. ChudziakAbstract
Rule-based reasoning over natural language input arises in domains where decisions must be auditable and justifiable: clinical protocols specify eligibility criteria in prose, evidence rules define admissibility through textual conditions, and scientific standards dictate methodological requirements. Applying rules to such inputs demands both interpretive flexibility and formal guarantees. Large language models (LLMs) provide flexibility but cannot ensure consistent rule application; symbolic systems provide guarantees but require structured input. This paper presents an integration pattern that combines these strengths: LLMs serve as ontology population engines, translating unstructured text into ABox assertions according to expert-authored TBox specifications, while SWRL-based reasoners apply rules with deterministic guarantees. The framework decomposes reasoning into entity identification, assertion extraction, and symbolic verification, with task definitions grounded in OWL 2 ontologies. Experiments across three domains (legal hearsay determination, scientific method-task application, clinical trial eligibility) and eleven language models validate the approach. Structured decomposition achieves statistically significant improvements over few-shot prompting in aggregate, with gains observed across all three domains. An ablation study confirms that symbolic verification provides substantial benefit beyond structured prompting alone. The populated ABox integrates with standard semantic web tooling for inspection and querying, positioning the framework for richer inference patterns that simpler formalisms cannot express.
Abstract (translated)
基于规则的推理在自然语言输入中出现于需要可审计和合理化的决策领域：临床协议通过散文形式规定资格标准，证据规则通过文本条件定义可接受性，科学标准则规定方法要求。将规则应用于此类输入既需要解释灵活性又需提供正式保证。大型语言模型（LLMs）提供了这种灵活性但不能确保一致的规则应用；而符号系统则能够提供这些保证，但需要结构化输入。本文提出了一种整合模式，结合了这两种系统的优点：通过专家编写的TBox规范，将非结构化的文本转化为ABox断言，大型语言模型充当本体论填充引擎，同时基于SWRL的推理器应用具有确定性保证的规则。框架将推理分解为实体识别、断言提取和符号验证三个部分，并根据OWL 2本体定义任务。 该方法在三个领域（法律传闻决定、科学方法-任务应用以及临床试验资格）中的十一种语言模型实验中得到了验证，结构化分解的整体效果显著优于少量提示的引导方式，在这三个领域均观察到了改进。消融研究确认了符号验证在单纯结构化提示之外提供了重大益处。填充后的ABox能够与标准语义网工具集成用于检查和查询，使该框架具备更丰富的推理模式表达能力，而这些简单的形式主义无法实现。 简而言之，这种结合大型语言模型的灵活性和基于规则系统的正式保证的方法，在多个实际应用领域内展示出了优越性，并且其结构化的处理方式还为后续进一步的应用开发提供了良好的基础。
URL
https://arxiv.org/abs/2601.01609
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Semantic Alignment of Multilingual Knowledge Graphs via Contextualized Vector Projections
2025-12-22 11:02:30Abhishek KumarAbstract
The paper presents our work on cross-lingual ontology alignment system which uses embedding based cosine similarity matching. The ontology entities are made contextually richer by creating descriptions using novel techniques. We use a fine-tuned transformer based multilingual model for generating better embeddings. We use cosine similarity to find positive ontology entities pairs and then apply threshold filtering to retain only highly similar entities. We have evaluated our work on OAEI-2022 multifarm track. We achieve 71% F1 score (78% recall and 65% precision) on the evaluation dataset, 16% increase from best baseline score. This suggests that our proposed alignment pipeline is able to capture the subtle cross-lingual similarities.
Abstract (translated)
本文介绍了我们关于跨语言本体对齐系统的工作，该系统使用基于嵌入的余弦相似度匹配。为了使本体实体在语境上更加丰富，我们采用了创新技术生成描述，并利用经过微调的多语言转换器模型来生成更好的嵌入表示。通过计算余弦相似度来寻找正向的本体实体对，并随后应用阈值过滤以保留高度相似的实体。我们在OAEI-2022 multifarm轨道上评估了我们的工作，取得了71%的F1分数（召回率为78%，精确率为65%），相比最佳基线得分提高了16%。这表明我们提出的对齐流水线能够捕捉到微妙的跨语言相似性。
URL
https://arxiv.org/abs/2601.00814
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$gamma$ `Attention' in Cognitive Agents: Ontology-Free Knowledge Representations With Promise Theoretic Semantics
2025-12-22 06:48:53Mark BurgessAbstract
The semantics and dynamics of `attention' are closely related to promise theoretic notions developed for autonomous agents and can thus easily be written down in promise framework. In this way one may establish a bridge between vectorized Machine Learning and Knowledge Graph representations without relying on language models implicitly. Our expectations for knowledge presume a degree of statistical stability, i.e. average invariance under repeated observation, or `trust' in the data. Both learning networks and knowledge graph representations can meaningfully coexist to preserve different aspects of data. While vectorized data are useful for probabilistic estimation, graphs preserve the intentionality of the source even under data fractionation. Using a Semantic Spacetime $\gamma(3,4)$ graph, one avoids complex ontologies in favour of classification of features by their roles in semantic processes. The latter favours an approach to reasoning under conditions of uncertainty. Appropriate attention to causal boundary conditions may lead to orders of magnitude compression of data required for such context determination, as required in the contexts of autonomous robotics, defence deployments, and ad hoc emergency services.
Abstract (translated)
`注意力'的语义和动态与为自主代理开发的承诺理论概念密切相关，因此可以很容易地在承诺框架中描述。通过这种方式，人们可以在不依赖语言模型的情况下，在向量化的机器学习表示和知识图表示之间建立一座桥梁。我们对知识的期望假设数据具有一定程度的统计稳定性，即重复观察下的平均不变性或“信任”。无论是学习网络还是知识图表示都能有意义地共存以保存数据的不同方面：虽然向量化数据对于概率估计很有用，但在数据分割的情况下，图形仍然可以保留源的意图。使用语义时空 $\gamma(3,4)$ 图，人们可以通过按其在语义过程中的角色分类特征来避免复杂的本体论，这有利于不确定条件下的推理方法。适当关注因果边界条件可能会导致大量数据压缩，以确定此类上下文所需的数据量，这对于自主机器人、防御部署和临时紧急服务等应用至关重要。
URL
https://arxiv.org/abs/2512.19084
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