Ontology
-
The Semantic Ladder: A Framework for Progressive Formalization of Natural Language Content for Knowledge Graphs and AI Systems
2026-03-23 15:59:59Lars VogtAbstract
Semantic data and knowledge infrastructures must reconcile two fundamentally different forms of representation: natural language, in which most knowledge is created and communicated, and formal semantic models, which enable machine-actionable integration, interoperability, and reasoning. Bridging this gap remains a central challenge, particularly when full semantic formalization is required at the point of data entry. Here, we introduce the Semantic Ladder, an architectural framework that enables the progressive formalization of data and knowledge. Building on the concept of modular semantic units as identifiable carriers of meaning, the framework organizes representations across levels of increasing semantic explicitness, ranging from natural language text snippets to ontology-based and higher-order logical models. Transformations between levels support semantic enrichment, statement structuring, and logical modelling while preserving semantic continuity and traceability. This approach enables the incremental construction of semantic knowledge spaces, reduces the semantic parsing burden, and supports the integration of heterogeneous representations, including natural language, structured semantic models, and vector-based embeddings. The Semantic Ladder thereby provides a foundation for scalable, interoperable, and AI-ready data and knowledge infrastructures.
Abstract (translated)
URL
https://arxiv.org/abs/2603.22136
PDF
-
gUFO: A Gentle Foundational Ontology for Semantic Web Knowledge Graphs
2026-03-21 21:03:02Jo\~ao Paulo A. Almeida, Giancarlo Guizzardi, Tiago Prince Sales, Claudenir M. FonsecaAbstract
gUFO is a lightweight implementation of the Unified Foundational Ontology (UFO) suitable for Semantic Web OWL 2 DL applications. UFO is a mature foundational ontology with a rich axiomatization and that has been employed in a significant number of projects in research and industry. Moreover, it is currently in the process of standardization by the International Organization for Standardization as the ISO/IEC CD 21838-5. gUFO stands out from other foundational ontology implementations (such as those provided for BFO and DOLCE) given its unique support for a typology of types (operationalizing OntoClean guidelines), its reification patterns for intrinsic and relational aspects, and its support for situations and high-order types. gUFO provides well-founded patterns to address recurrent problems in Semantic Web knowledge graphs. In this paper, we present gUFO with its constituting categories, relations and constraints, discuss how it differs from the original UFO reference ontology, elaborate on its community adoption, and systematically position it in relation to existing OWL-based implementations of popular alternative foundational ontologies.
Abstract (translated)
gUFO是统一基础本体论（UFO）的轻量级实现，适用于语义网OWL 2 DL应用。UFO是一个成熟的基础本体论，具有丰富的公理化体系，已在众多研究和工业项目中得到应用。此外，它目前正由国际标准化组织推进标准化进程，作为ISO/IEC CD 21838-5标准。相较于其他基础本体论实现（如为BFO和DOLCE提供的实现），gUFO的独特之处在于：其对类型分类体系的独特支持（将OntoClean指南操作化）、对内在与关系方面的具体化模式，以及对情境和高阶类型的支持。gUFO提供了基于良好理论基础的解决方案，以应对语义网知识图谱中反复出现的问题。本文介绍了gUFO的构成类别、关系与约束，讨论了其与原始UFO参考本体论的差异，阐述了其社区采用情况，并系统性地将其与现有基于OWL的流行替代基础本体论实现进行了对比定位。
URL
https://arxiv.org/abs/2603.20948
PDF
-
Semantic Sections: An Atlas-Native Feature Ontology for Obstructed Representation Spaces
Abstract
Recent interpretability work often treats a feature as a single global direction, dictionary atom, or latent coordinate shared across contexts. We argue that this ontology can fail in obstructed representation spaces, where locally coherent meanings need not assemble into one globally consistent feature. We introduce an atlas-native replacement object, the semantic section: a transport-compatible family of local feature representatives defined over a context atlas. We formalize semantic sections, prove that tree-supported propagation is always pathwise realizable, and show that cycle consistency is the key criterion for genuine globalization. This yields a distinction between tree-local, globalizable, and twisted sections, with twisted sections capturing locally coherent but holonomy-obstructed meanings. We then develop a discovery-and-certification pipeline based on seeded propagation, synchronization across overlaps, defect-based pruning, cycle-aware taxonomy, and deduplication. Across layer-16 atlases for Llama 3.2 3B Instruct, Qwen 2.5 3B Instruct, and Gemma 2 2B IT, we find nontrivial populations of semantic sections, including cycle-supported globalizable and twisted regimes after deduplication. Most importantly, semantic identity is not recovered by raw global-vector similarity. Even certified globalizable sections show low cross-chart signed cosine similarity, and raw similarity baselines recover only a small fraction of true within-section pairs, often collapsing at moderate thresholds. By contrast, section-based identity recovery is perfect on certified supports. These results support semantic sections as a better feature ontology in obstructed regimes.
Abstract (translated)
近期可解释性研究常将特征视为跨上下文的单一全局方向、字典原子或潜在坐标。我们认为这种本体论在阻塞表示空间中可能失效——局部一致的含义未必能整合为全局一致的特征。我们提出一种图集原生的替代对象：语义截面，即定义在上下文图集上的传输兼容的局部特征代表族。我们形式化语义截面，证明树支撑传播始终可路径实现，并表明循环一致性是真正全局化的关键判据。这区分出树局部、可全局化与扭曲截面三类，其中扭曲截面捕获局部一致但受全息阻塞的含义。随后我们开发了基于种子传播、重叠区同步、基于缺陷剪枝、循环感知分类与去重的发现认证流程。在Llama 3.2 3B Instruct、Qwen 2.5 3B Instruct与Gemma 2 2B IT的第16层图集上，我们发现非平凡的语义截面群体，包括去重后存在循环支撑的可全局化与扭曲态。最重要的是，语义同一性无法通过原始全局向量相似度恢复：即使认证的可全局化截面也呈现跨图表低带符号余弦相似度，原始相似度基线仅恢复小部分真实截面内配对，且在中等阈值时常崩溃。相比之下，基于截面的同一性恢复在认证支持集上完全准确。这些结果支持语义截面作为阻塞 regimes 中更优的特征本体论。
URL
https://arxiv.org/abs/2603.20867
PDF
-
Towards Intelligent Geospatial Data Discovery: a knowledge graph-driven multi-agent framework powered by large language models
2026-03-21 06:16:56Ruixiang Liu, Zhenlong Li, Ali Khosravi KazaziAbstract
The rapid growth in the volume, variety, and velocity of geospatial data has created data ecosystems that are highly distributed, heterogeneous, and semantically inconsistent. Existing data catalogs, portals, and infrastructures still rely largely on keyword-based search with limited semantic support, which often fails to capture user intent and leads to weak retrieval performance. To address these challenges, this study proposes a knowledge graph-driven multi-agent framework for intelligent geospatial data discovery, powered by large language models. The framework introduces a unified geospatial metadata ontology as a semantic mediation layer to align heterogeneous metadata standards across platforms and constructs a geospatial metadata knowledge graph to explicitly model datasets and their multidimensional relationships. Building on the structured representation, the framework adopts a multi-agent collaborative architecture to perform intent parsing, knowledge graph retrieval, and answer synthesis, forming an interpretable and closed-loop discovery process from user queries to results. Results from representative use cases and performance evaluation show that the framework substantially improves intent matching accuracy, ranking quality, recall, and discovery transparency compared with traditional systems. This study advances geospatial data discovery toward a more semantic, intent-aware, and intelligent paradigm, providing a practical foundation for next-generation intelligent and autonomous spatial data infrastructures and contributing to the broader vision of Autonomous GIS.
Abstract (translated)
地理空间数据在体量、种类和速度上的快速增长，催生了高度分布式、异构且语义不一致的数据生态系统。现有数据目录、门户和基础设施仍主要依赖基于关键词的搜索，语义支持有限，往往难以捕捉用户意图，导致检索性能薄弱。为应对这些挑战，本研究提出一种由知识图谱驱动、基于大语言模型的多智能体智能地理空间数据发现框架。该框架引入统一的地理空间元数据本体作为语义中介层，以对齐跨平台的异构元数据标准，并构建地理空间元数据知识图谱，显式建模数据集及其多维关系。基于这一结构化表示，框架采用多智能体协同架构执行意图解析、知识图谱检索与答案合成，形成从用户查询到结果的可解释闭环发现流程。代表性用例与性能评估结果表明，相较于传统系统，该框架在意图匹配准确率、排序质量、召回率及发现透明度方面均有显著提升。本研究推动地理空间数据发现迈向更语义化、意图感知和智能化的范式，为下一代智能自主空间数据基础设施提供了实践基础，并贡献于自主GIS的宏观愿景。
URL
https://arxiv.org/abs/2603.20670
PDF
-
Where can AI be used? Insights from a deep ontology of work activities
2026-03-21 03:28:39Alice Cai, Iman YeckehZaare, Shuo Sun, Vasiliki Charisi, Xinru Wang, Aiman Imran, Robert Laubacher, Alok Prakash, Thomas W. MaloneAbstract
Artificial intelligence (AI) is poised to profoundly reshape how work is executed and organized, but we do not yet have deep frameworks for understanding where AI can be used. Here we provide a comprehensive ontology of work activities that can help systematically analyze and predict uses of AI. To do this, we disaggregate and then substantially reorganize the approximately 20K activities in the US Department of Labor's widely used O*NET occupational database. Next, we use this framework to classify descriptions of 13,275 AI software applications and a worldwide tally of 20.8 million robotic systems. Finally, we use the data about both these kinds of AI to generate graphical displays of how the estimated units and market values of all worldwide AI systems used today are distributed across the work activities that these systems help perform. We find a highly uneven distribution of AI market value across activities, with the top 1.6% of activities accounting for over 60% of AI market value. Most of the market value is used in information-based activities (72%), especially creating information (36%), and only 12% is used in physical activities. Interactive activities include both information-based and physical activities and account for 48% of AI market value, much of which (26%) involves transferring information. These results can be viewed as rough predictions of the AI applicability for all the different work activities down to very low levels of detail. Thus, we believe this systematic framework can help predict at a detailed level where today's AI systems can and cannot be used and how future AI capabilities may change this.
Abstract (translated)
人工智能（AI）有望深刻改变工作的执行与组织方式，但我们目前仍缺乏深入框架来理解AI的应用场景。本文提供了一个全面的工作活动本体论，可帮助系统性地分析和预测AI的使用。为此，我们解构并重组了美国劳工部广泛使用的O*NET职业数据库中约2万项活动。接着，我们利用该框架对13,275项AI软件应用及全球2,080万台机器人系统进行了分类。最后，我们基于这两类AI的数据，生成了图形化展示，呈现当今全球所有AI系统预估数量与市场价值如何分布于其辅助完成的各类工作活动中。我们发现AI市场价值在活动间分布极不均衡：前1.6%的活动占据了超过60%的市场价值。大部分市场价值（72%）集中于基于信息的活动，尤其是信息创造（36%），而仅12%用于体力活动。交互式活动涵盖基于信息和体力两类活动，占AI市场价值的48%，其中26%涉及信息传递。这些结果可视为对所有不同工作活动AI适用性的粗略预测，甚至细化到极低层级。因此，我们认为这一系统性框架有助于在详细层面预测当今AI系统能否应用于特定工作，以及未来AI能力可能如何改变这一格局。
URL
https://arxiv.org/abs/2603.20619
PDF
-
Can Large Multimodal Models Inspect Buildings? A Hierarchical Benchmark for Structural Pathology Reasoning
2026-03-20 17:24:46Hui Zhong, Yichun Gao, Luyan Liu, Hai Yang, Wang Wang, Haowei Zhang, Xinhu ZhengAbstract
Automated building facade inspection is a critical component of urban resilience and smart city maintenance. Traditionally, this field has relied on specialized discriminative models (e.g., YOLO, Mask R-CNN) that excel at pixel-level localization but are constrained to passive perception and worse generization without the visual understandng to interpret structural topology. Large Multimodal Models (LMMs) promise a paradigm shift toward active reasoning, yet their application in such high-stakes engineering domains lacks rigorous evaluation standards. To bridge this gap, we introduce a human-in-the-loop semi-automated annotation framework, leveraging expert-proposal verification to unify 12 fragmented datasets into a standardized, hierarchical ontology. Building on this foundation, we present \textit{DefectBench}, the first multi-dimensional benchmark designed to interrogate LMMs beyond basic semantic recognition. \textit{DefectBench} evaluates 18 state-of-the-art (SOTA) LMMs across three escalating cognitive dimensions: Semantic Perception, Spatial Localization, and Generative Geometry Segmentation. Extensive experiments reveal that while current LMMs demonstrate exceptional topological awareness and semantic understanding (effectively diagnosing "what" and "how"), they exhibit significant deficiencies in metric localization precision ("where"). Crucially, however, we validate the viability of zero-shot generative segmentation, showing that general-purpose foundation models can rival specialized supervised networks without domain-specific training. This work provides both a rigorous benchmarking standard and a high-quality open-source database, establishing a new baseline for the advancement of autonomous AI agents in civil engineering.
Abstract (translated)
自动化建筑外立面检测是城市韧性与智慧城市维护的关键环节。传统方法依赖专用判别模型（如YOLO、Mask R-CNN），虽擅长像素级定位，却受限于被动感知能力，且因缺乏对结构拓扑的视觉理解而泛化能力不足。大型多模态模型（LMMs）有望推动向主动推理的范式转变，但其在高风险工程领域的应用尚缺乏严谨的评估标准。为填补这一空白，我们提出了人机协同的半自动标注框架，通过专家提案验证机制，将12个分散数据集整合为标准化分层本体。基于此基础，我们发布首个多维基准测试\textit{DefectBench}，旨在超越基础语义识别，深入检验LMMs的认知能力。该基准从三个递进认知维度评估18个前沿LMMs：语义感知、空间定位与几何生成分割。大量实验表明，当前LMMs虽展现出卓越的拓扑意识与语义理解能力（能有效诊断"何类缺陷"与"如何形成"），却在度量级定位精度（"何处缺陷"）方面存在显著不足。尤为关键的是，我们验证了零样本几何生成分割的可行性——证明通用基础模型无需领域特定训练即可媲美专业监督网络。本研究既提供了严谨的基准测试标准与高质量开源数据库，也为自主AI智能体在土木工程领域的进步确立了新基线。
URL
https://arxiv.org/abs/2603.20148
PDF
-
LLM-Enhanced Semantic Data Integration of Electronic Component Qualifications in the Aerospace Domain
2026-03-20 16:22:06Antonio De Santis, Marco Balduini, Matteo Belcao, Andrea Proia, Marco Brambilla, Emanuele Della ValleAbstract
Large manufacturing companies face challenges in information retrieval due to data silos maintained by different departments, leading to inconsistencies and misalignment across databases. This paper presents an experience in integrating and retrieving qualification data for electronic components used in satellite board design. Due to data silos, designers cannot immediately determine the qualification status of individual components. However, this process is critical during the planning phase, when assembly drawings are issued before production, to optimize new qualifications and avoid redundant efforts. To address this, we propose a pipeline that uses Virtual Knowledge Graphs for a unified view over heterogeneous data sources and LLMs to enhance retrieval and reduce manual effort in data cleansing. The retrieval of qualifications is then performed through an Ontology-based Data Access approach for structured queries and a vector search mechanism for retrieving qualifications based on similar textual properties. We perform a comparative cost-benefit analysis, demonstrating that the proposed pipeline also outperforms approaches relying solely on LLMs, such as Retrieval-Augmented Generation (RAG), in terms of long-term efficiency.
Abstract (translated)
大型制造公司因各部门维护的数据孤岛而在信息检索方面面临挑战，这导致跨数据库的数据不一致与错位。本文介绍了在整合与检索卫星板设计所用电子元件资质数据方面的一次实践。由于数据孤岛，设计人员无法立即确定单个元件的资质状态。然而，在规划阶段——即生产前下发装配图纸时——这一流程至关重要，旨在优化新资质认证并避免重复工作。为此，我们提出一种利用虚拟知识图谱对异构数据源提供统一视图、并借助大语言模型增强检索与减少数据清洗人工成本的流程。资质检索随后通过基于本体的数据访问方法实现结构化查询，并通过向量搜索机制基于相似的文本属性检索资质。我们进行了对比成本效益分析，证明所提出的流程在长期效率方面也优于仅依赖大语言模型的方法（如检索增强生成）。
URL
https://arxiv.org/abs/2603.20094
PDF
-
Ontology-Guided Diffusion for Zero-Shot Visual Sim2Real Transfer
2026-03-19 10:16:15Mohamed Youssef, Mayar Elfares, Anna-Maria Meer, Matteo Bortoletto, Andreas BullingAbstract
Bridging the simulation-to-reality (sim2real) gap remains challenging as labelled real-world data is scarce. Existing diffusion-based approaches rely on unstructured prompts or statistical alignment, which do not capture the structured factors that make images look real. We introduce Ontology- Guided Diffusion (OGD), a neuro-symbolic zero-shot sim2real image translation framework that represents realism as structured knowledge. OGD decomposes realism into an ontology of interpretable traits -- such as lighting and material properties -- and encodes their relationships in a knowledge graph. From a synthetic image, OGD infers trait activations and uses a graph neural network to produce a global embedding. In parallel, a symbolic planner uses the ontology traits to compute a consistent sequence of visual edits needed to narrow the realism gap. The graph embedding conditions a pretrained instruction-guided diffusion model via cross-attention, while the planned edits are converted into a structured instruction prompt. Across benchmarks, our graph-based embeddings better distinguish real from synthetic imagery than baselines, and OGD outperforms state-of-the-art diffusion methods in sim2real image translations. Overall, OGD shows that explicitly encoding realism structure enables interpretable, data-efficient, and generalisable zero-shot sim2real transfer.
Abstract (translated)
URL
https://arxiv.org/abs/2603.18719
PDF
-
An Onto-Relational-Sophic Framework for Governing Synthetic Minds
2026-03-19 08:58:10Huansheng Ning, Jianguo DingAbstract
The rapid evolution of artificial intelligence, from task-specific systems to foundation models exhibiting broad, flexible competence across reasoning, creative synthesis, and social interaction, has outpaced the conceptual and governance frameworks designed to manage it. Current regulatory paradigms, anchored in a tool-centric worldview, address algorithmic bias and transparency but leave unanswered foundational questions about what increasingly capable synthetic minds are, how societies should relate to them, and the normative principles that should guide their development. Here we introduce the Onto-Relational-Sophic (ORS) framework, grounded in Cyberism philosophy, which offers integrated answers to these challenges through three pillars: (1) a Cyber-Physical-Social-Thinking (CPST) ontology that defines the mode of being for synthetic minds as irreducibly multi-dimensional rather than purely computational; (2) a graded spectrum of digital personhood providing a pragmatic relational taxonomy beyond binary person-or-tool classifications; and (3) Cybersophy, a wisdom-oriented axiology synthesizing virtue ethics, consequentialism, and relational approaches to guide governance. We apply the framework to emergent scenarios including autonomous research agents, AI-mediated healthcare, and agentic AI ecosystems, demonstrating its capacity to generate proportionate, adaptive governance recommendations. The ORS framework charts a path from narrow technical alignment toward comprehensive philosophical foundations for the synthetic minds already among us.
Abstract (translated)
URL
https://arxiv.org/abs/2603.18633
PDF
-
CTG-DB: An Ontology-Based Transformation of ClinicalTrials.gov to Enable Cross-Trial Drug Safety Analyses
2026-03-16 21:37:09Jeffery L. Painter, Fran\c{c}ois Haguinet, Andrew BateAbstract
this http URL (this http URL) is the largest publicly accessible registry of clinical studies, yet its registry-oriented architecture and heterogeneous adverse event (AE) terminology limit systematic pharmacovigilance (PV) analytics. AEs are typically recorded as investigator-reported text rather than standardized identifiers, requiring manual reconciliation to identify coherent safety concepts. We present the this http URL Transformation Database (CTG-DB), an open-source pipeline that ingests the complete this http URL XML archive and produces a relational database aligned to standardized AE terminology using the Medical Dictionary for Regulatory Activities (MedDRA). CTG-DB preserves arm-level denominators, represents placebo and comparator arms, and normalizes AE terminology using deterministic exact and fuzzy matching to ensure transparent and reproducible mappings. This framework enables concept-level retrieval and cross-trial aggregation for scalable placebo-referenced safety analyses and integration of clinical trial evidence into downstream PV signal detection.
Abstract (translated)
URL
https://arxiv.org/abs/2603.15936
PDF
-
Open Biomedical Knowledge Graphs at Scale: Construction, Federation, and AI Agent Access with Samyama Graph Database
2026-03-16 10:36:13Madhulatha Mandarapu, Sandeep KunkunuruAbstract
Biomedical knowledge is fragmented across siloed databases -- Reactome for pathways, STRING for protein interactions, Gene Ontology for functional annotations, this http URL for study registries, and dozens more. Researchers routinely download flat files from each source and write bespoke scripts to cross-reference them, a process that is slow, error-prone, and not reproducible. We present two open-source biomedical knowledge graphs -- Pathways KG (118,686 nodes, 834,785 edges from 5 sources) and Clinical Trials KG (7,774,446 nodes, 26,973,997 edges from 5 sources) -- built on Samyama, a high-performance graph database written in Rust. Our contributions are threefold. First, we describe a reproducible ETL pattern for constructing large-scale KGs from heterogeneous public data sources, with cross-source deduplication, batch Cypher loading, and portable snapshot export. Second, we demonstrate cross-KG federation: loading both snapshots into a single graph tenant enables property-based joins across datasets, answering questions like ``Which biological pathways are disrupted by drugs currently in Phase~3 trials for breast cancer?'' -- a query that neither KG can answer alone. Third, we introduce schema-driven MCP server generation: each KG automatically exposes typed tools for LLM agents via the Model Context Protocol, enabling natural-language access to graph queries without manual tool authoring. All data sources are open-license (CC~BY~4.0, CC0, OBO). Snapshots, ETL code, and MCP configurations are publicly available. The combined federated graph (7.89M nodes, 27.8M edges) loads in 76 seconds on commodity hardware (Mac Mini M4, 16GB RAM), and the signature cross-KG query -- ``which pathways are disrupted by drugs in Phase~3 breast cancer trials?'' -- returns validated results in 2.1 seconds.
Abstract (translated)
URL
https://arxiv.org/abs/2603.15080
PDF
-
Extracting Breast Cancer Phenotypes from Clinical Notes: Comparing LLMs with Classical Ontology Methods
2026-03-16 05:09:20Abdullah Bin Faiz, Arbaz Khan Shehzad, Asad Afzal, Momin Tariq, Muhammad Siddiqi, Muhammad Usamah Shahid, Maryam Noor Awan, Muddassar FarooqAbstract
A significant amount of data held in Oncology Electronic Medical Records (EMRs) is contained in unstructured provider notes -- including but not limited to the chemotherapy (or cancer treatment) outcome, different biomarkers, the tumor's location, sizes, and growth patterns of a patient. The clinical studies show that the majority of oncologists are comfortable providing these valuable insights in their notes in a natural language rather than the relevant structured fields of an EMR. The major contribution of this research is to report an LLM-based framework to process provider notes and extract valuable medical knowledge and phenotype mentioned above, with a focus on the domain of oncology. In this paper, we focus on extracting phenotypes related to breast cancer using our LLM framework, and then compare its performance with earlier works that used knowledge-driven annotation system, paired with the NCIt Ontology Annotator. The results of the study show that an LLM-based information extraction framework can be easily adapted to extract phenotypes with an accuracy that is comparable to the classical ontology-based methods. However, once trained, they could be easily fine-tuned to cater for other cancer types and diseases.
Abstract (translated)
URL
https://arxiv.org/abs/2604.06208
PDF
-
The GELATO Dataset for Legislative NER
2026-03-14 21:32:36Matthew Flynn, Timothy Obiso, Sam NewmanAbstract
This paper introduces GELATO (Government, Executive, Legislative, and Treaty Ontology), a dataset of U.S. House and Senate bills from the 118th Congress annotated using a novel two-level named entity recognition ontology designed for U.S. legislative texts. We fine-tune transformer-based models (BERT, RoBERTa) of different architectures and sizes on this dataset for first-level prediction. We then use LLMs with optimized prompts to complete the second level prediction. The strong performance of RoBERTa and relatively weak performance of BERT models, as well as the application of LLMs as second-level predictors, support future research in legislative NER or downstream tasks using these model combinations as extraction tools.
Abstract (translated)
URL
https://arxiv.org/abs/2603.14130
PDF
-
Leveraging Large Language Models and Survival Analysis for Early Prediction of Chemotherapy Outcomes
2026-03-12 06:25:12Muhammad Faisal Shahid, Asad Afzal, Abdullah Faiz, Muhammad Siddiqui, Arbaz Khan Shehzad, Fatima Aftab, Muhammad Usamah Shahid, Muddassar FarooqAbstract
Chemotherapy for cancer treatment is costly and accompanied by severe side effects, highlighting the critical need for early prediction of treatment outcomes to improve patient management and informed decision-making. Predictive models for chemotherapy outcomes using real-world data face challenges, including the absence of explicit phenotypes and treatment outcome labels such as cancer progression and toxicity. This study addresses these challenges by employing Large Language Models (LLMs) and ontology-based techniques for phenotypes and outcome label extraction from patient notes. We focused on one of the most frequently occurring cancers, breast cancer, due to its high prevalence and significant variability in patient response to treatment, making it a critical area for improving predictive modeling. The dataset included features such as vitals, demographics, staging, biomarkers, and performance scales. Drug regimens and their combinations were extracted from the chemotherapy plans in the EMR data and shortlisted based on NCCN guidelines, verified with NIH standards, and analyzed through survival modeling. The proposed approach significantly reduced phenotypes sparsity and improved predictive accuracy. Random Survival Forest was used to predict time-to-failure, achieving a C-index of 73%, and utilized as a classifier at a specific time point to predict treatment outcomes, with accuracy and F1 scores above 70%. The outcome probabilities were validated for reliability by calibration curves. We extended our approach to four other cancer types. This research highlights the potential of early prediction of treatment outcomes using LLM-based clinical data extraction enabling personalized treatment plans with better patient outcomes.
Abstract (translated)
化疗作为癌症治疗的一种方法，成本高昂且伴有严重的副作用，这突显了早期预测治疗效果以改进患者管理和辅助知情决策的迫切需求。利用真实世界数据构建化疗疗效预测模型面临着挑战，例如缺乏明确的表型和治疗结果标签（如癌症进展和毒性）。本研究通过使用大型语言模型（LLMs）和基于本体的技术从病历笔记中提取表型和结果标签来解决这些问题。我们重点关注乳腺癌这一最常见的癌症之一，因为它的高发病率以及患者对治疗反应的高度变异性使其成为改进预测建模的关键领域。 数据集包括生命体征、人口统计学信息、分期、生物标志物及表现评分等特征。从电子病历（EMR）中的化疗计划中提取药物方案及其组合，并根据NCCN指南筛选，与NIH标准验证并进行生存分析模型的分析。所提出的方法显著减少了表型稀疏性并提高了预测准确性。 随机生存森林被用来预测失效时间，达到了C指数73%，并在特定时间点作为分类器来预测治疗效果，准确率和F1分数均超过70%。通过校准曲线验证了结果的概率可靠性。 我们进一步将这种方法扩展到了另外四种癌症类型上。这项研究强调了利用基于LLM的临床数据提取技术实现早期治疗效果预测的潜力，这有助于制定个性化的治疗计划并提高患者预后质量。
URL
https://arxiv.org/abs/2603.11594
PDF
-
An Extreme Multi-label Text Classification Library Dataset: What if we took 'Use of Practical AI in Digital Libraries' seriously?
2026-03-11 15:24:20Jennifer D'Souza, Sameer Sadruddin, Maximilian K\"ahler, Andrea Salfinger, Luca Zaccagna, Francesca Incitti, Lauro Snidaro, Osma SuominenAbstract
Subject indexing is vital for discovery but hard to sustain at scale and across languages. We release a large bilingual (English/German) corpus of catalog records annotated with the Integrated Authority File (GND), plus a machine-actionable GND taxonomy. The resource enables ontology-aware multi-label classification, mapping text to authority terms, and agent-assisted cataloging with reproducible, authority-grounded evaluation. We provide a brief statistical profile and qualitative error analyses of three systems. We invite the community to assess not only accuracy but usefulness and transparency, toward authority-anchored AI co-pilots that amplify catalogers' work.
Abstract (translated)
主题索引对于发现至关重要，但在大规模和跨语言环境中维护却颇具挑战。我们发布了一个大型的双语（英语/德语）目录记录语料库，并且这些记录使用综合权威文件（GND）进行了注释，同时还提供了一个机器可操作的GND分类学。该资源支持基于本体的知识多标签分类、文本到权威术语的映射以及代理辅助编目的工作，具备可重复性和以权威为基础的评估能力。我们提供了三个系统的简要统计概况和定性错误分析。我们邀请社区不仅从准确性方面，还从实用性及透明度方面对这些系统进行评价，以便共同开发出锚定于权威、能够增强目录员工作的AI辅助工具。
URL
https://arxiv.org/abs/2603.10876
PDF
-
Unifying Ontology Construction and Semantic Alignment for Deterministic Enterprise Reasoning at Scale
2026-03-11 09:45:17Hongyin ZhuAbstract
While enterprises amass vast quantities of data, much of it remains chaotic and effectively dormant, preventing decision-making based on comprehensive information. Existing neuro-symbolic approaches rely on disjoint pipelines and struggle with error propagation. We introduce the large ontology model (LOM), a unified framework that seamlessly integrates ontology construction, semantic alignment, and logical reasoning into a single end-to-end architecture. LOM employs a construct-align-reason (CAR) pipeline, leveraging its unified architecture across all three stages: it first autonomously constructs a domain-specific ontological universe from raw data, then aligns neural generation with this structural reality using a graph-aware encoder and reinforcement learning, and finally executes deterministic reasoning over the constructed topology, node attributes and relation types. We evaluate LOM on a comprehensive benchmark constructed from diverse real-world enterprise datasets. Experimental results demonstrate that LOM-4B achieves 88.8% accuracy in ontology completion and 94% in complex graph reasoning tasks, significantly outperforming state-of-the-art LLMs. These findings validate that autonomous logical construction is essential for achieving deterministic, enterprise-grade intelligence.
Abstract (translated)
URL
https://arxiv.org/abs/2604.09608
PDF
-
Automated Standardization of Legacy Biomedical Metadata Using an Ontology-Constrained LLM Agent
2026-03-10 18:47:30Josef Hardi, Martin J. O'Connor, Marcos Martinez-Romero, Jean G. Rosario, Stephen A. Fisher, Mark A. MusenAbstract
Scientific metadata are often incomplete and noncompliant with community standards, limiting dataset findability, interoperability, and reuse. When reporting guidelines exist, they typically lack machine-actionable representations. Producing FAIR datasets requires encoding metadata standards as machine-actionable templates with rich field specifications and precise value constraints. Recent work has shown that LLMs guided by field names and ontology constraints can improve metadata standardization, but these approaches treat constraints as static text prompts, relying on the model's training knowledge alone. We present an LLM-based metadata standardization system that queries authoritative biomedical terminology services in real time to retrieve canonically correct vocabulary terms on demand. We evaluate this approach on 839 legacy metadata records from the Human BioMolecular Atlas Program (HuBMAP) using an expert-curated gold standard for exact-match assessment. Our evaluation shows that augmenting the LLM with real-time tool access consistently improves prediction accuracy over the LLM alone across both ontology-constrained and non-ontology-constrained fields, demonstrating a practical, scalable approach to automated standardization of biomedical metadata.
Abstract (translated)
URL
https://arxiv.org/abs/2604.08552
PDF
-
Beyond Fine-Tuning: Robust Food Entity Linking under Ontology Drift with FoodOntoRAG
2026-03-10 14:57:34Jan Drole, Ana Gjorgjevikj, Barbara Korou\v{s}i'c Seljak, Tome EftimovAbstract
Standardizing food terms from product labels and menus into ontology concepts is a prerequisite for trustworthy dietary assessment and safety reporting. The dominant approach to Named Entity Linking (NEL) in the food and nutrition domains fine-tunes Large Language Models (LLMs) on task-specific corpora. Although effective, fine-tuning incurs substantial computational cost, ties models to a particular ontology snapshot (i.e., version), and degrades under ontology drift. This paper presents FoodOntoRAG, a model- and ontology-agnostic pipeline that performs few-shot NEL by retrieving candidate entities from domain ontologies and conditioning an LLM on structured evidence (food labels, synonyms, definitions, and relations). A hybrid lexical--semantic retriever enumerates candidates; a selector agent chooses a best match with rationale; a separate scorer agent calibrates confidence; and, when confidence falls below a threshold, a synonym generator agent proposes reformulations to re-enter the loop. The pipeline approaches state-of-the-art accuracy while revealing gaps and inconsistencies in existing annotations. The design avoids fine-tuning, improves robustness to ontology evolution, and yields interpretable decisions through grounded justifications.
Abstract (translated)
将食品标签和菜单上的食品术语标准化为本体概念是进行可信的膳食评估和安全报告的前提。在食品和营养领域，命名实体链接（Named Entity Linking，NEL）的主要方法是对大型语言模型（LLMs）使用特定任务的数据集进行微调。尽管这种方法有效，但它会带来显著的计算成本，并且将模型绑定到某个特定版本的本体上，在面对本体漂移时性能会下降。 本文介绍了一种名为FoodOntoRAG的技术，它是一个与模型和本体无关的管道，通过从领域本体中检索候选实体并让LLM基于结构化证据（食品标签、同义词、定义及关系）进行微调来执行少样本NEL。这一技术采用了混合词汇-语义检索器来枚举候选实体；选择代理根据理由选择最佳匹配项；一个独立的评分代理调整信心水平；当自信度低于某个阈值时，同义词生成代理会提出替换方案以重新进入循环。 该管道在达到当前最先进的准确性的同时，揭示了现有注释中的缺口和不一致之处。其设计避免了微调过程，提高了对本体演变的鲁棒性，并通过基于事实的理由提供了可解释性的决策。
URL
https://arxiv.org/abs/2603.09758
PDF
-
SMGI: A Structural Theory of General Artificial Intelligence
2026-03-09 02:31:31Aomar OsmaniAbstract
We introduce SMGI, a structural theory of general artificial intelligence, and recast the foundational problem of learning from the optimization of hypotheses within fixed environments to the controlled evolution of the learning interface itself. We formalize the Structural Model of General Intelligence (SMGI) via a typed meta-model $\theta = (r,\mathcal H,\Pi,\mathcal L,\mathcal E,\mathcal M)$ that treats representational maps, hypothesis spaces, structural priors, multi-regime evaluators, and memory operators as explicitly typed, dynamic components. By enforcing a strict mathematical separation between this structural ontology ($\theta$) and its induced behavioral semantics ($T_\theta$), we define general artificial intelligence as a class of admissible coupled dynamics $(\theta, T_\theta)$ satisfying four obligations: structural closure under typed transformations, dynamical stability under certified evolution, bounded statistical capacity, and evaluative invariance across regime shifts. We prove a structural generalization bound that links sequential PAC-Bayes analysis and Lyapunov stability, providing sufficient conditions for capacity control and bounded drift under admissible task transformations. Furthermore, we establish a strict structural inclusion theorem demonstrating that classical empirical risk minimization, reinforcement learning, program-prior models (Solomonoff-style), and modern frontier agentic pipelines operate as structurally restricted instances of SMGI.
Abstract (translated)
我们介绍了SMGI，即通用人工智能的结构理论，并将从固定环境中优化假设的基础学习问题转化为自身学习界面的可控进化。通过一个类型化元模型 $\theta = (r, \mathcal{H}, \Pi, \mathcal{L}, \mathcal{E}, \mathcal{M})$，我们形式化了结构智能模型（Structural Model of General Intelligence，SMGI），该模型将表示映射、假设空间、结构性先验、多制式评估器和记忆操作符视为显式的类型化动态组件。通过在这一结构本体论（$\theta$）与其诱导的行为语义（$T_\theta$）之间强制执行严格的数学分离，我们将通用人工智能定义为一类可接受的耦合动力学（$\theta, T_\theta$），这些动力学满足四个义务：类型变换下的结构性闭包、认证演化下的动态稳定性、统计容量有限以及制式转换下的评估不变性。我们证明了一个结构泛化界限，该界限将序列PAC-Bayes分析和李雅普诺夫稳定性联系起来，为在可接受的任务转换下控制容量并限制漂移提供了充分条件。此外，我们建立了一个严格的结构包含定理，表明传统的经验风险最小化、强化学习、程序先验模型（如Solomonoff风格）以及现代前沿代理管道都是SMGI的结构受限实例。
URL
https://arxiv.org/abs/2603.07896
PDF
-
Elenchus: Generating Knowledge Bases from Prover-Skeptic Dialogues
2026-03-07 01:26:36Bradley P. AllenAbstract
We present Elenchus, a dialogue system for knowledge base construction grounded in inferentialist semantics, where knowledge engineering is re-conceived as explicitation rather than extraction from expert testimony or textual content. A human expert develops a bilateral position (commitments and denials) about a topic through prover-skeptic dialogue with a large language model (LLM) opponent. The LLM proposes tensions (claims that parts of the position are jointly incoherent) which the expert resolves by retraction, refinement, or contestation. The LLM thus serves as a defeasible derivability oracle whose unreliability is structurally contained by the expert's authority. Our main technical contribution is a mapping from Elenchus dialectical states to material bases in Hlobil and Brandom's NonMonotonic MultiSuccedent (NMMS) logic, satisfying Containment and enabling the elaboration of logical vocabulary that makes explicit the inferential relationships negotiated in the dialectic. We demonstrate the approach on the W3C PROV-O provenance ontology, where a single dialogue session elicits and structures design tensions that a domain expert can articulate, corresponding to decisions documented in a retrospective analysis of the ontology's design. Using pyNMMS, an automated NMMS reasoner, we verify that the structural properties of the resulting material base (nontransitivity, nonmonotonicity, and independence) correspond to specific PROV design rationales, demonstrating end-to-end integration from dialogue through formal reasoning.
Abstract (translated)
我们介绍了一种名为Elenchus的对话系统，该系统旨在通过推理主义语义学构建知识库。在这个框架中，知识工程被重新定义为明确化过程，而非从专家证词或文本内容中的提取。一个领域内的专家会与大型语言模型（LLM）对手进行证明者-怀疑论者的对话，并就某个主题形成双边立场（承诺和否定）。在这一过程中，LLM提出张力（即认为该立场的部分是共同自相矛盾的主张），而专家则通过撤回、细化或反驳来解决这些张力。因此，LLM充当了一个可推翻性的推理预言机，其不可靠性被专家权威所结构化地限制。 我们的主要技术贡献在于将Elenchus辩证状态映射到了Hlobil和Brandom的非单调多后承（NMMS）逻辑中的材料基础，确保了包含原则并允许扩展逻辑词汇以明确在辩论中协商的推理关系。我们通过W3C PROV-O出处本体来展示这种方法的应用，在单一对话会话中引发了领域专家可以阐明的设计张力，并且这些张力对应于对本体设计的回顾分析中的记录决策。 利用pyNMMS（一个自动化NMMS推理器），我们验证了所得材料基础的结构性特征（非传递性、非单调性和独立性）与特定PROV设计理由相吻合，从而展示了从对话到正式推理的端到端集成。
URL
https://arxiv.org/abs/2603.06974
PDF
