Knowledge_Graph
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GraphRAG for Engineering Diagrams: ChatP&ID Enables LLM Interaction with P&IDs
2026-03-23 19:44:02Achmad Anggawirya Alimin, Artur M. SchweidtmannAbstract
Large Language Models (LLMs) combined with Retrieval-Augmented Generation (RAG) and knowledge graphs offer new opportunities for interacting with engineering diagrams such as Piping and Instrumentation Diagrams (P&IDs). However, directly processing raw images or smart P&ID files with LLMs is often costly, inefficient, and prone to hallucinations. This work introduces ChatP&ID, an agentic framework that enables grounded and cost-effective natural-language interaction with P&IDs using Graph Retrieval-Augmented Generation (GraphRAG), a paradigm we refer to as GraphRAG for engineering diagrams. Smart P&IDs encoded in the DEXPI standard are transformed into structured knowledge graphs, which serve as the basis for graph-based retrieval and reasoning by LLM agents. This approach enables reliable querying of engineering diagrams while significantly reducing computational cost. Benchmarking across commercial LLM APIs (OpenAI, Anthropic) demonstrates that graph-based representations improve accuracy by 18% over raw image inputs and reduce token costs by 85% compared to directly ingesting smart P&ID files. While small open-source models still struggle to interpret knowledge graph formats and structured engineering data, integrating them with VectorRAG and PathRAG improves response accuracy by up to 40%. Notably, GPT-5-mini combined with ContextRAG achieves 91% accuracy at a cost of only $0.004 per task. The resulting ChatP&ID interface enables intuitive natural-language interaction with complex engineering diagrams and lays the groundwork for AI-assisted process engineering tasks such as Hazard and Operability Studies (HAZOP) and multi-agent analysis.
Abstract (translated)
大语言模型（LLMs）结合检索增强生成（RAG）与知识图谱，为与管道及仪表流程图（P&ID）等工程图纸的交互提供了新机遇。然而，直接使用LLM处理原始图像或智能P&ID文件往往成本高昂、效率低下且易产生幻觉。本研究提出ChatP&ID——一个基于图的检索增强生成（GraphRAG）的智能体框架，我们称之为面向工程图纸的GraphRAG。该框架将符合DEXPI标准的智能P&ID转化为结构化知识图谱，作为LLM智能体进行基于图的检索与推理基础。此方法在显著降低计算成本的同时，实现了对工程图纸的可靠查询。通过对商业LLM API（OpenAI、Anthropic）的基准测试表明，基于图的表示相比原始图像输入将准确率提升了18%，相比直接摄入智能P&ID文件降低了85%的令牌成本。尽管小型开源模型在解读知识图谱格式和结构化工程数据方面仍存在困难，但将其与VectorRAG和PathRAG结合可使响应准确率提升高达40%。值得注意的是，GPT-5-mini结合ContextRAG以每任务仅0.004美元的成本达到了91%的准确率。由此产生的ChatP&ID界面实现了与复杂工程图纸的直观自然语言交互，并为危险与可操作性研究（HAZOP）和多智能体分析等AI辅助工艺工程任务奠定了基础。
URL
https://arxiv.org/abs/2603.22528
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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.
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URL
https://arxiv.org/abs/2603.22136
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Agentic Personas for Adaptive Scientific Explanations with Knowledge Graphs
2026-03-23 11:35:40Susana Nunes, Tiago Guerreiro, Catia PesquitaAbstract
AI explanation methods often assume a static user model, producing non-adaptive explanations regardless of expert goals, reasoning strategies, or decision contexts. Knowledge graph-based explanations, despite their capacity for grounded, path-based reasoning, inherit this limitation. In complex domains such as scientific discovery, this assumption fails to capture the diversity of cognitive strategies and epistemic stances among experts, preventing explanations that foster deeper understanding and informed decision-making. However, the scarcity of human experts limits the use of direct human feedback to produce adaptive explanations. We present a reinforcement learning approach for scientific explanation generation that incorporates agentic personas, structured representations of expert reasoning strategies, that guide the explanation agent towards specific epistemic preferences. In an evaluation of knowledge graph-based explanations for drug discovery, we tested two personas that capture distinct epistemic stances derived from expert feedback. Results show that persona-driven explanations match state-of-the-art predictive performance while persona preferences closely align with those of their corresponding experts. Adaptive explanations were consistently preferred over non-adaptive baselines (n = 22), and persona-based training reduces feedback requirements by two orders of magnitude. These findings demonstrate how agentic personas enable scalable adaptive explainability for AI systems in complex and high-stakes domains.
Abstract (translated)
AI解释方法通常预设静态用户模型，无论专家目标、推理策略或决策情境如何，均生成非自适应解释。基于知识图谱的解释方法尽管具备基于路径的具身推理能力，却也继承了这一局限。在科学发现等复杂领域中，该假设无法捕捉专家间认知策略与认知立场的多样性，导致解释无法促进深度理解与知情决策。然而，人类专家稀缺，限制了利用直接人类反馈生成自适应解释的可能。我们提出一种用于科学解释生成的强化学习方法，通过引入主体性角色——即对专家推理策略的结构化表征——引导解释智能体趋向特定认知偏好。在针对药物发现的知识图谱解释评估中，我们测试了两种源自专家反馈的不同认知立场角色。结果表明，角色驱动的解释在匹配最先进预测性能的同时，其角色偏好与对应专家的偏好高度一致。自适应解释持续受到青睐（n=22），且基于角色的训练将反馈需求降低了两个数量级。这些发现证明了主体性角色如何使AI系统在复杂高风险领域中实现可扩展的自适应可解释性。
URL
https://arxiv.org/abs/2603.21846
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SynLeaF: A Dual-Stage Multimodal Fusion Framework for Synthetic Lethality Prediction Across Pan- and Single-Cancer Contexts
2026-03-23 05:55:39Zheming Xing, Siyuan Zhou, Ruinan Wang, Rui Han, Shiming Zhang, Shiqu Chen, Yurui Huang, Jiahao Ma, Yifan Chen, Xuan Wang, Yadong Wang, Junyi LiAbstract
Accurate prediction of synthetic lethality (SL) is important for guiding the development of cancer drugs and therapies. SL prediction faces significant challenges in the effective fusion of heterogeneous multi-source data. Existing multimodal methods often suffer from "modality laziness" due to disparate convergence speeds, which hinders the exploitation of complementary information. This is also one reason why most existing SL prediction models cannot perform well on both pan-cancer and single-cancer SL pair prediction. In this study, we propose SynLeaF, a dual-stage multimodal fusion framework for SL prediction across pan- and single-cancer contexts. The framework employs a VAE-based cross-encoder with a product of experts mechanism to fuse four omics data types (gene expression, mutation, methylation, and CNV), while simultaneously utilizing a relational graph convolutional network to capture structured gene representations from biomedical knowledge graphs. To mitigate modality laziness, SynLeaF introduces a dual-stage training mechanism employing featurelevel knowledge distillation with adaptive uni-modal teacher and ensemble strategies. In extensive experiments across eight specific cancer types and a pancancer dataset, SynLeaF achieves superior performance in 17 out of 19 scenarios. Ablation studies and gradient analyses further validate the critical contributions of the proposed fusion and distillation mechanisms to model robustness and generalization. To facilitate community use, a web server is available at this https URL.
Abstract (translated)
准确预测合成致死性（SL）对于指导癌症药物和疗法开发具有重要意义。SL预测在有效融合异构多源数据方面面临重大挑战。现有多模态方法常因模态收敛速度差异而出现“模态懒惰性”，这阻碍了互补信息的利用。这也是大多数现有SL预测模型在泛癌和单癌SL对预测上均无法表现良好的原因之一。本研究提出SynLeaF，一个用于泛癌与单癌场景下SL预测的双阶段多模态融合框架。该框架采用基于变分自编码器（VAE）的交叉编码器与专家乘积机制，融合四种组学数据类型（基因表达、突变、甲基化和拷贝数变异），同时利用关系图卷积网络从生物医学知识图中捕获结构化基因表示。为缓解模态懒惰性，SynLeaF引入了采用特征级知识蒸馏的双阶段训练机制，配合自适应单模态教师与集成策略。在涵盖八种特定癌症类型及泛癌数据集的广泛实验中，SynLeaF在19个场景中的17个取得优异性能。消融实验与梯度分析进一步验证了所提出融合与蒸馏机制对模型鲁棒性和泛化能力的关键贡献。为促进社区使用，相关网络服务器已在此https URL 发布。
URL
https://arxiv.org/abs/2603.22369
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KG-Hopper: Empowering Compact Open LLMs with Knowledge Graph Reasoning via Reinforcement Learning
2026-03-22 23:07:29Shuai Wang, Yinan YuAbstract
Large Language Models (LLMs) demonstrate impressive natural language capabilities but often struggle with knowledge-intensive reasoning tasks. Knowledge Base Question Answering (KBQA), which leverages structured Knowledge Graphs (KGs) exemplifies this challenge due to the need for accurate multi-hop reasoning. Existing approaches typically perform sequential reasoning steps guided by predefined pipelines, restricting flexibility and causing error cascades due to isolated reasoning at each step. To address these limitations, we propose KG-Hopper, a novel Reinforcement Learning (RL) framework that empowers compact open LLMs with the ability to perform integrated multi-hop KG reasoning within a single inference round. Rather than reasoning step-by-step, we train a Reasoning LLM that embeds the entire KG traversal and decision process into a unified ``thinking'' stage, enabling global reasoning over cross-step dependencies and dynamic path exploration with backtracking. Experimental results on eight KG reasoning benchmarks show that KG-Hopper, based on a 7B-parameter LLM, consistently outperforms larger multi-step systems (up to 70B) and achieves competitive performance with proprietary models such as GPT-3.5-Turbo and GPT-4o-mini, while remaining compact, open, and data-efficient. The code is publicly available at: this https URL.
Abstract (translated)
大型语言模型（LLMs）展现出卓越的自然语言能力，但在知识密集型推理任务中仍面临挑战。知识库问答（KBQA）利用结构化知识图谱（KGs），因需进行精确的多跳推理而成为典型难题。现有方法通常依赖预定义流水线执行顺序推理步骤，限制了灵活性，并因各步骤孤立推理导致误差累积。为应对这些局限，我们提出KG-Hopper——一种新颖的强化学习（RL）框架，使紧凑的开源LLMs能够在单次推理中实现集成的多跳KG推理。我们转而训练一个推理LLM，将整个KG遍历与决策过程嵌入统一的“思考”阶段，从而支持跨步骤依赖的全局推理及带回溯的动态路径探索。在八个KG推理基准测试中，基于70亿参数LLM的KG-Hopper持续超越规模更大的多步系统（最高达700亿参数），并与GPT-3.5-Turbo和GPT-4o-mini等专有模型表现相当，同时保持模型紧凑、开源且数据高效。代码已公开：此网址。
URL
https://arxiv.org/abs/2603.21440
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DomAgent: Leveraging Knowledge Graphs and Case-Based Reasoning for Domain-Specific Code Generation
2026-03-22 22:39:32Shuai Wang, Dhasarathy Parthasarathy, Robert Feldt, Yinan YuAbstract
Large language models (LLMs) have shown impressive capabilities in code generation. However, because most LLMs are trained on public domain corpora, directly applying them to real-world software development often yields low success rates, as these scenarios frequently require domain-specific knowledge. In particular, domain-specific tasks usually demand highly specialized solutions, which are often underrepresented or entirely absent in the training data of generic LLMs. To address this challenge, we propose DomAgent, an autonomous coding agent that bridges this gap by enabling LLMs to generate domain-adapted code through structured reasoning and targeted retrieval. A core component of DomAgent is DomRetriever, a novel retrieval module that emulates how humans learn domain-specific knowledge, by combining conceptual understanding with experiential examples. It dynamically integrates top-down knowledge-graph reasoning with bottom-up case-based reasoning, enabling iterative retrieval and synthesis of structured knowledge and representative cases to ensure contextual relevance and broad task coverage. DomRetriever can operate as part of DomAgent or independently with any LLM for flexible domain adaptation. We evaluate DomAgent on an open benchmark dataset in the data science domain (DS-1000) and further apply it to real-world truck software development tasks. Experimental results show that DomAgent significantly enhances domain-specific code generation, enabling small open-source models to close much of the performance gap with large proprietary LLMs in complex, real-world applications. The code is available at: this https URL.
Abstract (translated)
大语言模型（LLMs）在代码生成方面展现出卓越能力。然而，由于大多数LLMs基于公共领域语料训练，直接将其应用于实际软件开发往往成功率较低，因为这些场景通常需要领域特定知识。特别是，领域特定任务通常需要高度专业化的解决方案，而这些方案在通用LLMs的训练数据中往往代表性不足或完全缺失。为应对这一挑战，我们提出了DomAgent——一个自主编码代理，它通过结构化推理和定向检索，使LLMs能够生成领域适配代码，从而弥合这一差距。DomAgent的核心组件是DomRetriever，这是一个新颖的检索模块，通过结合概念理解与经验案例，模拟人类学习领域特定知识的方式。它动态整合自上而下的知识图谱推理与自下而上的基于案例的推理，实现结构化知识与代表性案例的迭代检索与综合，以确保上下文相关性和广泛的任务覆盖。DomRetriever既可作为DomAgent的组成部分运行，也可独立与任何LLM配合使用，实现灵活的领域适配。我们在数据科学领域的开放基准数据集（DS-1000）上评估了DomAgent，并将其进一步应用于真实的卡车软件开发任务。实验结果表明，DomAgent显著提升了领域特定代码生成能力，使小型开源模型在复杂现实应用中大幅缩小了与大型专有LLMs的性能差距。代码已公开于：此https链接。
URL
https://arxiv.org/abs/2603.21430
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Graph Fusion Across Languages using Large Language Models
2026-03-22 14:10:09Kaung Myat Kyaw, Khush Agarwal, Jonathan ChanAbstract
Combining multiple knowledge graphs (KGs) across linguistic boundaries is a persistent challenge due to semantic heterogeneity and the complexity of graph environments. We propose a framework for cross-lingual graph fusion, leveraging the in-context reasoning and multilingual semantic priors of Large Language Models (LLMs). The framework implements structural linearization by mapping triplets directly into natural language sequences (e.g., [head] [relation] [tail]), enabling the LLM to map relations and reconcile entities between an evolving fused graph ($G_{c}^{(t-1)}$) and a new candidate graph ($G_{t}$). Evaluated on the DBP15K dataset, this exploratory study demonstrates that LLMs can serve as a universal semantic bridge to resolve cross-lingual discrepancies. Results show the successful sequential agglomeration of multiple heterogeneous graphs, offering a scalable, modular solution for continuous knowledge synthesis in multi-source, multilingual environments.
Abstract (translated)
跨语言知识图谱（KGs）的融合因语义异构性和图结构的复杂性而长期面临挑战。本研究提出一种跨语言图融合框架，利用大语言模型（LLMs）的上下文推理能力与多语言语义先验知识。该框架通过将三元组直接映射为自然语言序列（如[头实体][关系][尾实体]）实现结构线性化，使LLM能够在演化融合图（$G_{c}^{(t-1)}$）与候选新图（$G_{t}$）之间映射关系并调和实体。在DBP15K数据集上的探索性研究表明，LLMs可作为通用语义桥梁解决跨语言差异。实验结果显示多个异构图成功实现顺序聚合，为多源多语言环境下的持续知识整合提供了可扩展、模块化的解决方案。
URL
https://arxiv.org/abs/2603.21248
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KLDrive: Fine-Grained 3D Scene Reasoning for Autonomous Driving based on Knowledge Graph
2026-03-22 02:54:10Ye Tian, Jingyi Zhang, Zihao Wang, Xiaoyuan Ren, Xiaofan Yu, Onat Gungor, Tajana RosingAbstract
Autonomous driving requires reliable reasoning over fine-grained 3D scene facts. Fine-grained question answering over multi-modal driving observations provides a natural way to evaluate this capability, yet existing perception pipelines and driving-oriented large language model (LLM) methods still suffer from unreliable scene facts, hallucinations, opaque reasoning, and heavy reliance on task-specific training. We present KLDrive, the first knowledge-graph-augmented LLM reasoning framework for fine-grained question answering in autonomous driving. KLDrive addresses this problem through designing two tightly coupled components: an energy-based scene fact construction module that consolidates multi-source evidence into a reliable scene knowledge graph, and an LLM agent that performs fact-grounded reasoning over a constrained action space under explicit structural constraints. By combining structured prompting with few-shot in-context exemplars, the framework adapts to diverse reasoning tasks without heavy task-specific fine-tuning. Experiments on two large-scale autonomous-driving QA benchmarks show that KLDrive outperforms prior state-of-the-art methods, achieving the best overall accuracy of 65.04% on NuScenes-QA and the best SPICE score of 42.45 on GVQA. On counting, the most challenging factual reasoning task, it improves over the strongest baseline by 46.01 percentage points, demonstrating substantially reduced hallucinations and the benefit of coupling reliable scene fact construction with explicit reasoning.
Abstract (translated)
自动驾驶需要对精细的3D场景事实进行可靠推理。基于多模态驾驶观测的细粒度问答为评估这一能力提供了自然途径，但现有感知流程和面向驾驶的大语言模型（LLM）方法仍受困于不可靠的场景事实、幻觉、不透明的推理过程以及对任务特定训练的过度依赖。我们提出KLDrive——首个用于自动驾驶细粒度问答的知识图谱增强型LLM推理框架。该框架通过设计两个紧密耦合的组件来解决这一问题：一是基于能量的场景事实验证模块，可将多源证据整合为可靠的场景知识图谱；二是LLM智能体，能在显式结构约束下于受限动作空间中进行基于事实的推理。通过结合结构化提示与少量上下文示例，该框架无需大量任务特定微调即可适应多样化的推理任务。在两个大规模自动驾驶问答基准测试上的实验表明，KLDrive超越了先前最先进方法，在NuScenes-QA上取得65.04%的最佳总体准确率，在GVQA上取得42.45的最佳SPICE分数。在最具挑战性的计数任务（一种事实推理任务）上，其表现比最强基线提升46.01个百分点，显著降低了幻觉现象，并验证了将可靠场景事实验证与显式推理相结合的优势。
URL
https://arxiv.org/abs/2603.21029
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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
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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。
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https://arxiv.org/abs/2603.20670
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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
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DIAL-KG: Schema-Free Incremental Knowledge Graph Construction via Dynamic Schema Induction and Evolution-Intent Assessment
2026-03-20 15:39:22Weidong Bao, Yilin Wang, Ruyu Gao, Fangling Leng, Yubin Bao, Ge YuAbstract
Knowledge Graphs (KGs) are foundational to applications such as search, question answering, and recommendation. Conventional knowledge graph construction methods are predominantly static, rely ing on a single-step construction from a fixed corpus with a prede f ined schema. However, such methods are suboptimal for real-world sce narios where data arrives dynamically, as incorporating new informa tion requires complete and computationally expensive graph reconstruc tions. Furthermore, predefined schemas hinder the flexibility of knowl edge graph construction. To address these limitations, we introduce DIAL KG, a closed-loop framework for incremental KG construction orches trated by a Meta-Knowledge Base (MKB). The framework oper ates in a three-stage cycle: (i) Dual-Track Extraction, which ensures knowledge completeness by defaulting to triple generation and switching to event extraction for complex knowledge; (ii) Governance Adjudica tion, which ensures the fidelity and currency of extracted facts to prevent hallucinations and knowledge staleness; and (iii) Schema Evolution, in which new schemas are induced from validated knowledge to guide subsequent construction cycles, and knowledge from the current round is incrementally applied to the existing KG. Extensive experiments demon strate that our framework achieves state-of-the-art (SOTA) performance in the quality of both the constructed graph and the induced schemas.
Abstract (translated)
知识图谱（KGs）是搜索、问答和推荐等应用的基础。传统的知识图谱构建方法主要是静态的，依赖于从固定语料库中基于预定义模式进行单步构建。然而，在数据动态到达的真实场景中，这类方法并非最优，因为整合新信息需要完整且计算成本高昂的图重建。此外，预定义模式也限制了知识图谱构建的灵活性。为应对这些局限，我们提出了DIAL KG——一个由元知识库（MKB）协调的增量式知识图谱构建闭环框架。该框架运行于三阶段循环：(i) 双轨提取，通过默认生成三元组并在复杂知识场景切换为事件提取来确保知识完整性；(ii) 治理裁决，确保提取事实的真实性与时效性，防止幻觉与知识过时；(iii) 模式演化，从验证知识中归纳新模式以指导后续构建循环，并将当前轮次的知识增量式整合至现有图谱。大量实验表明，该框架在构建图谱质量与归纳模式方面均达到最先进（SOTA）性能。
URL
https://arxiv.org/abs/2603.20059
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RouterKGQA: Specialized--General Model Routing for Constraint-Aware Knowledge Graph Question Answering
2026-03-20 15:01:57Bo Yuan, Hexuan Deng, Xuebo Liu, Min ZhangAbstract
Knowledge graph question answering (KGQA) is a promising approach for mitigating LLM hallucination by grounding reasoning in structured and verifiable knowledge graphs. Existing approaches fall into two paradigms: retrieval-based methods utilize small specialized models, which are efficient but often produce unreachable paths and miss implicit constraints, while agent-based methods utilize large general models, which achieve stronger structural grounding at substantially higher cost. We propose RouterKGQA, a framework for specialized--general model collaboration, in which a specialized model generates reasoning paths and a general model performs KG-guided repair only when needed, improving performance at minimal cost. We further equip the specialized with constraint-aware answer filtering, which reduces redundant answers. In addition, we design a more efficient general agent workflow, further lowering inference cost. Experimental results show that RouterKGQA outperforms the previous best by 3.57 points in F1 and 0.49 points in Hits@1 on average across benchmarks, while requiring only 1.15 average LLM calls per question. Codes and models are available at this https URL.
Abstract (translated)
知识图谱问答（KGQA）是一种通过将推理过程锚定于结构化且可验证的知识图谱来缓解大语言模型（LLM）幻觉问题的前景方法。现有方法分为两类范式：基于检索的方法使用小型专用模型，效率高但常产生不可达路径且遗漏隐式约束；基于智能体的方法使用大型通用模型，虽能实现更强的结构锚定，但成本显著更高。我们提出RouterKGQA框架，实现专用-通用模型协作：由专用模型生成推理路径，仅在必要时由通用模型执行知识图谱引导的修复，从而以最小成本提升性能。我们还为专用模型配备约束感知答案过滤机制，以减少冗余答案。此外，我们设计了更高效的通用智能体工作流，进一步降低推理成本。实验结果表明，RouterKGQA在多个基准测试中平均将F1值提升3.57点、Hits@1提升0.49点，同时每个问题仅需平均1.15次LLM调用。代码与模型已公开于此https链接。
URL
https://arxiv.org/abs/2603.20017
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Integrating Meta-Features with Knowledge Graph Embeddings for Meta-Learning
2026-03-20 11:57:25Antonis Klironomos, Ioannis Dasoulas, Francesco Periti, Mohamed Gad-Elrab, Heiko Paulheim, Anastasia Dimou, Evgeny KharlamovAbstract
The vast collection of machine learning records available on the web presents a significant opportunity for meta-learning, where past experiments are leveraged to improve performance. Two crucial meta-learning tasks are pipeline performance estimation (PPE), which predicts pipeline performance on target datasets, and dataset performance-based similarity estimation (DPSE), which identifies datasets with similar performance patterns. Existing approaches primarily rely on dataset meta-features (e.g., number of instances, class entropy, etc.) to represent datasets numerically and approximate these meta-learning tasks. However, these approaches often overlook the wealth of past experimental results and pipeline metadata available. This limits their ability to capture dataset - pipeline interactions that reveal performance similarity patterns. In this work, we propose KGmetaSP, a knowledge-graph-embeddings approach that leverages existing experiment data to capture these interactions and improve both PPE and DPSE. We represent datasets and pipelines within a unified knowledge graph (KG) and derive embeddings that support pipeline-agnostic meta-models for PPE and distance-based retrieval for DPSE. To validate our approach, we construct a large-scale benchmark comprising 144,177 OpenML experiments, enabling a rich cross-dataset evaluation. KGmetaSP enables accurate PPE using a single pipeline-agnostic meta-model and improves DPSE over baselines. The proposed KGmetaSP, KG, and benchmark are released, establishing a new reference point for meta-learning and demonstrating how consolidating open experiment data into a unified KG advances the field.
Abstract (translated)
网络上可获取的海量机器学习记录为元学习提供了重大机遇，即利用过往实验来提升性能。两个关键的元学习任务是：流水线性能预估（PPE），用于预测流水线在目标数据集上的表现；以及基于数据集性能的相似性预估（DPSE），用于识别具有相似性能模式的数据集。现有方法主要依赖数据集元特征（如实例数量、类别熵等）对数据集进行数值表征，并近似完成这些元学习任务。然而，这些方法往往忽视了海量的过往实验结果和流水线元数据，这限制了其捕捉能揭示性能相似性模式的数据集-流水线交互的能力。 本研究提出KGmetaSP，一种基于知识图谱嵌入的方法，利用现有实验数据捕捉这些交互，从而提升PPE和DPSE的性能。我们在统一的知识图谱（KG）中表征数据集和流水线，并导出支持与流水线无关的元模型（用于PPE）和基于距离的检索（用于DPSE）的嵌入。为验证方法，我们构建了包含144,177个OpenML实验的大规模基准，支持丰富的跨数据集评估。KGmetaSP能够通过单一与流水线无关的元模型实现准确的PPE，并在DPSE上优于基线方法。我们发布了提出的KGmetaSP、知识图谱及基准，为元学习领域建立了新的参考基准，并展示了将开放实验数据整合到统一知识图谱中如何推动该领域发展。
URL
https://arxiv.org/abs/2603.19888
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GIP-RAG: An Evidence-Grounded Retrieval-Augmented Framework for Interpretable Gene Interaction and Pathway Impact Analysis
2026-03-19 23:36:26Fujian Jia, Jiwen Gu, Cheng Lu, Dezhi Zhao, Mengjiang Huang, Yuanzhi Lu, Xin Liu, Kang LiuAbstract
Understanding mechanistic relationships among genes and their impacts on biological pathways is essential for elucidating disease mechanisms and advancing precision medicine. Despite the availability of extensive molecular interaction and pathway data in public databases, integrating heterogeneous knowledge sources and enabling interpretable multi-step reasoning across biological networks remain challenging. We present GIP-RAG (Gene Interaction Prediction through Retrieval-Augmented Generation), a computational framework that combines biomedical knowledge graphs with large language models (LLMs) to infer and interpret gene interactions. The framework constructs a unified gene interaction knowledge graph by integrating curated data from KEGG, WikiPathways, SIGNOR, Pathway Commons, and PubChem. Given user-specified genes, a query-driven module retrieves relevant subgraphs, which are incorporated into structured prompts to guide LLM-based stepwise reasoning. This enables identification of direct and indirect regulatory relationships and generation of mechanistic explanations supported by biological evidence. Beyond pairwise interactions, GIP-RAG includes a pathway-level functional impact module that simulates propagation of gene perturbations through signaling networks and evaluates potential pathway state changes. Evaluation across diverse biological scenarios demonstrates that the framework generates consistent, interpretable, and evidence-supported insights into gene regulatory mechanisms. Overall, GIP-RAG provides a general and interpretable approach for integrating knowledge graphs with retrieval-augmented LLMs to support mechanistic reasoning in complex molecular systems.
Abstract (translated)
URL
https://arxiv.org/abs/2603.20321
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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
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Are a Thousand Words Better Than a Single Picture? Beyond Images -- A Framework for Multi-Modal Knowledge Graph Dataset Enrichment
2026-03-17 12:41:02Pengyu Zhang, Klim Zaporojets, Jie Liu, Jia-Hong Huang, Paul GrothAbstract
Multi-Modal Knowledge Graphs (MMKGs) benefit from visual information, yet large-scale image collection is hard to curate and often excludes ambiguous but relevant visuals (e.g., logos, symbols, abstract scenes). We present Beyond Images, an automatic data-centric enrichment pipeline with optional human auditing. This pipeline operates in three stages: (1) large-scale retrieval of additional entity-related images, (2) conversion of all visual inputs into textual descriptions to ensure that ambiguous images contribute usable semantics rather than noise, and (3) fusion of multi-source descriptions using a large language model (LLM) to generate concise, entity-aligned summaries. These summaries replace or augment the text modality in standard MMKG models without changing their architectures or loss functions. Across three public MMKG datasets and multiple baseline models, we observe consistent gains (up to 7% Hits@1 overall). Furthermore, on a challenging subset of entities with visually ambiguous logos and symbols, converting images into text yields large improvements (201.35% MRR and 333.33% Hits@1). Additionally, we release a lightweight Text-Image Consistency Check Interface for optional targeted audits, improving description quality and dataset reliability. Our results show that scaling image coverage and converting ambiguous visuals into text is a practical path to stronger MMKG completion. Code, datasets, and supplementary materials are available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2603.16974
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SIA: A Synthesize-Inject-Align Framework for Knowledge-Grounded and Secure E-commerce Search LLMs with Industrial Deployment
2026-03-17 05:41:22Zhouwei Zhai, Mengxiang Chen, Anmeng ZhangAbstract
Large language models offer transformative potential for e-commerce search by enabling intent-aware recommendations. However, their industrial deployment is hindered by two critical challenges: (1) knowledge hallucination due to insufficient encoding of dynamic, fine-grained product knowledge, and (2) security vulnerabilities under jailbreak attacks that threaten compliance. To address these issues, we propose SI--a Synthesize-Inject-Align framework for building knowledgeable and secure e-commerce search LLMs. Our approach first synthesizes high-quality natural language corpus by combining structured knowledge graphs with unstructured behavioral logs, augmented with reasoning chains and safety-aware this http URL then introduce a parameter-efficient pre-training strategy based on Depth Up-Scaling to inject domain knowledge while preserving general capabilities. Finally, a dual-path alignment method via multi-task instruction tuning and adversarial training strengthens both task performance and safety robustness. The framework has been deployed at this http URL, China's largest self-operated e-commerce platform, where A/B tests across five core search scenarios demonstrate significant improvements in key business metrics, validating its industrial effectiveness and scalability.
Abstract (translated)
URL
https://arxiv.org/abs/2603.16137
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A Framework and Prototype for a Navigable Map of Datasets in Engineering Design and Systems Engineering
2026-03-16 17:08:20H. Sinan Bank, Daniel R. HerberAbstract
The proliferation of data across the system lifecycle presents both a significant opportunity and a challenge for Engineering Design and Systems Engineering (EDSE). While this ``digital thread'' has the potential to drive innovation, the fragmented and inaccessible nature of existing datasets hinders method validation, limits reproducibility, and slows research progress. Unlike fields such as computer vision and natural language processing, which benefit from established benchmark ecosystems, engineering design research often relies on small, proprietary, or ad-hoc datasets. This paper addresses this challenge by proposing a systematic framework for a ``Map of Datasets in EDSE.'' The framework is built upon a multi-dimensional taxonomy designed to classify engineering datasets by domain, lifecycle stage, data type, and format, enabling faceted discovery. An architecture for an interactive discovery tool is detailed and demonstrated through a working prototype, employing a knowledge graph data model to capture rich semantic relationships between datasets, tools, and publications. An analysis of the current data landscape reveals underrepresented areas (``data deserts'') in early-stage design and system architecture, as well as relatively well-represented areas (``data oases'') in predictive maintenance and autonomous systems. The paper identifies key challenges in curation and sustainability and proposes mitigation strategies, laying the groundwork for a dynamic, community-driven resource to accelerate data-centric engineering research.
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URL
https://arxiv.org/abs/2603.15722
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Knowledge Graph Extraction from Biomedical Literature for Alkaptonuria Rare Disease
2026-03-16 14:09:09Giang Pham, Rebecca Finetti, Caterina Graziani, Bianca Roncaglia, Asma Bendjeddou, Linda Brodo, Sara Brunetti, Moreno Falaschi, Stefano Forti, Silvia Giulia Galfr\'e, Paolo Milazzo, Corrado Priami, Annalisa Santucci, Ottavia Spiga, Alina S\^irbuAbstract
Alkaptonuria (AKU) is an ultra-rare autosomal recessive metabolic disorder caused by mutations in the HGD (Homogentisate 1,2-Dioxygenase) gene, leading to a pathological accumulation of homogentisic acid (HGA) in body fluids and tissues. This leads to systemic manifestations, including premature spondyloarthropathy, renal and prostatic stones, and cardiovascular complications. Being ultra-rare, the amount of data related to the disease is limited, both in terms of clinical data and literature. Knowledge graphs (KGs) can help connect the limited knowledge about the disease (basic mechanisms, manifestations and existing therapies) with other knowledge; however, AKU is frequently underrepresented or entirely absent in existing biomedical KGs. In this work, we apply a text-mining methodology based on PubTator3 for large-scale extraction of biomedical relations. We construct two KGs of different sizes, validate them using existing biochemical knowledge and use them to extract genes, diseases and therapies possibly related to AKU. This computational framework reveals the systemic interactions of the disease, its comorbidities, and potential therapeutic targets, demonstrating the efficacy of our approach in analyzing rare metabolic disorders.
Abstract (translated)
URL
https://arxiv.org/abs/2603.15711
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