Ontology-Grounded Capability Interaction Graphs: From Knowledge Graphs to Fault Trees

• URL: http://arxiv.org/abs/2606.20779v1
• Date: Thu, 18 Jun 2026 16:36:23 GMT
• Title: Ontology-Grounded Capability Interaction Graphs: From Knowledge Graphs to Fault Trees
• Abstract summary: This paper introduces the Capability Interaction Graph (CIG), an ontology-driven representation of Cyber-Physical Systems (CPSs)<n>Building upon this representation, we propose an automated synthesis algorithm for generating Fault Trees (FTs) directly from CIGs encoded as knowledge graphs.<n>We provide a common semantic representation across engineering domains and support the automated generation of reliability models.
• Score: 0.29494468099506904
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The development of Cyber-Physical Systems (CPSs) is inherently multidisciplinary, involving expertise from domains such as software engineering, electrical engineering, and mechatronics. throughout the lifecycle of the system, from design to deployment. Ensuring system reliability in Cyber-Physical Systems (CPSs) requires the identification and analysis of potential failures and their cascading effects. However, reliability modeling remains a challenging and error-prone activity, as it often depends on tacit expert knowledge, incomplete documentation of failure modes, and limited consideration of interactions between subsystems. To address these challenges, this paper introduce the Capability Interaction Graph (CIG), an ontology-driven representation of CPS architectures grounded in the Unified Foundational Ontology (UFO). Due to its graph-based structure, a CIG is naturally represented as a knowledge graph (KG), enabling the explicit capture of functional dependencies and system semantics. Building upon this representation, we propose an automated synthesis algorithm for generating Fault Trees (FTs) directly from CIGs encoded as knowledge graphs. Fault Tree Analysis provides an effective mechanism for evaluating critical failure properties, including failure propagation paths and minimal cut set sets. Our approach reduces this complexity by leveraging CIGs and knowledge graphs. We provide a common semantic representation across engineering domains and support the automated generation of reliability models.

Related papers

• CausalGraphX: A Counterfactual Graph Neural Network Framework for Explainable Systemic Risk Assessment [3.9523001246697813]
  We introduce CausalGraphX, a novel framework that integrates GNNs with counterfactual reasoning to provide explainable assessments of systemic risk.<n>Our results demonstrate that CausalGraphX significantly outperforms traditional and deep learning baselines in predicting cascading defaults.
  arXiv  Detail & Related papers  (2026-07-15T23:10:06Z)
• From Graphs to Gradients: Physics-Inspired Structural Attribution for Cyber-Physical IoT Systems and Beyond [7.419398719484998]
  Interpretable explanation methods in Artificial Intelligence aim to uncover the underlying causes and their effects.<n>This paper introduces a novel framework inspired by statistical mechanics that instead models variable dependencies through an undirected, energy-based representation.<n>We empirically examined our framework through simulations on an industrial IoT testbed with hybrid continuous and discrete variables.
  arXiv  Detail & Related papers  (2026-07-06T18:59:27Z)
• A data-driven security quantification framework for IoT-based systems [1.7499351967216337]
  This study introduces a data-driven probabilistic security framework for IoT-based safety-critical systems.<n>It integrates Model-Based Systems Engineering (SEMB), Attack Tree Analysis (ATA) and empirical vulnerability data.<n>It quantifies system compromise probabilities, identifies likely causes of attacks, and prioritises mitigation strategies.
  arXiv  Detail & Related papers  (2026-06-15T11:04:04Z)
• Spatio-Temporal Attention Graph Neural Network: Explaining Causalities With Attention [0.0]
  Industrial Control Systems underpin critical infrastructure and face growing cyber-physical threats.<n>Machine learning-based anomaly detection approaches in ICS shows strong theoretical performance.<n>We propose a Spatio-Temporal Attention Graph Neural Network (STA-GNN) for unsupervised and explainable anomaly detection in ICS.
  arXiv  Detail & Related papers  (2026-03-11T11:41:38Z)
• CLAIRE: Compressed Latent Autoencoder for Industrial Representation and Evaluation -- A Deep Learning Framework for Smart Manufacturing [51.56484100374058]
  CLAIRE is a hybrid end-to-end learning framework that integrates unsupervised deep representation learning with supervised classification for intelligent quality control in smart manufacturing systems.<n>It employs an optimized deep autoencoder to transform raw input into a compact latent space, effectively capturing the intrinsic data structure while suppressing irrelevant or noisy features.<n>The proposed framework highlights the potential of integrating explainable AI with feature-aware regularization for robust fault detection.
  arXiv  Detail & Related papers  (2026-03-06T15:11:58Z)
• Formal Analysis of Metastable Failures in Software Systems [5.436969030534807]
  We provide the mathematical foundations of metastability in request-response server systems.<n>We show how to construct continuous-time Markov chains (CTMCs) that approximate the semantics of the programs.<n>We show that our qualitative visual analysis captures and predicts many instances of metastability that were observed in the field in a matter of milliseconds.
  arXiv  Detail & Related papers  (2025-10-03T22:44:07Z)
• Representing Time-Continuous Behavior of Cyber-Physical Systems in Knowledge Graphs [43.655629596182486]
  This paper introduces a modular semantic model to represent differential equations directly within knowledge graphs and to enrich them semantically.<n>A validation of these artifacts was conducted in the domain of aviation maintenance.<n>Results show that differential equations of a complex Electro-Hydraulic Servoactuator can be formally represented in a knowledge graph and be contextualized with other lifecycle data.
  arXiv  Detail & Related papers  (2025-06-08T13:54:23Z)
• Learning to Model Graph Structural Information on MLPs via Graph Structure Self-Contrasting [50.181824673039436]
  We propose a Graph Structure Self-Contrasting (GSSC) framework that learns graph structural information without message passing. The proposed framework is based purely on Multi-Layer Perceptrons (MLPs), where the structural information is only implicitly incorporated as prior knowledge. It first applies structural sparsification to remove potentially uninformative or noisy edges in the neighborhood, and then performs structural self-contrasting in the sparsified neighborhood to learn robust node representations.
  arXiv  Detail & Related papers  (2024-09-09T12:56:02Z)
• Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision [51.88848982611515]
  Unsupervised graph neural architecture search remains unexplored in the literature. We propose a novel Disentangled Self-supervised Graph Neural Architecture Search model. Our model is able to achieve state-of-the-art performance against several baseline methods in an unsupervised manner.
  arXiv  Detail & Related papers  (2024-03-08T05:23:55Z)
• Representing Timed Automata and Timing Anomalies of Cyber-Physical Production Systems in Knowledge Graphs [51.98400002538092]
  This paper aims to improve model-based anomaly detection in CPPS by combining the learned timed automaton with a formal knowledge graph about the system. Both the model and the detected anomalies are described in the knowledge graph in order to allow operators an easier interpretation of the model and the detected anomalies.
  arXiv  Detail & Related papers  (2023-08-25T15:25:57Z)
• Neural combinatorial optimization beyond the TSP: Existing architectures under-represent graph structure [9.673093148930876]
  We analyze how and whether recent neural architectures can be applied to graph problems of practical importance. We show that augmenting the structural representation of problems with Distance is a promising step towards the still-ambitious goal of learning multi-purpose autonomous solvers.
  arXiv  Detail & Related papers  (2022-01-03T14:14:28Z)
• Bayesian Inductive Learner for Graph Resiliency under uncertainty [1.9254132307399257]
  We propose a Bayesian graph neural network-based framework for identifying critical nodes in a large graph. The fidelity and the gain in computational complexity offered by the framework are illustrated.
  arXiv  Detail & Related papers  (2020-12-26T07:22:29Z)
• Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification [50.83222170524406]
  We study the intrinsic difficulty in graph classification under the unified concept of resolution dilemmas'' We propose SLIM'', an inductive neural network model for Structural Landmarking and Interaction Modelling.
  arXiv  Detail & Related papers  (2020-06-29T01:01:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.