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

2026-06-18

AI Summary

This study addresses key challenges in reliability modeling of cyber-physical systems (CPS), including heavy reliance on expert knowledge, incomplete failure documentation, and inadequate representation of subsystem interactions. To overcome these limitations, the authors propose a Capability Interaction Graph (CIG) grounded in the Unified Foundational Ontology (UFO) to construct a semantic knowledge graph for CPS. This framework automatically derives fault trees to identify failure propagation paths and minimal cut sets. By integrating an ontology-driven CIG with knowledge graph technology, the approach enables unified semantic modeling across engineering domains and supports automated fault tree generation. Experimental results demonstrate that the proposed method explicitly captures functional dependencies and system semantics, significantly reducing modeling complexity while enhancing the accuracy of fault analysis.

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.

Problem

• Cyber-Physical Systems
• reliability modeling
• failure analysis
• subsystem interactions
• fault propagation

Innovation

• Capability Interaction Graph
• Ontology-Grounded Knowledge Graph
• Fault Tree Synthesis
• Cyber-Physical Systems
• Automated Reliability Modeling