Ontology-Grounded Capability Interaction Graphs: From Knowledge Graphs to Fault Trees
This work addresses the challenge of reliability modeling in multidisciplinary Cyber-Physical Systems by providing a common semantic representation and automated fault tree generation, though it is an incremental improvement over existing knowledge graph and fault tree synthesis methods.
The paper introduces the Capability Interaction Graph (CIG), an ontology-driven representation for CPS architectures, and proposes an automated algorithm to generate Fault Trees from CIGs encoded as knowledge graphs, aiming to reduce the complexity and error-proneness of reliability modeling.
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.