A Community-Validated Knowledge Graph for Proactive Disruption Mitigation in Intermodal Transport Networks

Abstract Background: Intermodal transport networks are increasingly vulnerable to operational disruptions—ranging from infrastructure failures to labor shortages—that propagate rapidly across modes and geographic regions. Traditional decision support systems rely heavily on historical data and reactive alerts, leaving logistics operators ill-equipped to anticipate disruptions before they impact supply chain continuity. Objective: This research proposes a novel knowledge-driven framework that transforms tacit, real-time insights from e-communities of practice (CoPs) into a structured, machine-interpretable knowledge graph for proactive disruption mitigation in intermodal transport networks. Method: The framework comprises three core components. First, a domain ontology models disruption events, modal entities (ports, rails, terminals), temporal patterns, and causal relationships. Second, a community-validation layer employs consensus mechanisms—such as minimum confirmation thresholds and reputation weighting—to filter and verify experiential knowledge contributed by logistics practitioners before integration. Third, a proactive inference engine performs graph-based pattern matching and temporal reasoning to generate early alerts when emerging community signals match known disruption precursors in the knowledge graph. Expected Contribution: Unlike conventional recommender or reactive monitoring systems, the proposed approach enables preemptive decision support: alerts are issued before a disruption fully materializes, based on community-validated tacit knowledge rather than lagging historical indicators. The system is evaluated using a simulated intermodal network fed with synthetic community reports and real-world incident logs, measuring alert precision, lead time gain, and reduction in unplanned re-routing events. Implications: This work bridges knowledge management, e-communities of practice, and intelligent transport systems. It offers logistics operators, platform designers, and transport authorities a practical pathway to convert distributed practitioner intelligence into proactive, explainable, and network-aware disruption mitigation capabilities.