What is it about?
Freight transport involves many organisations, including transport companies, warehouses and customers. Each organisation manages its own information, often using different systems and terminology. As a result, even when records describe the same delivery, combining and interpreting them can be difficult. My research explores how knowledge graphs can help connect this fragmented information without requiring organisations to replace their existing systems. A knowledge graph represents information through meaningful connections between objects, activities and events. The research goes beyond connecting records. It also investigates how to check whether an answer obtained from different sources is supported by appropriate evidence, where that evidence comes from, and whether the relevant conditions for its use are satisfied. This paper introduces the proposed approach and a plan to evaluate it through a freight transport scenario.
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Why is it important?
Sharing data does not necessarily mean that organisations understand or interpret it in the same way. For example, a delivery time recorded by one organisation might refer to the vehicle's arrival, while another might record the completion of unloading. Confusing these events can lead to incorrect conclusions, even when the original records are accurate. This research addresses the problem of making information understandable and usable across organisational boundaries. Its distinctive focus is not only on finding answers from connected records, but also on examining the evidence and conditions supporting those answers. If successful, the approach could contribute to more transparent and reliable information sharing between logistics stakeholders, supporting better coordination and potentially enabling more efficient and sustainable freight operations.
Perspectives
One of the questions guiding my research is whether exchanging data between different organisations is enough to make that information useful. I don't think it is. Even when data is accessible, its meaning depends on where it comes from, what it describes, and the conditions under which it can be interpreted and used. This is what I'm trying to address in my PhD. I'm exploring how knowledge graphs can help connect information from different systems while preserving its meaning and context. The aim is not only to retrieve answers across organisations, but also to understand the evidence supporting those answers and check whether the relevant conditions are satisfied. This paper presents the approach I'm developing and how I plan to evaluate it in a freight transport scenario.
Mohammed Younes
Institut Polytechnique de Paris
Read the Original
This page is a summary of: A Knowledge Graph-Based Framework for Interoperable Supply-Chain Data Ecosystems, November 2026, ACM (Association for Computing Machinery),
DOI: 10.1145/3799682.3840235.
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