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Driven by economic globalisation, various industries have developed a trend towards high specialisation and vertical division of labor, resulting in vast and intricate supply chain networks. However, unforeseen disasters can cause supply chain disruptions, subsequently impacting the regular production and operations of both upstream and downstream enterprises. To tackle this challenge, this study utilises Graph Neural Networks (GNNs) to synthesise graph structural data within the supply chain network, aiming to identify alternative suppliers to mitigate the impact of disruptions. We construct a large-scale knowledge graph to represent the realistic automotive supply chain network in China. Additionally, we propose a GNN-based framework that utilises information about interactions between buyers and suppliers to recommend alternative suppliers from the knowledge graph. Experimental results show that our approach significantly outperforms state-of-the-art GNN-based models, including Light-GCN and NGCF. Our research provides an intelligent and efficient perspective on supplier selection for the Chinese automobile industry. [ABSTRACT FROM AUTHOR]

The deployment of the Industrial Internet of Things (IIoT) in smart container terminals provides a foundation for sensing and recording all operational processes. However, little effort has been devoted to integrating the massive data regarding interoperability challenges, thus limiting the value of data in advancing the intelligent evolution of ports. In this research, we propose a graph-based approach to organise operational records semantically, thereby facilitating data-driven decision-making in container terminals. We first construct a knowledge graph for operational processes in container terminals, employing a tailored procedure for the automatic conversion of operational records into triples. By utilising the graph information, we propose a novel method that integrates reinforcement learning (RL) with a mathematical solver for optimising scheduling problems. The quay crane scheduling problem (QCSP) is illustrated as an example to elaborate on the technical details. Based on a dataset from a real-world container terminal, numerical studies demonstrate the superiority of the proposed framework in terms of information retrieval efficiency and solution quality compared with the traditional data organisation approach. [ABSTRACT FROM AUTHOR]

Modern supply chains are complex, interconnected systems that contain emergent, invisible dependencies. Lack of visibility often hinders effective risk planning and results in delayed discovery of supply chain problems, with examples ranging from product contamination, unsustainable production practices, or exposure to suppliers clustered in geographical areas prone to natural or man-made disasters. Initiatives that rely on manual collection of data often fail due to supply chain complexity and unwillingness of suppliers to share data. In this paper, we propose a neurosymbolic machine learning technique to proactively uncover hidden risks in supply chains and discover new information. Our method uses a combination of graph neural networks and knowledge graph reasoning. Unlike existing research our model is able to infer multiple types of hidden relationship risks, presenting a step change in automated supply chain surveillance. The approach has been tested on two empirical datasets from the automotive and energy industries, illustrating that it can provide inference in multiple types of links such as companies, products, production capabilities, certifications; thereby facilitating complex queries that go beyond who-supplies-whom. As such, additional risk insights can emerge from graph structure, providing practitioners with new knowledge. [ABSTRACT FROM AUTHOR]

The increasing complexity of industrial systems demands more effective and intelligent maintenance approaches to address manufacturing defects arising from faults in multiple asset modules. Traditional digital twin (DT) systems, however, face limitations in interoperability, knowledge sharing, and causal inference. As such, cognitive digital twins (CDTs) can add value by managing a collaborative web of interconnected systems, facilitating advanced cross-domain analysis and dynamic context considerations. This paper introduces a CDT system that leverages industrial knowledge graphs (iKGs) to support maintenance planning and operations. By employing a design structure matrix (DSM) to model dependencies and relationships, a semantic translation approach maps the knowledge into a graph-based representation for reasoning and analysis. An automatic solution generation mechanism, utilising graph sequencing with Louvain and PageRank algorithms, derives feasible solutions, which can be validated via simulation to minimise production disruption impacts. The CDT system can also identify potential disruptions in new product designs, thus enabling preventive actions to be taken. A case study featuring a print production manufacturing line illustrates the CDT system's capabilities in causal inference and solution explainability. The study concludes with a discussion of limitations and future directions, providing valuable guidelines for manufacturers aiming to enhance reactive and predictive maintenance strategies. [ABSTRACT FROM AUTHOR]

Micro, Small, and Medium-sized Enterprises (MSMEs) are essential for the growth and development of the country's economy, as they create jobs, generate income, and foster production and innovation. In recent years, credit risk assessment (CRA) has been an essential process used by financial institutions to evaluate the creditworthiness of MSMEs and determine the likelihood of default. Traditionally, CRA has relied on credit scores and financial statements, but with the advent of machine learning (ML) algorithms, lenders have a new tool at their disposal. By and large, ML algorithms are designed to classify borrowers based on their credit history and transactional data while leveraging the entity relationship involved in credit transactions. This study introduces an innovative knowledge graph-driven credit risk assessment model (RGCN-RF) based on the Relational Graph Convolutional Network (RGCN) and Random Forest (RF) algorithm. RGCN is employed to identify topological structures and relationships, which is currently nascent in traditional credit risk assessment methods. RF categorises MSMEs based on the enterprise embedding vector generated from RGCN. Extensive experimentation is conducted to assess model performance utilising the Indian MSMEs database. The balanced accuracy of 92% obtained using the RGCN-RF model demonstrates a considerable advancement over prior techniques in identifying risk-free enterprises. [ABSTRACT FROM AUTHOR]

BackgroundOntologies are fundamental components of informatics infrastructure in domains such as biomedical, environmental, and food sciences, representing consensus knowledge in an accurate and computable form. However, their construction and maintenance demand substantial resources and necessitate substantial collaboration between domain experts, curators, and ontology experts. We present Dynamic Retrieval Augmented Generation of Ontologies using AI (DRAGON-AI), an ontology generation method employing Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). DRAGON-AI can generate textual and logical ontology components, drawing from existing knowledge in multiple ontologies and unstructured text sources.ResultsWe assessed performance of DRAGON-AI on de novo term construction across ten diverse ontologies, making use of extensive manual evaluation of results. Our method has high precision for relationship generation, but has slightly lower precision than from logic-based reasoning. Our method is also able to generate definitions deemed acceptable by expert evaluators, but these scored worse than human-authored definitions. Notably, evaluators with the highest level of confidence in a domain were better able to discern flaws in AI-generated definitions. We also demonstrated the ability of DRAGON-AI to incorporate natural language instructions in the form of GitHub issues.ConclusionsThese findings suggest DRAGON-AI's potential to substantially aid the manual ontology construction process. However, our results also underscore the importance of having expert curators and ontology editors drive the ontology generation process.

Feature selection in Knowledge Graphs (KGs) is increasingly utilized in diverse domains, including biomedical research, Natural Language Processing (NLP), and personalized recommendation systems. This paper delves into the methodologies for feature selection (FS) within KGs, emphasizing their roles in enhancing machine learning (ML) model efficacy, hypothesis generation, and interpretability. Through this comprehensive review, we aim to catalyze further innovation in FS for KGs, paving the way for more insightful, efficient, and interpretable analytical models across various domains. Our exploration reveals the critical importance of scalability, accuracy, and interpretability in FS techniques, advocating for the integration of domain knowledge to refine the selection process. We highlight the burgeoning potential of multi-objective optimization and interdisciplinary collaboration in advancing KG FS, underscoring the transformative impact of such methodologies on precision medicine, among other fields. The paper concludes by charting future directions, including the development of scalable, dynamic FS algorithms and the integration of explainable AI principles to foster transparency and trust in KG-driven models.