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Supply chain risks (SCRs) do not work in isolation and have impact both on each member of a chain and the performance of the entire supply chain. The purpose of this paper is to quantitatively assess the impact of dynamic risk propagation within and between integrated firms in global fresh produce supply chains.

A risk propagation ontology-based Bayesian network (BN) model was developed to measure dynamic SCR propagation. The proposed model was applied to a two-tier Australia-China table grape supply chain (ACTGSC) featured with an upstream Australian integrated grower and exporter and a downstream Chinese integrated importer and online retailer.

An ontology-based BN can be generated to accurately represent the risk domain of interest using the knowledge and inference capabilities inherent in a risk propagation ontology. In addition, the analyses revealed that supply discontinuity, product inconsistency and/or delivery delay originating in the upstream firm can propagate to increase the downstream firm’s customer value risk and business performance risk.

The work was conducted in an Australian-China table grape supply chain, so results are only product chain-specific in nature. Additionally, only two state values were considered for all nodes in the model, and finally, while the proposed methodology does provide a large-scale risk network map, it may not be appropriate for a large supply chain network as it only follows the process flow of a single supply chain.

This study supports the backward-looking traceability of risk root causes through the ACTGSC and the forward-looking prediction of risk propagation to key risk performance measures.

The methodology used in this paper provides an evidence-based decision-making capability as part of a system-wide risk management approach and fosters collaborative SCR management, which can yield numerous societal benefits.

The proposed methodology addresses the challenges in using a knowledge-based approach to develop a BN model, particularly with a large-scale model and integrates risk and performance for a holistic risk propagation assessment. The combination of modelling approaches to address the issue is unique.

This study aims to provide an ontology based Baysian network for clinical specialty supporting. As a knowledge base, ontology plays an essential role in domain applications especially in expert systems. Interactive question answering systems are suitable for personal domain consulting and recommended for real-time usage. Clinical specialty supporting for dispatching patients can assist hospitals to locate desired treatment departments for individuals relevant to their syndromes and disease efficiently and effectively. By referring to interactive question answering systems, individuals can understand how to alleviate time and medical resource wasting according to recommendations from medical ontology-based systems.

This work presents an ontology based on clinical specialty supporting using an interactive question answering system to achieve this aim. The ontology incorporates close temporal associations between words in input query to represent word co-occurrence relationships in concept space. The patterns defined in lexicon chain mechanism are further extracted from the query words to infer related concepts for treatment departments to retrieve information.

The precision and recall rates are considered as the criteria for model optimization. Finally, the inference-based interactive question answering system using natural language interface is adopted for clinical specialty supporting, and indicates its superiority in information retrieval over traditional approaches.

From the observed experimental results, we find the proposed method is useful in practice especially in treatment department decision supporting using metrics precision and recall rates. The interactive interface using natural language dialogue attracts the users’ attention and obtains a good score in mean opinion score measure.

This study aims to propose a collaborative knowledge-based ontological research model for designing a collaborative product development process (PDP) while considering different design for X techniques.

This study follows a thematic literature analysis to identify the key design concepts needed to assess environmental, service, safety, manufacture and assembly, supply chain and quality concerns in developing a collaborative PDP.

The proposed model provides a guide for methodology, engineering and ontology evaluation metrics (verification, assessment and validation). The findings benefit both practitioners and managers because they address the key knowledge taxonomy needed to assist them in storing information, promoting teamwork and making decisions in a collaborative PDP while incorporating various design for X approaches and product life cycles.

This study introduces a novel knowledge-based collaborative ontological research model, which is specifically designed to tackle the challenges of developing collaborative products in the contemporary landscape. The model presents a significant and valuable contribution to the field by introducing an ontological approach for acquiring, representing and leveraging knowledge in a computer-interpretable format to support the design of collaborative products. In addition, it provides a comprehensive guide for evaluating the effectiveness and efficacy of the ontology developed.

The supply chain (SC) encompasses all actions related to meeting customer requests and transferring materials upstream to meet those demands. Organisations must operate towards increasing SC efficiency and effectiveness to meet SC objectives. Although most businesses expected the coronavirus disease 2019 (COVID-19) pandemic to severely negatively impact their SCs, they did not know how to model disruptions or their effects on performance in the event of a pandemic, leading to delayed responses, an incomplete understanding of the pandemic's effects and late deployment of recovery measures. Therefore, this study aims to consider the impact of implementing Bayesian network (BN) modelling to measure SC performance in the airline catering context.

This study presents a method for modelling and quantifying SC performance assessment for airline catering. In the COVID-19 context, the researchers proposed a BN model to measure SC performance and risk events and quantify the consequences of pandemic disruptions.

The study simulates and measures the impact of different triggers on SC performance and business continuity using forward and backward propagation analysis, among other BN features, enabling us to combine various SC perspectives and explicitly account for pandemic scenarios.

This study's findings offer a fresh theoretical perspective on the use of BNs in pandemic SC disruption modelling. The findings can be used as a decision-making tool to predict and better understand how pandemics affect SC performance.

Credit risk evaluation is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. Most of the current credit risk methods rely solely on expert knowledge or large amounts of data, which causes some problems like variable interactions hard to be identified, models lack interpretability, etc. To address these issues, the authors propose a new approach.

First, the authors improve interpretive structural model (ISM) to better capture and utilize expert knowledge, then combine expert knowledge with big data and the proposed fuzzy interpretive structural model (FISM) and K2 are used for expert knowledge acquisition and big data learning, respectively. The Bayesian network (BN) obtained is used for forward inference and backward inference. Data from Lending Club demonstrates the effectiveness of the proposed model.

Compared with the mainstream risk evaluation methods, the authors’ approach not only has higher accuracy and better presents the interaction between risk variables but also provide decision-makers with the best possible interventions in advance to avoid defaults in the financial field. The credit risk assessment framework based on the proposed method can serve as an effective tool for relevant policymakers.

The authors propose a novel credit risk evaluation approach, namely FISM-K2. It is a decision support method that can improve the ability of decision makers to predict risks and intervene in advance. As an attempt to combine expert knowledge and big data, the authors’ work enriches the research on financial risk.

The purpose of this paper is to develop and operationalize a process for prioritizing supply chain risks that is capable of capturing the value at risk (VaR), the maximum loss expected at a given confidence level for a specified timeframe associated with risks within a network setting.

The proposed “Worst Expected Best” method is theoretically grounded in the framework of Bayesian Belief Networks (BBNs), which is considered an effective technique for modeling interdependency across uncertain variables. An algorithm is developed to operationalize the proposed method, which is demonstrated using a simulation model.

Point estimate-based methods used for aggregating the network expected loss for a given supply chain risk network are unable to project the realistic risk exposure associated with a supply chain. The proposed method helps in establishing the expected network-wide loss for a given confidence level. The vulnerability and resilience-based risk prioritization schemes for the model considered in this paper have a very weak correlation.

This paper introduces a new “Worst Expected Best” method to the literature on supply chain risk management that helps in assessing the probabilistic network expected VaR for a given supply chain risk network. Further, new risk metrics are proposed to prioritize risks relative to a specific VaR that reflects the decision-maker's risk appetite.

Supply chain risks (SCRs) have uncertainty and interdependency characteristics that must be incorporated into the risk assessment stage of the SCR management framework. This study aims to develop SCR networks and determine the major risk drivers that impact the performance of the sago starch agro-industry (SSA).

The risk and performance variables were collected from the relevant literature and expert consultations. The Bayesian network (BN) approach was used to model the uncertain and interdependent SCRs. A hybrid method was used to develop the BN structure through the expert’s knowledge acquisitions and the learning algorithm application. Sensitivity analyses were performed to examine the significant risk driver and their related paths.

The analyses of model indicated several significant risk drivers that could affect the performance of the SSA. These SCR including both operational and disruption risks across sourcing, processing and delivery stage.

The implementation of the methodology was only applied to the Indonesian small-medium size sago starch agro-industry. The generalization of findings is limited to industry characteristics. The modelled system is restricted to inbound, processing and outbound logistics with the risk perspective from the industry point of view.

The results of this study assist the related actors of the sago starch agro-industry in recognizing the major risk drivers and their related paths in impacting the performance measures.

This study proposes the use of a hybrid method in developing SCR networks. This study found the significant risk drivers that impact the performance of the sago starch agro-industry.

The purpose of this study is to reduce the semantic distance by proposing a model for integrating indexes of textual and visual features via a multi-modality ontology and the use of DBpedia to improve the comprehensiveness of the ontology to enhance semantic retrieval.

A multi-modality ontology-based approach was developed to integrate high-level concepts and low-level features, as well as integrate the ontology base with DBpedia to enrich the knowledge resource. A complete ontology model was also developed to represent the domain of sport news, with image caption keywords and image features. Precision and recall were used as metrics to evaluate the effectiveness of the multi-modality approach, and the outputs were compared with those obtained using a single-modality approach (i.e. textual ontology and visual ontology).

The results based on ten queries show a superior performance of the multi-modality ontology-based IMR system integrated with DBpedia in retrieving correct images in accordance with user queries. The system achieved 100 per cent precision for six of the queries and greater than 80 per cent precision for the other four queries. The text-based system only achieved 100 per cent precision for one query; all other queries yielded precision rates less than 0.500.

This study only focused on BBC Sport News collection in the year 2009.

The paper includes implications for the development of ontology-based retrieval on image collection.

This study demonstrates the strength of using a multi-modality ontology integrated with DBpedia for image retrieval to overcome the deficiencies of text-based and ontology-based systems. The result validates semantic text-based with multi-modality ontology and DBpedia as a useful model to reduce the semantic distance.

A grid information retrieval model has benefits for sharing resources and processing mass information, but cannot handle conceptual heterogeneity without integration of semantic information. The purpose of this research is to propose a concept‐based retrieval mechanism to catch the user's query intentions in a grid environment. This research re‐ranks documents over distributed data sources and evaluates performance based on the user judgment and processing time.

This research uses the ontology lookup service to build the concept set in the ontology and captures the user's query intentions as a means of query expansion for searching. The Globus toolkit is used to implement the grid service. The modification of the collection retrieval inference (CORI) algorithm is used for re‐ranking documents over distributed data sources.

The experiments demonstrate that this proposed approach successfully describes the user's query intentions evaluated by user judgment. For processing time, building a grid information retrieval model is a suitable strategy for the ontology‐based retrieval model.

Most current semantic grid models focus on construction of the semantic grid, and do not consider re‐ranking search results from distributed data sources. The significance of evaluation from the user's viewpoint is also ignored. This research proposes a method that captures the user's query intentions and re‐ranks documents in a grid based on the CORI algorithm. This proposed ontology‐based retrieval mechanism calculates the global relevance score of all documents in a grid and displays those documents with higher relevance to users.

The purpose of this paper is to propose a data-driven scheme for identifying critical project complexity dimensions and establishing the trade-off across multiple project performance criteria.

This paper adopts a hybrid approach using Bayesian Belief Networks (BBNs) and Artificial Neural Networks (ANNs). The output of the ANN model is used as input to the BBN model for prioritizing project complexity dimensions relative to multiple project performance criteria. The proposed process is demonstrated through a real application in the construction industry.

With a number of nonlinear interactions involved within and across project complexity and performance, it is not feasible to model and assess the strength of these interactions using conventional techniques. The proposed process helps in effectively mapping a “multidimensional complexity” space to a “multidimensional performance” space and makes use of data from past projects for operationalizing this mapping scheme by means of ANNs. This obviates the need for developing a parametric model that is both challenging and computationally cumbersome. The mapping function can be used for generating all possible scenarios required for the development of a data-driven BBN model.

This paper introduces a data-driven process for operationalizing the mapping of project complexity to project performance within a network setting of interacting complexity dimensions and performance criteria. The results of the application study manifest the importance of capturing the interdependency across project complexity and performance. Ignoring the underlying interdependencies and relying exclusively on conventional correlation-based techniques may lead to making suboptimal decisions.