In this work, we present an algorithmic framework that supports a decision process in which an end user is assisted by a domain expert to solve a problem. In addition, the communication between the end user and the domain expert is characterized by a limited number of questions and answers. The framework we have developed helps the domain expert to pinpoint a small number of questions to the end user to increase the likelihood of their insights being correct. The proposed framework is based on the domain expert’s knowledge and includes an interaction with both the domain expert and the end user. The domain expert’s knowledge is represented by a knowledge graph, and the end user’s information related to the problem is entered into the graph as evidence. This triggers the inference algorithm in the graph, which suggests to the domain expert the next question for the end user. The paper presents a detailed proposed framework in a medical diagnostic domain; however, it can be adapted to additional domains with a similar setup. The software framework we have developed makes the decision-making process accessible in an interactive and explainable manner, which includes the use of semantic technology and is, therefore, innovative.

In the dynamic landscape of healthcare, decision support systems (DSS) confront continuous challenges, especially in the era of big data. Background: This study extends a Q&A-based medical DSS framework that utilizes semantic technologies for disease inference based on a patient’s symptoms. The framework inputs “evidential symptoms” (symptoms experienced by the patient) and outputs a ranked list of hypotheses, comprising an ordered pair of a disease and a characteristic symptom. Our focus is on advancing the framework by introducing ontology integration to semantically enrich its knowledgebase and refine its outcomes, offering three key advantages: Propagation, Hierarchy, and Range Expansion of symptoms. Additionally, we assessed the performance of the fully implemented framework in Python. During the evaluation, we inspected the framework’s ability to infer the patient’s disease from a subset of reported symptoms and evaluated its effectiveness in ranking it prominently among hypothesized diseases. Methods: We conducted the expansion using dedicated algorithms. For the evaluation process, we defined various metrics and applied them across our knowledge base, encompassing 410 patient records and 41 different diseases. Results: We presented the outcomes of the expansion on a toy problem, highlighting the three expansion advantages. Furthermore, the evaluation process yielded promising results: With a third of patient symptoms as evidence, the framework successfully identified the disease in 94% of cases, achieving a top-ranking accuracy of 73%. Conclusions: These results underscore the robust capabilities of the framework, and the enrichment enhances the efficiency of medical experts, enabling them to provide more precise and informed diagnostics.

Advances in the field of machine learning (ML) have been reflected in the intensity of research studies exploiting these techniques for a better understanding of existing phenomena, and for predicting future ones, as a mean for promoting a more efficient and sustainable transportation system. The present study aims to understand the trends of utilizing diverse ML approaches to tackle issues within sub-domains of transportation and to identify underutilized potentials among them. This paper presents a methodology for the bi-dimensional classification of a large corpus of scientific articles. The articles are classified into six transport-related sub-domains, based on the definition of the Israeli Smart Transport Research Center, whose aim is a transportation system with zero externalities, and the ML techniques used in each of them is identified. A fuzzy KNN model is implemented for the multi-classification of articles into the transportation sub-domains and an ontology-based reasoning for identifying the share of each applied ML approach is employed. The application of these methodologies to a corpus of 1718 articles revealed, among other findings, an increasing share of artificial neural networks and deep learning techniques from 2018 until 2022, particularly in the traffic management sub-domain. A significant contribution of the development of these automatic methodologies is the ability to reuse them for ongoing exploration of trends regarding the use of ML techniques for transportation sub-domains.

Healthcare 4.0 addresses modernization and digital transformation challenges, such as home-based care and precision treatments, by leveraging advanced technologies to enhance accessibility and efficiency. Semantic technologies, particularly knowledge graphs (KGs), have proven instrumental in representing interconnected medical data and improving clinical decision-support systems. We previously introduced a semantic framework to assist medical experts during patient interactions. Operating iteratively, the framework prompts medical experts with relevant questions based on patient input, progressing toward accurate diagnoses in time-constrained settings. It comprises two components: (a) a KG representing symptoms, diseases, and their relationships, and (b) algorithms that generate questions and prioritize hypotheses—a ranked list of symptom–disease pairs. An earlier extension enriched the KG with a symptom ontology, incorporating hierarchical structures and inheritance relationships to improve accuracy and question-generation capabilities. This paper further extends the framework by introducing strategies tailored to specific medical domains. Strategies integrate domain-specific knowledge and algorithms, refining decision making while maintaining the iterative nature of expert–patient interactions. We demonstrate this approach using an emergency medicine case study, focusing on life-threatening conditions. The KG is enriched with attributes tailored to emergency contexts and supported by dedicated algorithms. Boolean rules attached to graph edges evaluate to TRUE or FALSE at runtime based on patient-specific data. These enhancements optimize decision making by embedding domain-specific goal-oriented knowledge and inference processes, providing a scalable and adaptable solution for diverse medical contexts.