Chronic hepatitis B virus (HBV) infection is still a global health problem, with over 296 million
chronically HBV-infected individuals worldwide. The merging data about clinical parameters …

Knowledge graphs (KGs) naturally capture the convergence of data and knowledge, making
them expressive frameworks for describing and integrating heterogeneous data in a …

Abstract Knowledge Graphs (KGs) are graph-based structures that integrate heterogeneous
data, capture domain knowledge, and enable explainable AI through symbolic reasoning …

Artificial Intelligence (AI) systems can introduce biases that lead to unreliable outcomes and,
in the worst-case scenarios, perpetuate systemic and discriminatory results when deployed …

Neural (aka subsymbolic) AI methods, in particular, those based on deep learning, recently
achieved great successes in various application domains, eg,[10, 19]. However, they are …

Abstract Knowledge graphs (KGs) are naturally capable of capturing the convergence of
data and knowledge, thereby making them highly expressive frameworks for describing and …

Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent
decision-making and a wide range of Artificial Intelligence (AI) services across major …

Explainability is an essential reason limiting the application of neural networks in many vital
fields. Although neuro-symbolic AI hopes to enhance the overall explainability by leveraging …

Neuro-symbolic AI is an effective method for improving the overall performance of AI models
by combining the advantages of neural networks and symbolic learning. However, there are …

Abstract Machine learning (ML) is becoming increasingly important in healthcare decision-
making, requiring highly interpretable insights from predictive models. Although integrating …