The review “Symbolic Approaches for Explainable Artificial Intelligence” discusses the potential of symbolic AI to improve transparency, contrasting it with opaque deep learning systems. Though connectionist models perform well, their poor interpretability means that they are of concern for bias and trust in high-stakes fields such as healthcare and finance. The authors integrate symbolic AI methods—rule-based reasoning, ontologies, and expert systems—with neuro-symbolic integrations (e.g., DeepProbLog). This paper covers topics such as scalability and integrating knowledge, proposing solutions like dynamic ontologies. The survey concludes by advocating for hybrid AI approaches and interdisciplinary collaboration to reconcile technical innovation with ethical and regulatory demands.

Traditional relational databases have been widely used for many years and have been the go-to choice for the industry. However, the emergence of NoSQL databases coincided with the increasing popularity of the Internet, social networks, and cloud computing. Data migration has become a crucial topic, due to the rapid growth of applications and the ever-expanding amount of data being collected. This has led to a shift from relational database management systems (RDBMS) to NoSQL databases, also known as not only SQL. This article compares the characteristics of both database architectures, SQL and NoSQL, focusing on aspects such as scalability and performance, flexibility, and security. Additionally, the role of AI in streamlining the migration process will be explored.