The field of relational learning and ontology is experiencing significant developments, with a focus on improving the representation and reasoning of complex knowledge. Researchers are working to overcome the limitations of current methods, such as the lack of effective visualization tools and the inability to model entities and relations accurately. New approaches, including the use of monadic second-order logic and language models, are being explored to enhance the expressiveness and scalability of ontological classification and learning. These innovations have the potential to improve the performance of deep learning models and enable more effective knowledge representation and reasoning. Noteworthy papers include: OntView, which introduces a novel ontology viewer that provides an intuitive visual representation of ontology concepts and their formal definitions. ChemLog, which presents an approach that allows the use of monadic second-order formalisations for ontology classification, and Language Models as Ontology Encoders, which proposes a new ontology embedding method that effectively incorporates textual labels and preserves logical structure.