This work explores the integration of ontology-based reasoning and Machine Learning techniques for explainable classification in the domain of moral and cultural values. By relying on an ontological formalization of moral values as in the Basic Human Values theory, which is based on the Description and Situation Ontology Design Pattern, the sandra neuro-symbolic reasoner is used to infer values (formalized as descriptions) that are satisfied by a certain sentence. Sentences, alongside their structured representation, are automatically generated using an open-source Large Language Model. The inferred descriptions are used to automatically detect the value associated with a sentence. We show that only relying on the reasoner’s inference results in explainable classification comparable to other more complex approaches. Moreover, we test how the LLMs tacit knowledge can be exploited to obtain novel formalizations of the domain. We show that combining the reasoner’s inferences with simple distributional semantics methods largely outperforms all the baselines, including complex models based on neural network architectures.