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Analogous to human information processing that integrates more rational, slow reasoning and more holistic and intuitive, faster information processing, neural-symbolic learning approaches integrate logical reasoning with neural networks. In this way, expert background knowledge (in our group, mainly ontologies and their logical axioms) can be used by neural networks in order learn both from data and axioms, thus improving performance while simultaneously reducing the amount of data that is needed for training.

While in many neural-symbolic approaches, logical and neural modules cooperate, we focus on tightly integrated approaches like logical neural networks and semantic loss approaches, because these are most promising for a new generation of artificial intelligence systems.