Neuro-symbolic integration is a current field of investigation in which symbolic approaches are combined with deep learning ones. In this work we start from simple non-relational knowledge that can be extracted from text by considering the co-occurrence of entities inside textual corpora; we show that we can easily integrate this knowledge with Logic Tensor Networks (LTNs), a neuro-symbolic model. Using LTNs it is possible to integrate axioms and facts with commonsense knowledge represented in a sub-symbolic form in one single model performing well in reasoning tasks. In spite of some current limitations, we show that results are promising.
Bianchi, F., Palmonari, M., Hitzler, P., Serafini, L. (2019). Complementing Logical Reasoning with Sub-symbolic Commonsense. Intervento presentato a: RuleML+RR - 3rd International Joint Conference on Rules and Reasoning, Bolzano, Italia [10.1007/978-3-030-31095-0_11].
Complementing Logical Reasoning with Sub-symbolic Commonsense
Bianchi, Federico;Palmonari, Matteo;
2019
Abstract
Neuro-symbolic integration is a current field of investigation in which symbolic approaches are combined with deep learning ones. In this work we start from simple non-relational knowledge that can be extracted from text by considering the co-occurrence of entities inside textual corpora; we show that we can easily integrate this knowledge with Logic Tensor Networks (LTNs), a neuro-symbolic model. Using LTNs it is possible to integrate axioms and facts with commonsense knowledge represented in a sub-symbolic form in one single model performing well in reasoning tasks. In spite of some current limitations, we show that results are promising.
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