Sandra -- A Neuro-Symbolic Reasoner Based On Descriptions And Situations
This addresses the challenge of integrating neural networks with symbolic knowledge for improved reasoning in AI, though it appears incremental as it builds on existing ontology patterns.
The paper tackles the problem of neuro-symbolic reasoning by introducing Sandra, a reasoner that combines vectorial representations with deductive reasoning using an ontology, and it outperforms baselines on standard benchmarks while providing interpretability and control.
This paper presents sandra, a neuro-symbolic reasoner combining vectorial representations with deductive reasoning. Sandra builds a vector space constrained by an ontology and performs reasoning over it. The geometric nature of the reasoner allows its combination with neural networks, bridging the gap with symbolic knowledge representations. Sandra is based on the Description and Situation (DnS) ontology design pattern, a formalization of frame semantics. Given a set of facts (a situation) it allows to infer all possible perspectives (descriptions) that can provide a plausible interpretation for it, even in presence of incomplete information. We prove that our method is correct with respect to the DnS model. We experiment with two different tasks and their standard benchmarks, demonstrating that, without increasing complexity, sandra (i) outperforms all the baselines (ii) provides interpretability in the classification process, and (iii) allows control over the vector space, which is designed a priori.