Summary of Sandra — a Neuro-symbolic Reasoner Based on Descriptions and Situations, by Nicolas Lazzari et al.
Sandra – A Neuro-Symbolic Reasoner Based On Descriptions And Situations
by Nicolas Lazzari, Stefano De Giorgis, Aldo Gangemi, Valentina Presutti
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Sandra is a neuro-symbolic reasoner that combines vectorial representations with deductive reasoning. It builds a vector space constrained by an ontology and performs reasoning over it, allowing it to be combined with neural networks and bridging the gap between symbolic knowledge representations. The paper presents Sandra as a model-based approach that infers all possible perspectives (descriptions) from a given set of facts (situation), even in the presence of incomplete information. It is based on the Description and Situation (DnS) ontology design pattern, a formalization of frame semantics. The authors prove that their method is correct with respect to the DnS model and experimentally demonstrate its effectiveness on two different tasks and their standard benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Sandra is a new way for computers to reason and understand the world. It combines ideas from artificial intelligence and computer science to make decisions based on rules and patterns, like humans do. The paper describes how Sandra works and shows that it can be very good at certain tasks, even better than other methods. This is important because it means we can use Sandra to help us make sense of incomplete or uncertain information. |
Keywords
» Artificial intelligence » Semantics » Vector space
