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.

这篇论文介绍了一种名为 sandra 的神经符号推理器，它将向量表示与演绎推理相结合。Sandra 利用本体论建立了一个向量空间，并对其进行推理。该推理器的几何性质使其能够与神经网络结合，弥合了符号知识表示与神经网络之间的差距。通过描述和情境 (DnS) 本体设计模式，Sandra 可以推断出所有可能的角度 (描述)，以合理解释给定的事实 (情境)，即使信息不完整。我们证明了我们的方法与 DnS 模型是正确的。我们在两个不同的任务和它们的标准基准上进行实验，证明 sandra 在不增加复杂性的情况下 (i) 胜过了所有基线 (ii) 在分类过程中提供了可解释性，以及 (iii) 可以控制预先设计好的向量空间。

Sandra—— 基于描述和情境的神经符号推理器

Sandra -- A Neuro-Symbolic Reasoner Based On Descriptions And Situations

Knowledge base question answering (KBQA)is an important task in Natural
Language Processing. Existing approaches face significant challenges including
complex question understanding, necessity for reasoning, and lack of large
end-to-end training datasets. In this work, we propose Neuro-Symbolic Question
Answering (NSQA), a modular KBQA system, that leverages (1) Abstract Meaning
Representation (AMR) parses for task-independent question understanding; (2) a
simple yet effective graph transformation approach to convert AMR parses into
candidate logical queries that are aligned to the KB; (3) a pipeline-based
approach which integrates multiple, reusable modules that are trained
specifically for their individual tasks (semantic parser, entity
andrelationship linkers, and neuro-symbolic reasoner) and do not require
end-to-end training data. NSQA achieves state-of-the-art performance on two
prominent KBQA datasets based on DBpedia (QALD-9 and LC-QuAD1.0). Furthermore,
our analysis emphasizes that AMR is a powerful tool for KBQA systems.

本文提出了一种模块化的知识库问答系统 Neuro-Symbolic Question Answering (NSQA)，它采用 Abstract Meaning Representation (AMR) parse 语义分析、简单有效的图转换方法和多个可重复使用训练模块（语义分析、实体和关系链接和神经符号推理器），避免需要端到端的训练数据，并在两个著名知识库问答数据集上（基于 DBpedia 的 QALD-9 和 LC-QuAD1.0）实现了最先进的性能，并且 AMR 是 KBQA 系统的强大工具。