Neural logical reasoning (NLR) is a fundamental task in knowledge discovery and artificial intelligence. NLR aims at answering multi-hop queries with logical operations on structured knowledge bases based on distributed representations of queries and answers. While previous neural logical reasoners can give specific entity-level answers, i.e., perform inductive reasoning from the perspective of logic theory, they are not able to provide descriptive concept-level answers, i.e., perform abductive reasoning, where each concept is a summary of a set of entities. In particular, the abductive reasoning task attempts to infer the explanations of each query with descriptive concepts, which make answers comprehensible to users and is of great usefulness in the field of applied ontology. In this work, we formulate the problem of the joint abductive and inductive neural logical reasoning (AI-NLR), solving which needs to address challenges in incorporating, representing, and operating on concepts. We propose an original solution named ABIN for AI-NLR. Firstly, we incorporate description logic-based ontological axioms to provide the source of concepts. Then, we represent concepts and queries as fuzzy sets, i.e., sets whose elements have degrees of membership, to bridge concepts and queries with entities. Moreover, we design operators involving concepts on top of the fuzzy set representation of concepts and queries for optimization and inference. Extensive experimental results on two real-world datasets demonstrate the effectiveness of ABIN for AI-NLR.
翻译:神经逻辑逻辑推理(NLR)是知识发现和人工智能的一项基本任务。NLR的目的是在基于分散的询问和答案的表述基础上,以结构化的知识基础,用逻辑操作回答多呼询问和多呼询问;虽然以前的神经逻辑推理师可以给出具体的实体层面的答案,即从逻辑理论的角度进行感性推理,但他们无法提供描述性概念层面的答案,即进行绑架性推理,其中每个概念都是一套实体的概要。特别是,在模拟推理任务中试图用描述性概念来推断每项查询的解释,使用户能够理解答案,在应用本体学领域非常有用。在这项工作中,我们提出联合的绑架和感性神经逻辑推理(AI-NLRR)问题,解决在整合、代表概念和概念操作方面所面临的挑战。我们为AI-NLRRR提出一个原解决方案。首先,我们纳入了基于逻辑的逻辑推理学理论的顺序描述,以提供概念的源。然后,我们把概念和查询概念和疑问作为模糊性的概念和感错概念,包括设计师的顶级概念。