Knowledge injection of Datalog rules via Neural Network Structuring with KINS

IF 0.7 4区数学 Q3 COMPUTER SCIENCE, THEORY & METHODS

Journal of Logic and Computation Pub Date : 2023-06-08 DOI:10.1093/logcom/exad037

Matteo Magnini, Giovanni Ciatto, Andrea Omicini

引用次数: 0

Abstract

Abstract We propose a novel method to inject symbolic knowledge in form of Datalog formulæ into neural networks (NN), called Knowledge Injection via Network Structuring (KINS). The idea behind our method is to extend NN internal structure with ad-hoc layers built out of the injected symbolic knowledge. KINS does not constrain NN to any specific architecture, neither requires logic formulæ to be ground. Moreover, it is robust w.r.t. both lack of data and imperfect/incomplete knowledge. Experiments are reported, involving multiple datasets and predictor types, to demonstrate how KINS can significantly improve the predictive performance of the neural networks it is applied to.

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基于KINS的神经网络结构化数据规则的知识注入

摘要提出了一种将符号知识以Datalog公式的形式注入神经网络的新方法——通过网络结构注入知识(knowledge Injection via Network Structuring, KINS)。我们的方法背后的思想是用注入的符号知识构建的特别层来扩展神经网络的内部结构。KINS不将神经网络约束到任何特定的架构，也不要求逻辑公式是接地的。此外，它在缺乏数据和不完善/不完整知识的情况下是稳健的。报告了涉及多个数据集和预测器类型的实验，以证明KINS如何显着提高其应用的神经网络的预测性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Logic and Computation 工程技术-计算机：理论方法

CiteScore

1.90

自引率

14.30%

发文量

审稿时长

6-12 weeks

期刊介绍： Logic has found application in virtually all aspects of Information Technology, from software engineering and hardware to programming and artificial intelligence. Indeed, logic, artificial intelligence and theoretical computing are influencing each other to the extent that a new interdisciplinary area of Logic and Computation is emerging. The Journal of Logic and Computation aims to promote the growth of logic and computing, including, among others, the following areas of interest: Logical Systems, such as classical and non-classical logic, constructive logic, categorical logic, modal logic, type theory, feasible maths.... Logical issues in logic programming, knowledge-based systems and automated reasoning; logical issues in knowledge representation, such as non-monotonic reasoning and systems of knowledge and belief; logics and semantics of programming; specification and verification of programs and systems; applications of logic in hardware and VLSI, natural language, concurrent computation, planning, and databases. The bulk of the content is technical scientific papers, although letters, reviews, and discussions, as well as relevant conference reviews, are included.