A Neuro-Symbolic Explainable Machine Learning Model Using Knowledge Graphs

使用知识图的神经符号可解释机器学习模型

• 批准号: 2112481
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2112481
• 关键词: Neuro; Symbolic; Explainable; Machine; Learning

MotivationDespite the popularity and recent advances in Artificial Intelligence (AI) systems boosted by machine learning (ML) methods, most of the existing models fall short on their ability to explain their reasoning process, i.e., in providing human-like justifications for the reasoning behind a certain AI task. This functionality of "being interpretable" is a fundamental requirement for the adoption and uptake of AI systems in real-world scenarios, as users need to trust and understand the approximations and inferences done by the system. The application of AI in contexts with high social and economic impact (as in health care and legal settings) will require the evolution of black-box AI models in the direction of systems which can justify, explain and dialogue with their end-users about the underlying reasoning process, providing transparent human-interpretable outputs.ApproachThis project aims at developing a neuro-symbolic explainable Machine Learning model using Knowledge graphs. The goal is to support the construction of complex AI systems for addressing tasks such as Question Answering and Text Entailment, which can output meaningful human-like explanations in addition to the expected output. Research QuestionsThe project targets the following research questions:1. Can knowledge graphs (definitional, fact-based, discourse-level) be used in conjunction to differential(neuro) inductive logic programming (the neuro-symbolic approach) to support explainable machine learning?2. Which set of quantitative measures can be used to evaluate explainable machine learning systems? Novel Engineering ContentThe project will articulate for the first-time the connection between knowledge graphs, which represents large-scale background knowledge and differential inductive logic programming.EPSRC Research AreasNatural Language Processing, Machine Learning

动机尽管机器学习（ML）方法推动了人工智能（AI）系统的普及和最新进展，但大多数现有模型都缺乏解释其推理过程的能力，即为背后的推理提供类似人类的理由某个人工智能任务。这种“可解释”的功能是在现实场景中采用和采用人工智能系统的基本要求，因为用户需要信任和理解系统所做的近似和推理。人工智能在具有较高社会和经济影响的环境中（如医疗保健和法律环境）的应用将需要黑盒人工智能模型朝着能够证明、解释并与最终用户对话的系统方向发展。底层推理过程，提供透明的人类可解释的输出。方法该项目旨在使用知识图开发一种神经符号可解释的机器学习模型。目标是支持构建复杂的人工智能系统来解决诸如问答和文本蕴涵等任务，除了预期的输出之外，还可以输出有意义的类人解释。研究问题本项目针对以下研究问题： 1．知识图（定义的、基于事实的、话语层面的）能否与微分（神经）归纳逻辑编程（神经符号方法）结合使用来支持可解释的机器学习？2。哪组定量测量可用于评估可解释的机器学习系统？新颖的工程内容该项目将首次阐明代表大规模背景知识的知识图与微分归纳逻辑编程之间的联系。 EPSRC 研究领域自然语言处理、机器学习