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

尽管受机器学习（ML）方法的推动，人工智能（AI）系统的流行和最新进展，但大多数现有模型都无法解释其推理过程，即，为某项人工智能任务背后的推理提供类似人类的理由。这种“可解释”的功能是在现实世界场景中采用和吸收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断。人工智能在具有高度社会和经济影响的环境中的应用（如在医疗保健和法律的设置）将需要黑盒人工智能模型的发展方向，系统可以证明，解释和对话与他们的最终用户关于底层的推理过程，提供透明的人类可解释的输出。其目标是支持构建复杂的人工智能系统，以解决诸如提问和文本蕴涵等任务，这些任务除了预期的输出外，还可以输出有意义的类人解释。本项目主要针对以下几个问题进行研究：1.知识图（定义的、基于事实的、话语级的）能否与微分（神经）归纳逻辑编程（神经符号方法）结合使用，以支持可解释的机器学习？2.哪一组量化指标可以用来评估可解释的机器学习系统？新的工程内容该项目将首次阐明知识图之间的联系，知识图代表大规模背景知识和差分归纳逻辑编程。EPSRC研究领域自然语言处理，机器学习