AI and Cognitive Computing for Reasoning about Big Data and Knowledge Graphs with Application to the Oil and Gas Industry

用于推理大数据和知识图的人工智能和认知计算及其在石油和天然气行业的应用

• 批准号: 2370505
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2370505
• 关键词: AI; Cognitive; Computing; Reasoning; about

Brief Description: The broad aim of this doctoral project is to gain, in cooperation with BP, a deep understanding of how the latest AI and cognitive computing technologies can be used for reasoning over big data. One of the application of this project is in the oil and gas industry - supporting and improving core business processes and decision making in this sector. Towards this aim, the significant contributions of the student are planned to be the development of a rule learner and reasoner for big data, specific designed of the learner for existential rules, and systematic evaluation its impact on applications important to BP.To the mutual benefit of the student, BP, and Oxford University, and to maximise synergies, the studentship will be attached to the VADA "Value Added Data Systems" project which - as one of its goals - aims to develop a general-purpose reasoning system, building on the experience with the Datalog family of languages.Alignment to EPSRC's Strategies and Research Areas: This project falls within the EPSRC Information and communication technologies (ICT) theme, and the following research areas: Artificial intelligence technologies, Databases, and Information systems.Novelty and Research Methodology: Declarative rules such as Prolog and Datalog rules are common formalisms to express expert knowledge and are used in a number of systems. Since developing such rules is time-consuming and requires scarce expert knowledge, it is essential to develop algorithms for learning such rules. This project addresses the problem of learning existential rules, which found applications in many uses cases such as Knowledge Graphs, the Semantic Web and Web Data Extraction. In particular, we concentrate on developing evolutionary learning algorithms for existential rules. We define the rule learning setting and review the main approaches to learning rules, such as top-down, bottom-up, and neural methods. We review existing evolutionary approaches to rule learning, discuss different genetic encoding schema, initial population creation methods, evolution operators, and evaluation fitness functions. In addition, from a wider view, we explore four interaction models between logical reasoning engines and Machine Learning approaches. Last but not least, we outline the answers to the proposed research questions for existential rule learning with promising experimental results, and exhibit applications in crude corrosivity, knowledge graph rule mining and question answering data sets. This project focuses on studying Evolutionary Algorithms (EA, also known as Genetic Algorithm, GA) for the Inductive Logic Programming (ILP) problem. EAs are a family of biology-inspired search algorithms that optimize for the most promising preliminary solutions, while exploring a wide search space at the same time. In particular, in our setting, atoms, partial or parameterized rules can be treated as chromosomes, from which the new population of chromosomes can be derived via the operations of mutation, crossover and selection. While performing these operations, a quality measure, called fitness function is computed to judge whether an obtained new generation of chromosomes is fit for continuing the search. Such evolutionary algorithms typically do not perform exhaustive search and at the same time are less likely to fall into local optima. In addition, they are flexible in that they might not require imposed template on the shape of rules, as it is typically the case in other approaches to ILP. Companies and Collaborators Involved: This is an EPSRC Industrial CASE studentship project in collaboration with BP.

简要说明：该博士学位项目的广泛目的是与BP合作，深入了解最新的AI和认知计算技术如何使用大数据推理。该项目的应用之一是在石油和天然气行业中 - 支持和改善该领域的核心业务流程和决策。为了实现这一目标，计划学生的重大贡献是制定规则学习者和大数据的推理者，对学习规则的学习者的特定设计，并系统地评估其对BP的应用的影响。在学生，BP和牛津大学中，为了最大程度地提高协同作用，该学生将附加到VADA“增值数据系统”项目上，该项目（作为其目标之一）旨在开发一种通用推理系统，并以此为基础与EPSRC的策略和研究领域保持一致：该项目属于EPSRC信息和通信技术（ICT）主题（ICT）主题以及以下研究领域：人工智能技术，数据库和信息系统。研究方法：宣言规则，例如序言和数据编则是表达专家知识的常见形式主义，并用于许多系统中。由于制定此类规则是耗时的，需要稀缺的专家知识，因此开发学习此类规则的算法至关重要。该项目解决了学习存在规则的问题，该规则在许多情况下发现了应用程序，例如知识图，语义网和网络数据提取。特别是，我们专注于为存在规则开发进化学习算法。我们定义规则学习设置并审查学习规则的主要方法，例如自上而下，自下而上和神经方法。我们审查了统治学习的现有进化方法，讨论不同的遗传编码模式，初始人口创建方法，进化运算符和评估适应性功能。此外，从更广阔的角度来看，我们探讨了逻辑推理引擎和机器学习方法之间的四个相互作用模型。最后但并非最不重要的一点是，我们以有希望的实验结果概述了生存规则学习的拟议研究问题的答案，并在粗腐蚀性，知识图规则挖掘和问题答案数据集中展示了应用。该项目的重点是研究归纳性逻辑编程（ILP）问题的进化算法（EA，也称为遗传算法，GA）。 EAS是一个以生物学风格的搜索算法为家族，可为最有希望的初步解决方案进行优化，同时探索广泛的搜索空间。特别是，在我们的环境中，原子，部分或参数化规则可以视为染色体，可以通过突变，交叉和选择的操作得出新的染色体种群。在执行这些操作时，计算出一种称为健身功能的质量度量，以判断获得的新一代染色体是否适合继续搜索。这种进化算法通常不会进行详尽的搜索，同时又不属于本地Optima。此外，它们具有灵活性，因为它们可能不需要在规则的形状上强加的模板，因为在其他方法中通常情况下是这种情况。涉及的公司和合作者：这是一个与BP合作的EPSRC工业案例学生奖学金项目。

项目成果

Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence

• DOI: 10.1609/aaai.v34i05.6216
• 发表时间: 2019-05
• 期刊: ArXiv
• 作者: Yuhang Song;Jianyi Wang;Thomas Lukasiewicz;Zhenghua Xu;Mai Xu;Zihan Ding;Lianlong Wu

Evolutionary Learning of Existential Rules

存在规则的进化学习

• DOI: 10.24963/ijcai.2019/928
• 发表时间: 2019
• 作者: Wu L

An Evolutionary Algorithm for Rule Learning over Knowledge Graphs

知识图谱规则学习的进化算法

• 发表时间: 2021
• 作者: Wu L

Democratise Financial Knowledge Graph Construction By Mining Massive Brokerage Research Report

挖掘海量券商研究报告，民主化金融知识图谱建设

• 发表时间: 2022
• 作者: Cheng Z

Rule Learning over Knowledge Graphs with Genetic Logic Programming (Extended Abstract)

使用遗传逻辑编程进行知识图的规则学习（扩展摘要）

• 发表时间: 2022
• 作者: Wu L