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

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.