[School of Engineering PhD Scholarships] Advancing Symbolic AI for Knowledge Representation and Reasoning: Generalised Adaptive Content Extraction and Reuse for Ontologies

About the project

About the Project Automated content extraction methods provide useful resources for backend-processing in knowledge processing system, and they greatly ease tasks of knowledge engineers in creating, updating and generally maintaining knowledge bases but also of reusing their content and creating new services. Previous work in the Department has developed automated means for modularising knowledge bases into smaller stand-alone knowledge bases. In other previous work we developed forgetting based approaches to compute restricted summarisations of knowledge bases and have used them to build engines to compute abductive explanations for new observations. For the medical ontology SNOMED CT, we have developed software to generate subontologies based on preservation of definitions of focus concepts and concept hierarchies. In this project we would like to conduct research that will combine and refine these techniques to create a powerful, adaptive framework for content extraction and sharing. In this framework the user will be given more flexibility and extra levers to control the level of detail of extracted content, how the content may be expressed, e.g., over a language with lower complexity, or how the content may be integrated with other knowledge bases or background theories to incorporate, e.g., numerical/domain specific reasoning. The project will explore how contemporary techniques in knowledge representation and reasoning (such as ontology modularisation, forgetting/interpolation, definability, ontology alignment, subontology generation) grounded in logic and automated reasoning can be used, refined and generalised for automated extraction and reuse of content from knowledge bases. There are various possible applications for adaptive content extraction and sharing in a range of domains: reasoning with large knowledge bases (SNOMED CT), explainable AI and software verification, which could provide suitable settings for showcasing the developed method and results on real-life problems. Envisaged results include new algorithms, resources, fundamental insights into computational properties (termination behaviour, complexity) and practical evaluation, written up for presentation at the SNOMED Expo, conferences and in top journals. The developed framework will be an important step in the area of ontologies and SNOMED CT to the provision of more flexible and sustainable automated support for customised content management and sharing, including collaborative community content development and involving integration of knowledge from different domains, as well as provision of new services with wider applicability. The project will involve close collaboration with the supervisory team and researchers in the Formal Methods Research Group and the Information Management Research Group of the Department who are leading the development of reasoning-based tools for processing ontologies. Our external collaborators at SMOMED International have committed relevant resources and staff time for meetings and technical support to the project. There is the opportunity to attend Masters-level courses which cover relevant state-of-the-art methods in the area. Additional support is available through Department/University researcher development sessions and resources. For this PhD project competitive University/Faculty funding is available for 3.5 years for Jan-March 26/27 (ideally) or September/October 27/28 start. Candidates with own funding or external funding are welcome to apply. Qualified applicants are strongly encouraged to informally contact the main supervisor to discuss the application prior to applying. This project is expected to start in September 2027. Before you apply: We strongly recommend that you contact the supervisors for this project before you apply. How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Engineering Scholarships . If you don’t already have an applicant account, please follow the instructions here. . When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing the motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications . Please note late or incomplete applications will not be considered. Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status. We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder. Eligibility : The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in a discipline directly relevant to the PhD PhD Computer Science, Mathematics, or a combined Computer Science and Maths degree, or related field(or international equivalent) OR any upper-second class (2:1) honours degree and a Master’s degree at merit in a discipline directly relevant to the PhD Computer Science, Mathematics, or a combined Computer Science and Maths degree, or related field (or international equivalent). Desirable background: A strong interest in symbolic AI and knowledge representation and reasoning A good educational/academic background in one or more of the following topics: logic, automated reasoning (theorem proving), modal/description logics, ontologies, theoretical computer science. Confidence and independence in programming complex systems in a mainstream programming language. Excellent report writing and presentation skills. This project will remain open until filled. If your application is submitted by 1 st November 2026, you can expect a decision by 18 th December 2026. If your application is submitted by 15 th January 2027, you can expect a decision by 30 th March 2027. Self or externally funded students can also be considered for this project. FSESoE

Research areas