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Department of Computer Science

Knowledge Graph Construction via Learning and Reasoning

Primary supervisor

Additional supervisors

  • Jiaoyan Chen

Contact admissions office

Other projects with the same supervisor


  • Competition Funded Project (Students Worldwide)

This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. Applications for this project are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full department and project details for further information.

Project description

Knowledge graphs (KGs) are graph-structured knowledge resources which are often expressed as triples such as ("UK", "hasCapital", "London") and ("London", "instanceOf", "City"). As well as such basic "facts", KGs often include structural knowledge about the domain or of experts, typically based on a hierarchy of entity types (a.k.a. classes or concepts), e.g., ("City", "subClassOf", "HumanSettlement"), and logics in the form of rules and ontological axioms. KGs also include ontologies which are often used by domains for representing human knowledge with reasoning supported, e.g., the SNOMED ontology in clinic settings. All these KGs have been widely used as the backend of AI and information systems, such as in Google search engine, Alexa and Siri.

The Information Management Group at the University of Manchester invites applications for PhD candidates in the area of KG construction. PhD projects in this area will explore how contemporary techniques in Machine Learning (such as Transformer and Semantic Embeddings), Knowledge Engineering (such as Knowledge Representation and Reasoning) and Data Engineering can be used as a foundation to KG construction and curation.

Examples of research challenges include: 1) how to extract new knowledge from tables and text; 2) how to insert the new knowledge into a KG; 3) how to integrate different KGs with matching; 4) how to curate KG, e.g., completion, with symbolic reasoning and machine learning prediction.

Applicants are expected to have:

1. An excellent undergraduate degree in Computer Science or Mathematics (or related discipline), and preferably, a relevant M.Sc. degree.
2. Confidence and independence in programming complex systems in Java or Python.
3. Previous academic or industry experience in at least one of the relevant topics such as Machine Learning, Natural Language Processing, Semantic Web, Knowledge Engineering, Data Engineering and Data Science.
4. Excellent report writing and presentation skills.

Please note that applicants must additionally satisfy the standard requirements for postgraduate studies at the University of Manchester, such as a first-class or high upper-second class (or an equivalent international qualification) and English language qualifications, as stated in the PGR guidelines.

Qualified applicants are strongly encouraged to informally contact Jiaoyan Chen ( and Norman Paton ( to discuss the application prior to applying.

Person specification

For information


Applicants will be required to evidence the following skills and qualifications.

  • You must be capable of performing at a very high level.
  • You must have a self-driven interest in uncovering and solving unknown problems and be able to work hard and creatively without constant supervision.


Applicants will be required to evidence the following skills and qualifications.

  • You will have good time management.
  • You will possess determination (which is often more important than qualifications) although you'll need a good amount of both.


Applicants will be required to address the following.

  • Comment on your transcript/predicted degree marks, outlining both strong and weak points.
  • Discuss your final year Undergraduate project work - and if appropriate your MSc project work.
  • How well does your previous study prepare you for undertaking Postgraduate Research?
  • Why do you believe you are suitable for doing Postgraduate Research?