Graph and Ontology Specialist

The University of Sheffield is a remarkable place to work. Our people are at the heart of everything we do. Their diverse backgrounds, abilities and beliefs make Sheffield a world-class university.

We offer a fantastic range of benefits including a highly competitive annual leave entitlement (with the ability to purchase more), a generous pension scheme, flexible working opportunities, a commitment to your development and wellbeing, a wide range of retail discounts, and much more. Find out more about our benefits (opens in a new window) and join us to become part of something special.

Are you an experienced knowledge engineer who enjoys solving complex data challenges? We have an exciting opportunity for you to join us as a Knowledge Graph and Ontology Specialist and build the semantic foundations for the future of industrial data.

You will join the AMRC at the University of Sheffield, as part of a growing interoperability team currently funded through the leadership of a UKRI Future Leadership Fellow. We are tackling critical barriers of system interoperability preventing organisations from leveraging the benefits of leading-edge technology, such as digital twins and AI, by unifying the current siloed infrastructures to drive industrial adoption.

While the wider project focuses on accelerating industrial interoperability approaches, this role is dedicated entirely to the knowledge graph engineering and ontological understanding underpinning its success.

• Design, build, and maintain formal, machine-readable ontologies (e.g., using UML, RDF(S), SHACL, OWL) to support knowledge representation across multiple high-impact industrially-focused innovation projects.
• Apply advanced modelling paradigms, explicitly determining the appropriate use of 3D (endurantist/spatial) versus 4D (perdurantist/spatiotemporal) data modelling approaches to capture the state and lifecycle of engineering and research entities.
• Clearly document and differentiate the use of semantic technologies from primitive data dictionaries and taxonomies through to formal ontologies and logic across the project's infrastructure, ensuring the right tool is used for the right semantic requirement.
• Work closely with end users, software engineering and data scientists to ensure that all semantic models are FAIR (Findable, Accessible, Interoperable, and Reusable).
• Collaborate with the senior technical fellow, industry partners, and domain experts to extract implicit domain knowledge into explicit, rigorous conceptual models.
• Design the high-level semantic strategy and lifecycle management for the project's knowledge graphs and data schemas.
• Lead the writing of technical documentation, ontology release notes, and contribute to the dissemination of the project's ontological approach.
• Provide dissemination and mentorship to research teams on the importance of robust knowledge graph development and the practical differences between different semantic approaches (taxonomies vs ontologies).
• Organise technical alignment meetings and supervise/mentor junior staff.
• Make ethical decisions in your role, embedding the University's sustainability strategy into your working activities wherever possible.
• Carry out other duties, commensurate with the grade and remit of the post.

Our diverse community of staff and students recognises the unique abilities, backgrounds, and beliefs of all. We foster a culture where everyone feels they belong and are respected. Even if your past experience doesn't match perfectly with this role's criteria, your contribution is valuable, and we encourage you to apply.

• Essential:
  Bachelor's or master's degree in Information Science, Computer Science, Philosophy (with a focus on formal logic/ontology), Systems Engineering, or a related area, coupled with 2-3 years of practical knowledge graph and ontology experience.
• Essential:
  Working knowledge of foundational upper ontologies (e.g., BORO, HQDM, IES, ISO 15926, BFO, UFO, SUMO, DOLCE) and a demonstrable understanding of 4D (perdurantist / spatiotemporal) vs. 3D (endurantist / spatial) modelling methods in extending domain ontologies.
• Essential:
  Deep, practical understanding of the distinctions, limitations, and appropriate applications of formal ontologies versus data dictionaries, vocabularies, and taxonomies.
• Essential:
  Experience with semantic web technologies (RDF(S), OWL, SPARQL, SHACL), standard conceptual modelling languages (e.g., UML or…