Large language models can generate impressive responses, but they sometimes produce incorrect or inconsistent information because they lack explicit grounding in structured knowledge. Ontologies provide a way to organize knowledge about concepts and their relationships, which can help AI systems better understand and reason about information.
In this project, the student will work with a senior researcher and join a small research team comprising postgraduate students. Together, the team will explore how large language models can connect their generated text to concepts in an ontology and use these structured relationships to support more reliable reasoning.
The aim of the project is to investigate simple methods for linking language model outputs to ontology concepts and to study how ontology based reasoning can improve the accuracy and consistency of AI systems. Through this work, the student will gain hands on experience in modern AI research and knowledge driven approaches to intelligent systems.
School
Computer Science and Engineering
Research Area
Artificial intelligence | Natural language processing | Data management and database
Suitable for recognition of Work Integrated Learning (industrial training)?
No
- Research environment
- Expected outcomes
- Supervisory team
- Reference material/links
The student will work closely with a senior researcher and collaborate with a small team of postgraduate students working on AI and data centric systems.
The supervisor will provide the relevant research environment, including GPU server and LLM API.
- The project will produce an experimental study on ontology linking and ontology based reasoning with large language models.
- Outcomes include a research poster, a short research report, and a project presentation.
- There may also be opportunities to contribute to a conference submission and to participate in the academic review process.
