Extracting insights from a Knowledge Graph of an ongoing research programme for Climate Compatible Growth
In this thesis, you will leverage your computational skills and energy-domain knowledge to engineer a knowledge graph from bibliographic databases, online sources and records of research outputs. You will demonstrate the ability of the knowledge graph to provide insights that could support the monitoring and evaluation of the Climate Compatible Growth programme.
Background
Climate Compatible Growth (CCG) is a £38m UK aid-funded research programme, helping developing countries take a path of low carbon development whilst simultaneously unlocking profitable investment in green infrastructure, opening up new markets and supporting delivery of the Sustainable Development Goals (SDGs). Over 60 researchers are funded by the programme, and they work in multiple partner countries including Kenya, Zambia, Vietnam and Laos.
A large number (over 800) of research outputs are produced by the programme, including academic journal articles, datasets, policy briefs and reports. Among this number, the programme also records non-typical research outputs including teaching material, participation in events, and presentations at workshops.
Recent advances in linked data and knowledge engineering provide unrealised opportunities for extracting insights from this body of information. For example, a Knowledge Graph , uses a graph data-structure representation of entities – such as journal articles, authors, workshops and workshop participants - and encodes the relationships between them – “is the author of”, “attended”, “participated in”. This makes it possible to reason over the data, extracting implicit knowledge from the data using logical inference.
Task description
The thesis will be divided into several stages:
- Familiarisation and Planning – the student will determine the scope of the project with the supervisor team, review, select and conduct training on required software tools, data, supplemented with a short, targeted literature review.
- Implementation – the student will develop the knowledge graph, and investigate the potential for insights
- Writing Up and Examination – the student will finalise and present the written report
Learning outcomes
After completing the thesis work, the student will be able to:
- Develop, engineer and exploit knowledge graphs to reason over data
- Implement computational solutions by applying state-of-the-art knowledge engineering and Python libraries to large datasets and public APIs
- Conduct a research project independently from developing the overall research design through to the delivery of a final report
- Communicate results coherently and in a scientific manner
If the work is of good quality and the student is interested, the research project will be designed to be suitable for a peer-reviewed publication in a high-quality journal.
Prerequisites
Students should be familiar with the Python programming language, have an enthusiasm for sustainability/climate action, and have the ability to work independently to lead a research project by identifying key problems and delivering a solution.
Specialization track
Transformation of Energy System (TES)
Division/Department
– Department of Energy Technology
Research areas:
Duration
6 months, starting January 2023.
How to apply
Send an email expressing your interest in the topic and your CV to the supervisors.
Supervision
