User concept models are formal description and specification of user background knowledge. In their brains, users implicitly possess a concept model, which is generated from their background knowledge. While this concept model cannot be proven in laboratories, many knowledge engineers have observed it in user behaviour. When users read through a document, they can easily determine whether or not it is of their interest, on the basis of a judgement that arises from their implicit concept models. Therefore, there exists a hypothesis if a user’s concept model can be simulated, we can understand how a decision (e.g., whether a document is interesting) is made, and thus, we can infer user information needs by analysing the existing concepts in simulated user concept model. This study focuses on user concept models in the personalised information gathering considering challenges presented in Big Data era. The thesis project will make potential theoretical contributions to knowledge advancement in knowledge engineering and cognitive science, as well methodological contributions to text mining, information retrieval, and data format to help deal with Big Data challenges.

Professor Xiaohui Tao

• Professor Ji Zhang

• School of Agricultural and Environmental Sciences

• Artificial Intelligence and Image Processing
• Cognitive Sciences
• Data Format
• Information Systems

• Doctor of Philosophy (DPHD)
• Master of Research (MRES)