REASONING ABOUT TEMPORAL KNOWLEDGE

Supervisors

Suitable for

Abstract

"Our world produces nowadays huge amounts of time stamped data, say measurements from meteorological stations, recording of online payments, GPS locations of your mobile phone, etc. To reason on top of such massive temporal datasets effectively we need to provide a well-structured formalisation of temporal knowledge and to devise algorithms with good computational properties. This, however, is highly non-trivial; in particular logical formalisms for temporal reasoning often have high computational complexity.

This project provides an opportunity to join the Knowledge Representation and Reasoning group and participate in exciting EPSRC-funded research on temporal reasoning, temporal knowledge graphs, and reasoning over streaming data. There are opportunities to engage both in theoretically-oriented and practically-oriented research in this area. For example, in recent months, we have been investigating the properties and applications of DatalogMTL---a temporal extension of the well-known Datalog rule language which is well-suited for reasoning over large and frequently-changing temporal datasets; the project could focus on analysing the theoretical properties of DatalogMTL and its fragments such as its complexity and expressiveness, or alternatively in more practical aspects such as optimisation techniques for existing reasoning algorithms. There are many avenues for research in this area and we would be more than happy to discuss possible concrete alternatives with the student(s)."

The theoretical part of the project requires good understanding of logics (completing Knowledge Representation and Reasoning course could be beneficial), whereas the practical part is suited for those who have programming skills.