I have an interdisciplinary background, with a research profile revolving around the formalisation and of language and science. My Oxford DPhil thesis (in print as a monograph), explores how world knowledge can be learnt automatically from text by employing Inductive Logic Programming, a subfield of machine learning. The knowledge learnt involves cause-effect relations between individuals and companies for the financial domain (e.g. if a person A is being fired by a company B then another person C will be hired, a company D acquires a company E and so on). This work covers topics in Computational Linguistics, Computational Semantics, Knowledge Discovery & Domain Adaptation and pioneered the acquisition of world knowledge exclusively from corpora.
The novelty and significance of my thesis work lies in its holistic approach to learning domain knowledge from a corpus and I have been interested in applying it to other areas, such as the Biosciences where the plethora of information available in terms of articles and papers on the web is making the acquisition of knowledge difficult to manage. This desire of mine to see a useful and purposeful application of my thesis work in the Biosciences lead me to move towards Computational Biology and Biological text mining.
I currently hold an Early Career Fellowship from the Leverhulme Trust (2010-2013) and have a
joint affiliation with the Department of Computer Science at Aberystwyth University, UK, and the
text mining group at the European Bioinformatics Institute (EMBL-EBI) in Cambridge, where I will
be hosted for the duration of my fellowship. My research interests include Biomedical Text Mining, Knowledge Discovery, Information Extraction, Textual Entailment, Machine learning applications for
Natural language processing, as well as automating the research lifecycle and bridging the gap
between Natural Language Processing and the Semantic Web.