Brian Davis
Brian Davis
Dublin City University

Technology and application areas:

    • Semantic Web
    • Interoperability
    • Content Analytics and Preservation


Brian Davis is an Assistant Professor at the School of Computing, DCU. Previously, Brian was a Lecturer in Computer Science at Maynooth University and Research Fellow, Adjunct Lecturer and Research Unit Leader at the Science Foundation Ireland (SFI) funded INSIGHT Centre for Data Analytics, NUI Galway (NUIG) for four years and led the Knowledge Discovery Unit focusing on the specific research areas of: NLP, Data Visualization and Knowledge Discovery from heterogeneous data sources (text and graph).  He also coordinated a 3-year Horizon 2020 Innovation Action – SSIX – Social Sentiment Financial Indexes (Grant No 645425). His core expertise intersects with Natural Language Processing (NLP) and Ontology Engineering/Semantic Web. Other research interests include: and Data Visualisation, Computer and Data Ethics, NLP for social media, cross lingual opinion mining from social media. He has reviewed for several workshops, conferences and journals in the field of Semantic Web and NLP over the years i.e., WWW, ESWC, ISWC, LREC CNL, JODS, NLDB, LD4IE, DEXA, SEMANTICs and JNLE, IJCIS, LRE, JWS, ACM Surveys, Algorithms, Applied Computing and Informatics. In 2016 he was a guest editor for the LRE Special issue on Controlled Languages. He has over eight years research experience in Ontology Based Information Extraction and Semantic Annotation. Brian continues to be very passionate about NLP frameworks (completing all certification exams for the GATE – General Architecture for Text Engineering framework to GURU level). Moreover, he is particularly interested in conducting more research into Open Architectures for Text Generation.

Brian is interested in projects in  Natural Language Generation(NLG) – architectures for  interoperability between NLG components; Natural Language Generation(NLG) – Hybrid Knowledge based/Data driven NLG, and  Relation Extraction – Ontology Based Relation Extraction (Hybrid Rule based/Machine Learning approaches)