Mathieu d'AquinSenior Research Fellow
I have now moved to the Insight Centre for Data Analytics of the National University of Ireland Galway. in KMi, I was a senior research fellow. The major common point between all my research activities is the Semantic Web, and especially methods and tools to build intelligent applications relying on formalised knowledge distributed online. I have especially been involved in the development of the Watson Semantic Web search engine, and in many applications of its APIs.
As part of several projects, I have worked on many aspects of building and exploiting the Semantic Web, including ontology building, ontology modularization, ontology matching, ontology evolution, ontology publication, etc. More recently, I have been working on aspects related to the use of semantic technologies and the Semantic Web for monitoring and managing online personal information, as well to the realisation of applications producing and consuming linked data. As part of MK:Smart, I'm leading the development of the MK Data Hub infrastructure for data curation, management and analysis.
Keys: Semantic Web, Ontologies, NeOn, Linked Data, Semantic Applications, Watson, Cupboard, Privacy, Personal Information Management, Smart Cities, IoT
Daquino, M., Daga, E., d'Aquin, M., Gangemi, A., Holland, S., Laney, R., Penuela, A. and Mulholland, P. (2017) Characterizing the Landscape of Musical Data on the Web: State of the Art and Challenges, Workshop: Second Workshop on Humanities in the Semantic Web - WHiSe II at Co-located with the 16th International Semantic Web Conference (ISWC), Vienna, Austria
Liu, S., Allocca, C., d'Aquin, M. and Motta, E. (2017) TAA: A Platform for Triple Accuracy Measuring and Evidence Triples Discovering, Demo at The International Semantic Web Conference 2017
Liu, S., d'Aquin, M. and Motta, E. (2017) Measuring Accuracy of Triples in Knowledge Graphs, International Conference on Language, Data and Knowledge, Galway, Ireland, 10318, pp. 343-357, Springer, Cham
Liu, S. and d'Aquin, M. (2017) Unsupervised Learning for Understanding Student Achievement in a Distance Learning Setting, The IEEE Global Engineering Education Conference (EDUCON) 2017, Athens, Greece, IEEE
Daga, E., d'Aquin, M., Motta, E. and Gangemi, A. (2016) An Incremental Learning Method to Support the Annotation of Workflows with Data-to-Data Relations, Knowledge Engineering and Knowledge Management, pp. 129-144, Springer