Bernardo Cuenca Grau
Professor Bernardo Cuenca Grau
Room
305,
Wolfson Building,
Parks Road, Oxford OX1 3QD
United Kingdom
Interests
My research is in the broad field of artificial intelligence. In particular, my work revolves around the areas of knowledge representation and reasoning, knowledge graphs, computational logic, semantic technologies, and their applications to data management and the Web. My activities within these areas cover a wide spectrum, including theory and foundations, algorithm design, software and systems, technology standards, and engagement with industry.
You can find my DBLP entry here and my Google Scholar profile here.
Biography
I am a professor at the Department of Computer Science and a Tutorial Fellow at Keble College. Until October 2017, I held a prestigious University Research Fellowship awarded by the British Royal Society. I am a co-founder of Oxford Semantic Technologies , a start-up company from the University of Oxford focusing on high-performance reasoning and query evaluation over large-scale knowledge graphs.
Awards and Fellowships
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Distinguished Paper Award at the 2017 International Joint Conference on Artificial Intelligence for the paper "Foundations of Declarative Data Analysis Using Limit Datalog Programs"
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Best Paper Award at the 2010 AAAI Conference on Artificial Intelligence for the paper "How Incomplete is your Semantic Web Reasoner?"
- The 2021 Semantic Web Science Association 10 Year Award for the paper "LogMap: Logic Based and Scalable Ontology Matching".
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Best Paper Award at the 3thrd European Semantic Web Conference (ESWC-2006) for the paper "Repairing Unsatisfiable Concepts in OWL Ontologies."
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Royal Society University Research Fellowship (2009-2017).
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Oxford Department of Computer Science Teaching Award in 2021 for the course "Artificial Intelligence".
University of Oxford Mathematical And Physical Sciences Division (MPLS) Commercial Impact Award (Honourable Mention) in 2021.
Doctoral Study in Computer Science
Students working towards their D.Phil (PhD) are an integral part of the Information Systems Group, and make a vital contribution to our research. See our studentships page for further details, and information about funding opportunities . Should you consider applying for a doctoral position in our group, please do not hesitate to contact me first.
Selected Activities
- Co-founder, Oxford Semantic Technologies .
- Editorial Board Member, ACM Transactions on the Web .
- Editorial Board Member, Journal of Web Semantics.
- Editorial Board Member, Semantic Web Journal. .
- General Chair: 2020 International Workshop on Description Logics (DL-2020)
Software Tools
I have been involved in the design of several tools for ontology management and reasoning. These include the ontology matching tool LogMap , the ontology reasoners MORe , PAGOdA , and SEQUOIA, the temporal reasoner METEOR, and the semantic faceted search system SemFacet
See also
Selected Publications
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The Complexity and Expressive Power of Limit Datalog
Mark Kaminski‚ Egor V. Kostylev‚ Bernardo Cuenca Grau‚ Boris Motik and Ian Horrocks
In Journal of the ACM (JACM). Vol. 62. February, 2022.
Details about The Complexity and Expressive Power of Limit Datalog | BibTeX data for The Complexity and Expressive Power of Limit Datalog | Link to The Complexity and Expressive Power of Limit Datalog
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The Delay and Window Size Problems in Rule−based Stream Reasoning
Alessandro Ronca‚ Mark Kaminski‚ Bernardo Cuenca Grau and Ian Horrocks
In Artificial Intelligence Journal (AIJ). January, 2022.
Details about The Delay and Window Size Problems in Rule−based Stream Reasoning | BibTeX data for The Delay and Window Size Problems in Rule−based Stream Reasoning | Link to The Delay and Window Size Problems in Rule−based Stream Reasoning
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Faithful Approaches to Rule Learning
David Tena Cucala‚ Bernardo Cuenca Grau and Boris Motik
In Proceedings of the 19th International Conference on the Principles of Knowledge Representation and Reasoning (KR−2022). 2022.
Details about Faithful Approaches to Rule Learning | BibTeX data for Faithful Approaches to Rule Learning