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M. Ortiz de la Fuente:
"Improving Data Management using Domain Knowledge";
Talk: International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, Sweden (invited); 06-13-2018 - 06-19-2018; in: "Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden.", (2018), ISBN: 978-0-9992411-2-7; 5709 - 5713.

The development of tools and techniques for flexible and reliable data management is a long-standing challenge, ever more pressing in today´s data-rich world. We advocate using domain knowledge expressed in ontologies to tackle it, and summarize some research efforts to this aim that follow two directions. First, we consider the problem of ontology-mediated query answering (OMQA), where queries in a standard database query language are enriched with an ontology expressing background knowledge about the domain of interest, used to retrieve more complete answers when querying incomplete data. We discuss some of our contributions to OMQA, focusing on (i) expressive languages for OMQA, with emphasis on combining the open- and closed-world assumptions to reason about partially complete data; and (ii) OMQA algorithms based on rewriting techniques. The second direction we discuss proposes to use ontologies to manage evolving data. In particular, we use ontologies to model and reason about constraints on datasets, effects of operations that modify data, and the integrity of the data as it evolves.

Description Logics and Ontologies, Logics for Knowledge Representation, Knowledge Representation Languages, Databases

http://dx.doi.org/10.24963/ijcai.2018/814

https://www.ijcai.org/proceedings/2018/0814.pdf