Talks and Poster Presentations (with Proceedings-Entry):

M.-P. Andresel, M. Ortiz de la Fuente, M. Simkus:
"Query Rewriting for Ontology-mediated Conditional Answers";
Talk: 34th AAAI Conference on Artificial Intelligence (AAAI-20), New York, New York, USA; 2020-02-07 - 2020-02-12; in: "The Thirty-Forth AAAI Conference on Artificial Intelligence, AAAI 2020, New York, New York, USA, February 7 - 12, 2020", V. Conitzer, F. Sha (ed.); AAAI-20 Technical Tracks, Vol. 34 No. 03 (2020), 2734 - 2741.

English abstract:
Among many proposals for extracting useful answers from incomplete data, ontology-mediated queries (OMQs) use domain knowledge to infer missing facts. We propose an extension of OMQs that allows to make certain assumptions-for example, about parts of the data that may be unavailable at query time, or costly to query-and retrieve conditional answers, that is, tuples that become certain query answers when the assumptions hold. We show that querying in this powerful formalism often has no higher worst-case complexity than in plain OMQs, and that these queries are first-order rewritable for DL-LiteR . Rewritability is preserved even if we use closed predicates to combine the (partial) closed- and open-world assumptions. This is remarkable, since closed predicates are a very useful extension of OMQs, but they make query answering intractable in data complexity even in very restricted settings.

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Electronic version of the publication:

Related Projects:
Project Head Mantas Simkus:

Project Head Mantas Simkus:

Created from the Publication Database of the Vienna University of Technology.