=Paper=
{{Paper
|id=Vol-2369/keynote01
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-2369/keynote01.pdf
|volume=Vol-2369
|dblpUrl=https://dblp.org/rec/conf/amw/Ortiz19
}}
==None==
<pdf width="1500px">https://ceur-ws.org/Vol-2369/keynote01.pdf</pdf>
<pre>
From Complete to Incomplete Data and Back in
       Ontology-Enriched Databases

                              Magdalena Ortiz

             Institute of Information Systems, TU Wien, Austria



   Abstract. Enriching a database with a background theory expressing
   domain knowledge, usually called an ontology, has been proposed as a
   tool to overcome the incompleteness of data. In ontology mediated query-
   ing the theory is used to infer answers that may involve implied facts not
   present in the data. This and other related reasoning problems have been
   extensively studied over the last decade, mostly for ontologies written in
   description logics and in dialects of Datalog± . But the usual first-order
   semantics used in this setting, which assumes that all data is incomplete,
   can sometimes be too weak and not give all expected answers. I will
   discuss some alternatives that have been explored for combining com-
   plete and incomplete data in the presence of description logic ontologies,
   and the challenges that they pose, including increased computational
   complexity of reasoning and non-monotonicity of the ontology mediated
   query languages they induce. I will discuss a few interesting reasoning
   problems that arise in these setting, and some translations from these
   query languages into variants of Datalog.

</pre>