Querying DL-lite Knowledge Bases from Hidden Datasets *

We compare repair plans based on the answers they satisfy, an unwanted answer being satisfied if it does not hold under brave semantics in the resulting KB, and a wanted answer

After transfer- ring the notions of globally-, Pareto- and completion-optimal repairs to our setting, we study the data complexity of the core reasoning tasks: query entailment

Also, we present polynomial time algorithms for the evolution of the instance level knowledge bases expressed in DL-Lite A,id , which the most expressive Description Logics of

We consider DL-Lite N horn , the most expressive dialect of DL-Lite that does not include role inclusions and for which query answering is in AC 0 for data complexity; for relations

We first show that for SQL semantics and satisfiable ontologies we can apply the same rewriting as for DL-Lite b core [21], which implies TC 0 data complexity of query

We proposed a framework for query answering over inconsistent KBs that is based on (i) a notion of repair that preserves more information than classical repair strategies, (ii)

Motivated by the fact that real-world datasets contain concrete data values and database queries use built-in predicates on these values, we investigate ontology- based query

We consider the setting of temporal query answering pre- sented in [2] where a temporal knowledge base (TKB) consists of a global TBox and a sequence of ABoxes that represents the