In this paper we present MASTRO, a Java tool for ontology-based data access (OBDA) developed at Sapienza Università di Roma and at the Free University of Bozen-Bolzano. MASTRO manages OBDA systems in which the ontology is specified in DL-LiteA,id , a

A Description Logic (DL) ontology is constituted by two components, a TBox that expresses general knowledge about the concepts and their relationships, and an ABox that describes the properties of individuals that are instances of concepts. We address the problem of how to deal with changes to a DL ontology, when these changes affect only the ABox, i.e. when the TBox is considered invariant. We consider two basic changes, namely instance-level update and instance-level erasure, roughly corresponding to the addition and the deletion of a set of facts involving individuals. We characterize the semantics of instance-level update and erasure on the basis of the approaches proposed by Winslett and by Katsuno and Mendelzon. Interestingly, DLs are typically not closed with respect to instance-level update and erasure, in the sense that the set of models corresponding to the application of any of these operations to a knowledge base in a DL may not be expressible by ABoxes in . In particular, we show that this is true for DL-Lite, a tractable DL that is oriented towards data-intensive applications. To deal with this problem, we first introduce DL-Lite, a DL that minimally extends DL-Lite and is closed with respect to instance-level update, and present a polynomial algorithm for computing instance-level update in this logic. Then, we provide a principled notion of best approximation with respect to a fixed language of instance-level update and erasure, and exploit the algorithm for instance-level update for DL-Lite to get polynomial algorithms for approximated instance-level update and erasure for DL-Lite. These results confirm the nice computational properties of DL-Lite for data intensive applications, even where information about instances is not only read, but also written.

We study models of incomplete information for XML, their computational properties, and query answering. While our approach is motivated by the study of relational incompleteness, incomplete information in XML documents may appear not only as null values but also as missing structural information. Our goal is to provide a classification of incomplete descriptions of XML documents, and separate features—or groups of features—that lead to hard computational problems from those that admit efficient algorithms. Our classification of incomplete information is based on the combination of null values with partial structural descriptions of documents. The key computational problems we consider are consistency of partial descriptions, representability of complete documents by incomplete ones, and query answering. We show how factors such as schema information, the presence of node ids, and missing structural information affect the complexity of these main computational problems, and find robust classes of incomplete XML descriptions that permit tractable query evaluation.