Abstract. We propose a new family of Description Logics (DLs), called DLLite, specifically tailored to capture basic ontology languages, while keeping low complexity of reasoning. Reasoning here means not only computing subsumption between concepts, and checking satisfiability of the whole knowledge base, but also answering complex queries (in particular, unions of conjunctive queries) over the instance level (ABox) of the DL knowledge base. We show that, for the DLs of the DL-Lite family, the usual DL reasoning tasks are polynomial in the size of the TBox, and query answering is LogSpace in the size of the ABox (i.e., in data complexity). To the best of our knowledge, this is the first result of polynomial time data complexity for query answering over DL knowledge bases. Notably our logics allow for a separation between TBox and ABox reasoning during query evaluation: the part of the process requiring TBox reasoning is independent of the ABox, and the part of the process requiring access to the ABox can be carried out by an SQL engine, thus taking advantage of the query optimization strategies provided by current Data Base Management Systems. Since it can be shown that even slight extensions to the logics of the DL-Lite family make query answering at least NLogSpace in data complexity, thus ruling out the possibility of using on-the-shelf relational technology for query processing, we can conclude that the logics of the DL-Lite family are the maximal DLs supporting efficient query answering over large amounts of instances.

Abstract. Many organizations nowadays face the problem of accessing existing data sources by means of flexible mechanisms that are both powerful and efficient. Ontologies are widely considered as a suitable formal tool for sophisticated data access. The ontology expresses the domain of interest of the information system at a high level of abstraction, and the relationship between data at the sources and instances of concepts and roles in the ontology is expressed by means of mappings. In this paper we present a solution to the problem of designing effective systems for ontology-based data access. Our solution is based on three main ingredients. First, we present a new ontology language, based on Description Logics, that is particularly suited to reason with large amounts of instances. The second ingredient is a novel mapping language that is able to deal with the so-called impedance mismatch problem, i.e., the problem arising from the difference between the basic elements managed by the sources, namely data, and the elements managed by the ontology, namely objects. The third ingredient is the query answering method, that combines reasoning at the level of the ontology with specific mechanisms for both taking into account the mappings and efficiently accessing the data at the sources.

We review PLTLf and PLDLf, the pure-past versions of the well-known logics on finite traces LTLf and LDLf, respectively. PLTLf and PLDLf are logics about the past, and so scan the trace backwards from the end towards the beginning. Because of this, we can exploit a foundational result on reverse languages to get an exponential improvement, over LTLf /LDLf , for computing the corresponding DFA. This exponential improvement is reflected in several forms of sequential decision making involving temporal specifications, such as planning and decision problems in non-deterministic and non-Markovian domains. Interestingly, PLTLf (resp., PLDLf ) has the same expressive power as LTLf (resp., LDLf ), but transforming a PLTLf (resp., PLDLf ) formula into its equivalent LTLf (resp.,LDLf) is quite expensive. Hence, to take advantage of the exponential improvement, properties of interest must be directly expressed in PLTLf /PLDLf .

UML is the de-facto standard formalism for software design and analysis. To support the design of large-scale industrial applications, sophisticated CASE tools are available on the market, that provide a user-friendly environment for editing, storing, and accessing multiple UNIL diagrams. It would be highly desirable to equip such CASE tools with automated reasoning capabilities, such as those studied in Artificial Intelligence and, in particular, in Knowledge Representation and Reasoning. Such capabilities would allow to automatically detect relevant formal properties of UML diagrams, such as inconsistencies or redundancies. With regard to this issue, we consider UML class diagrams, which are one of the most important components of UML, and we address the problem of reasoning on such diagrams. We resort to several results developed in the field of Knowledge Representation and Reasoning, regarding Description Logics (DLs), a family of logics that admit decidable reasoning procedures. Our first contribution is to show that reasoning on UML class diagrams is EXPTIME-hard, even under restrictive assumptions; we prove this result by showing a polynomial reduction from reasoning in DLs. The second contribution consists in establishing EXPTIME-membership of reasoning on UML class diagrams, provided that the use of arbitrary OCL (first-order) constraints is disallowed. We get this result by using DLR(ifd), a very expressive EXPTIME-decidable DL that has been developed to capture typical features of conceptual and object-oriented data models. ne last contribution has a more practical flavor, and consists in a polynomial encoding of UML class diagrams in the DL ALCQI, which essentially is the most expressive DL supported by current state-of-the-art DL-based reasoning systems. Though less expressive than DLR(ifd), the DL ALC QI preserves enough semantics to keep reasoning about UML class diagrams sound and complete. Exploiting such an encoding, one can use current DL-based reasoning systems as core reasoning engines for a next generation of CASE tools, that are equipped with reasoning capabilities on UML class diagrams. (c) 2005 Elsevier B.V. All rights reserved

Abstract. The main focus of this paper is on automatic e-Service composition. We start by developing a framework in which the exported behavior of an e-Service is described in terms of its possible executions (execution trees). Then we specialize the framework to the case in which such exported behavior (i.e., the execution tree of the e-Service) is represented by a finite state machine. In this specific setting, we analyze the complexity of synthesizing a composition, and develop sound and complete algorithms to check the existence of a composition and to return one such a composition if one exists. To the best of our knowledge, our work is the first attempt to provide an algorithm for the automatic synthesis of e-Service composition, that is both proved to be correct, and has an associated computational complexity characterization.