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Data integration provides a unified and abstract view over a set of existing data sources. The typical architecture of a data integration system comprises the global schema, which is the structure for the unified view, the source schema, and the mapping, which is a formal account of how data at the sources relate to the global view. Most of the research work on data integration in the last decades deals with the problem of processing a query expressed on the global schema by computing a suitable query over the sources, and then evaluating the latter in order to derive the answers to the original query. Here, we address a novel issue in data integration: starting from a query expressed over the sources, the goal is to find an abstraction of such query, i.e., a query over the global schema that captures the original query, modulo the mapping. The goal of the paper is to provide an overview of the notion of abstraction in data integration, by presenting a formal framework, illustrating the results that have appeared in the recent literature, and discussing interesting directions for future research.

The use of virtual collections of data is often essential in several data and knowledge management tasks. In the literature, the standard way to define virtual data collections is via views, i.e., virtual relations defined using queries. In data and knowledge bases, the notion of views is a staple of data access, data integration and exchange, query optimization, and data privacy. In this work, we study views in Ontology-Based Data Access (OBDA) systems. OBDA is a powerful paradigm for accessing data through an ontology, i.e., a conceptual specification of the domain of interest written using logical axioms. Intuitively, users of an OBDA system interact with the data only through the ontology's conceptual lens. We present a novel framework to express natural and sophisticated forms of views in OBDA systems and introduce fundamental reasoning tasks for these views. We study the computational complexity of these tasks and present classes of views for which these tasks are tractable or at least decidable.

In Ontology-Based Data Access (OBDA), a domain ontology is linked to the data sources of an organization in order to query, integrate and manage data through the concepts and relations of the domain of interest, thus abstracting from the technical details of the data layer implementation. While the great majority of contributions in OBDA in the last decade have been concerned with the issue of computing the answers of queries expressed over the ontology, recent papers address a different problem, namely the one of providing suitable abstractions of data services, i.e., characterizing or explaining the semantics of queries over the sources in terms of queries over the domain ontology. Current works on this subject are based on expressing abstractions in terms of unions of conjunctive queries (UCQs) over the ontology. In this paper we advocate the use of a non-monotonic language for this task. As a first contribution, we present a simple extension of UCQs with non-monotonic features, and show that non-monotonicity provides more expressive power in characterizing the semantics of data services. A second contribution is to prove that, similarly to the case of monotonic abstractions, depending on the expressive power of the languages used to specify the various components of the OBDA system, there are cases where neither perfect nor approximated abstractions exist for a given data service. As a third contribution, we single out interesting special cases where the existence of abstractions is guaranteed, and we present algorithms for computing such abstractions in these cases.

The issue of cooperation, integration, and coordination between information peers has been addressed over the years both in the context of the Semantic Web and in several other networked environments, including data integration, Peer-to-Peer and Grid computing, service-oriented computing, distributed agent systems, and collaborative data sharing. One of the main problems arising in such contexts is how to exploit the mappings between peers in order to answer queries posed to one peer. We address this issue for peers managing data through ontologies and in particular focus on ontologies specified in logics of the DL-Lite family. Our goal is to present some basic, fundamental results on this problem. In particular, we focus on a simplified setting based on just two interoperating peers, and we investigate how to solve the so-called “What-To-Ask” problem: find a way to answer queries posed to a peer by relying only on the query answering service available at the queried peer and at the other peer. We show both a positive and a negative result. Namely, we first prove that a solution to this problem always exists when the ontology is specified in \(\textit{DL-Lite}_{\mathcal {R}} \), and we provide an algorithm to compute it. Then, we show that for the case of \(\textit{DL-Lite}_{\mathcal {F}} \) the problem may have no solution. We finally illustrate that a solution to our problem can still be found even for more general networks of peers, and for any language of the DL-Lite family, provided that we interpret mappings according to an epistemic semantics, rather than the usual first-order semantics.

In order to be implemented by policy makers, science, technology, and innovation ( ) policies and indicator building need data. Whenever we need data, we need a method for data management, and in the era of big data , a crucial role is played by data integration . Therefore, STI policies and indicator development need data integration. Two main approaches to data integration exist, namely procedural and declarative. In this chapter, we follow the latter approach and focus our attention on the ontology-based data integration ( ) paradigm. The main principles of OBDI are: (i) Leave the data where they are. (ii) Build a conceptual specification of the domain of interest (ontology), in terms of knowledge structures. (iii) Map such knowledge structures to concrete data sources. (iv) Express all services over the abstract representation. (v) Automatically translate knowledge services to data services.

While the amount of data stored in current information systems continuously grows, and the processes making use of such data become more and more complex, extracting knowledge and getting insights from these data, as well as governing both data and the associated processes, are still challenging tasks. The problem is complicated by the proliferation of data sources and services both within a single organization, and in cooperating environments. Effectively accessing, integrating and managing data in complex organizations is still one of the main issues faced by the information technology industry today. Indeed, it is not surprising that data scientists spend a comparatively large amount of time in the data preparation phase of a project, compared with the data minining and knowledge discovery phase. Whether you call it data wrangling, data munging, or data integration, it is estimated that 50%-80% of a data scientists time is spent on collecting and organizing data for analysis. If we consider that in any complex organization, data governance is also essential for tasks other than data analytics, we can conclude that the challenge of identifying, gathering, retaining, and providing access to all relevant data for the business at an acceptable cost, is huge.

This paper proposes an Ontology-Based Data Management (OBDM) approach to coordinate, integrate and maintain the data needed for Science, Technology and Innovation (STI) policy development. The OBDM approach is a form of integration of information in which the global schema of data is substituted by the conceptual model of the domain, formally specified through an ontology. Implemented in Sapientia, the ontology of multi-dimensional research assessment, it offers a transparent platform as the base for the assessment process; it enables one to define and specify in an unambiguous way the indicators on which the evaluation is based, and to track their evolution over time; also it allows to the analysis of the effects of the actual use of the indicators on the behavior of scholars, and spot opportunistic behaviors; and it provides a monitoring system to track over time the changes in the established evaluation criteria and their consequences for the research system. It is argued that easier access to and a more transparent view of scientific-scholarly outcomes help to improve the understanding of basic science and the communication of research outcomes to the wider public. An OBDM approach could successfully contribute to solve some of the key issues in the integration of heterogeneous data for STI policies.

While big data analytics is considered as one of the most important paths to competitive advantage of today’s enterprises, data scientists spend a comparatively large amount of time in the data preparation and data integration phase of a big data project. This shows that data integration is still a major challenge in IT applications. Over the past two decades, the idea of using semantics for data integration has become increasingly crucial, and has received much attention in the AI, database, web, and data mining communities. Here, we focus on a specific paradigm for semantic data integration, called Ontology-Based Data Access (OBDA). The goal of this paper is to provide an overview of OBDA, pointing out both the techniques that are at the basis of the paradigm, and the main challenges that remain to be addressed.

Metamodeling and metaquerying are gaining momentum in the context of both conceptual modeling and semantic web. Indeed it has been largely recognized that metamodeling represents a very useful tool to formalize complex patterns involving elements of the domain of interest, that otherwise are forced to be excluded from the modeling process, and a number of languages equipped with a spectrum of metamodeling features have been studied. However, it has been recognized as well, that the benefit of using metamodeling is greatly limited if one cannot use metaquerying as tool for extracting knowledge deriving from such meta-level patterns. Unfortunately, at the moment, no system exists that correctly manages metamodeling and metaquerying. The goal of this work is precisely to fill this gap by introducing a system, called MQ-Mastro , that allows metaquerying over ontologies expressed in a language of the OWL 2 family equipped with a semantics appropriate for metamodeling.

A schema mapping is a formal specification of the relationship holding between the databases conforming to two given schemas, called source and target, respectively. While in the general case a schema mapping is specified in terms of assertions relating two queries in some given language, various simplified forms of mappings, in particular lav and gav, have been considered, based on desirable properties that these forms enjoy. Recent works propose methods for transforming schema mappings to logically equivalent ones of a simplified form. In many cases, this transformation is impossible, and one might be interested in finding simplifications based on a weaker notion, namely logical implication, rather than equivalence. More precisely, given a schema mapping M, find a simplified (lav, or gav) schema mapping M^' such that M^' logically implies M. In this paper we formally introduce this problem, and study it in a variety of cases, providing techniques and complexity bounds. The various cases we consider depend on three parameters: the simplified form to achieve (lav, or gav), the type of schema mapping considered (sound, or exact), and the query language used in the schema mapping specification (conjunctive queries and variants over relational databases, or regular path queries and variants over graph databases). Notably, this is the first work on comparing schema mappings for graph databases.

Ontology-based data access (OBDA) is a novel paradigm for accessing large data repositories through an ontology, that is a formal description of a domain of interest. Supporting the management of OBDA applications poses new challenges, as it requires to provide effective tools for (i) allowing both expert and non-expert users to analyze the OBDA specification, (ii) collaboratively documenting the ontology, (iii) exploiting OBDA services, such as query answering and automated reasoning over ontologies, e.g., to support data quality check, and (iv) tuning the OBDA application towards optimized performances. To fulfill these challenges, we have built a novel system, called MASTRO STUDIO, based on a tool for automated reasoning over ontologies, enhanced with a suite of tools and optimization facilities for managing OBDA applications. To show the effectiveness of MASTRO STUDIO, we demonstrate its usage in one OBDA application developed in collaboration with the Italian Ministry of Economy and Finance.

OWL 2 QL is the profile of OWL 2 targeted to Ontology-Based Data Access (OBDA) scenarios, where large amount of data are to be accessed, and thus answering conjunctive queries over data is the main task. However, this task is quite restrained wrt the classical KR Ask-and-Tell framework based on querying the whole theory, not only facts (data). If we use SPARQL as query language, we get much closer to this ideal. Indeed, SPARQL queries over OWL 2 QL, under the so-called Direct Semantics Entailment Regime, may comprise any assertion expressible in the language, i.e., both ABox atoms and TBox atoms, including inequalities expressed by means of DifferentIndividuals. Nevertheless this regime is hampered by the assumption that variables in queries need to be typed, meaning that the same variable cannot occur in positions of different types, e.g., both in class and individual position (punning). In this paper we dismiss this limiting assumption by resorting to a recent meta modeling semantics and show that query answering in the resulting entailment regime is polynomially compilable into Datalog (and hence PTIME wrt both TBox and ABox).

After years of focus on technologies for big data storing and processing, many observers are pointing out that making sense of big data cannot be done without suitable tools for conceptualizing, preparing, and integrating data (see http://www.dbta.com/). Research in the last years has shown that taking into account the semantics of data is crucial for devising powerful data integration solutions. In this work we focus on a specific paradigm for semantic data integration, named "Ontology-Based Data Access" (OBDA), proposed in [1--4]. According to such paradigm, the client of the information system is freed from being aware of how data and processes are structured in concrete resources (databases, software programs, services, etc.), and interacts with the system by expressing her queries and goals in terms of a conceptual representation of the domain of interest, called ontology. More precisely, a system realizing the vision of OBDA is constituted by three components:• The ontology, whose goal is to provide a formal, clean and high level representation of the domain of interest, and constitutes the component with which the clients of the system (both humans and software programs) interact.• The data source layer, representing the existing data sources in the information system, which are managed by the processes and services operating on their data.• The mapping between the two layers, which is an explicit representation of the relationship between the data sources and the ontology, and is used to translate the operations on the ontology (e.g., query answering) in terms of concrete actions on the data sources.

Artificial Intelligence technologies are growingly used within several software systems ranging from Web services to mobile applications. It is by no doubt true that the more AI algorithms and methods are used the more they tend to depart from a pure "AI" spirit and end to refer to the sphere of standard software. In a sense, AI seems strongly connected with ideas, methods and tools that are not (yet) used by the general public. On the contrary, a more realistic view of it would be a rich and pervading set of successful paradigms and approaches. Industry is currently perceiving semantic technologies as a key contribution of AI to innovation. In this paper a survey of current industrial experiences is used to discuss different semantic technologies at work in heterogeneous areas, ranging from Web services to semantic search and recommender systems.The resulting picture confirms the vitality of the area and allows to sketch a general taxonomy of approaches, that is the main contribution of this paper.

The task of an information integration system is to combine data residing at different sources, providing the user with a unified view of them, called global schema. Users formulate queries over the global schema, and the system suitably queries the sources, providing an answer to the user, who is not obliged to have any information about the sources. Recent developments in IT such as the expansion of the Internet and the World Wide Web, have made available to users a huge number of information sources, generally autonomous, heterogeneous and widely distributed: as a consequence, information integration has emerged as a crucial issue in many application domains, e.g., distributed databases, cooperative information systems, data warehousing, or on-demand computing. Recent estimates view information integration to be a $10 Billion market by 2006 [14].

Schema mappings establish a correspondence between data stored in two databases, called source and target respectively. Query processing under schema mappings has been investigated extensively in the two cases where each target atom is mapped to a query over the source (called GAV, global-as-view), and where each source atom is mapped to a query over the target (called LAV, local-as-view). The general case, called GLAV, in which queries over the source are mapped to queries over the target, has attracted a lot of attention recently, especially for data exchange. However, query processing for GLAV mappings has been considered only for the basic service of query answering, and mainly in the context of conjunctive queries (CQs) in relational databases. In this paper we study query processing for GLAV mappings in a wider sense, considering not only query answering, but also query rewriting, perfectness (the property of a rewriting to compute exactly the certain answers), and query containment relative to a mapping. We deal both with the relational case, and with graph databases, where the basic querying mechanism is that of regular path queries. Query answering in GLAV can be smoothly reduced to a combination of the LAV and GAV cases, and for CQs this reduction can be exploited also for the remaining query processing tasks. In contrast, as we show, GLAV query processing for graph databases is non-trivial and requires new insights and techniques. We obtain upper bounds for answering, rewriting, and perfectness, and show decidability of relative containment.

In this paper we present MASTRO, a Java tool for ontology-based data access (OBDA) developed at Sapienza Universita di Roma and at the Free University of Bozen-Bolzano. MASTRO manages OBDA systems in which the ontology is specified in DL-Lite A,id, a logic of the DL-Lite family of tractable Description Logics specifically tailored to ontology-based data access, and is connected to external JDBC enabled data management systems through semantic mappings that associate SQL queries over the external data to the elements of the ontology. Advanced forms of integrity constraints, which turned out to be very useful in practical applications, are also enabled over the ontologies. Optimized algorithms for answering expressive queries are provided, as well as features for intensional reasoning and consistency checking. MASTRO provides a proprietary API, an OWLAPI compatible interface, and a plugin for the Protege 4 ontology editor. It has been successfully used in several projects carried out in collaboration with important organizations, on which we briefly comment in this paper.

We illustrate the usefulness of an Ontology-Based Data Management (OBDM) approach to develop an open information system, allowing for a deep level of interoperability among different databases, and accounting for additional dimensions of data quality compared to the standard dimensions of the OECD (Quality framework and guidelines for OECD statistical activities, OECD Publishing, Paris, 2011) Quality Framework. Recent advances in engineering in computer science provide promising tools to solve some of the crucial issues in data integration for Research and Innovation.

Information integration is one of the most important aspects of a Data Warehouse. When data passes from the sources of the application-oriented operational environment to the Data Warehouse, possible inconsistencies and redundancies should be resolved, so that the warehouse is able to provide an integrated and reconciled view of data of the organization. We describe a novel approach to data integration in Data Warehousing. Our approach is based on a conceptual representation of the Data Warehouse application domain, and follows the so-called local-as-view paradigm: both source and Data Warehouse relations are defined as views over the conceptual model. We propose a technique for declaratively specifying suitable reconciliation correspondences to be used in order to solve conflicts among data in different sources. The main goal of the method is to support the design of mediators that materialize the data in the Data Warehouse relations. Starting from the specification of one such relation as a query over the conceptual model, a rewriting algorithm reformulates the query in terms of both the source relations and the reconciliation correspondences, thus obtaining a correct specification of how to load the data in the materialized view.

A basic feature of Terminological Knowledge Representation Systems is to represent knowledge by means of taxonomies, here called terminologies, and to provide a specialized reasoning engine to do inferences on these structures. The taxonomy is built through a representation language called a concept language (or description logic ), which is given a well-defined set-theoretic semantics. The efficiency of reasoning has often been advocated as a primary motivation for the use of such systems. The main contributions of the paper are: (1) a complexity analysis of concept satisfiability and subsumption for a wide class of concept languages; (2) algorithms for these inferences that comply with the worst-case complexity of the reasoning task they perform.

In ontology-based data access (OBDA), an ontology is connected to autonomous, and generally pre-existing, data repositories through mappings, so as to provide a high-level, conceptual view over such data. User queries are posed over the ontology, and answers are computed by reasoning both on the ontology and the mappings. Query answering in OBDA systems is typically performed through a query rewriting approach which is divided into two steps: (i) the query is rewritten with respect to the ontology (ontology rewriting of the query); (ii) the query thus obtained is then reformulated over the database schema using the mapping assertions (mapping rewriting of the query). In this paper we present a new approach to the optimization of query rewriting in OBDA. The key ideas of our approach are the usage of inclusion between mapping views and the usage of perfect mappings, which allow us to drastically lower the combinatorial explosion due to mapping rewriting. These ideas are formalized in PerfectMap, an algorithm for OBDA query rewriting. We have experimented PerfectMap in a real-world OBDA scenario: our experimental results clearly show that, in such a scenario, the optimizations of PerfectMap are crucial to effectively perform query answering.

The work “Data Integration under Integrity Constraints”, published at the CAiSE 2002 Conference, proposes a rewriting technique for answering queries in data integration systems, when the global schema contains the classical key and foreign key constraints, and the mapping between the data sources and the global schema is of the global-as-view type. In this addendum, we explain why this research was important and how it gave raise to several results in the following years.

In this paper we present the current version of Mastro, a system for ontologybased data access (OBDA) developed at Sapienza Universita di Roma. Mastro allows users for accessing external data sources by querying an ontology expressed in a fragment of the W3C Web Ontology Language (OWL). As in data integration [5], mappings are used in OBDA to specify the correspondence between a unified view of the domain (called global schema in data integration terminology) and the data stored at the sources. The distinguishing feature of the OBDA approach, however, is the fact that the global schema is specified using an ontology language, which typically allows to provide a rich conceptualization of the domain of interest, independently from the source representation.

A schema mapping is a formal specification of the relationship holding between the databases conforming to two given schemas, called source and target, respectively. While in the general case a schema mapping is specified in terms of assertions relating two queries in some given language, various simplified forms of mappings, in particular LAV and GAV, have been considered, based on desirable properties that these forms enjoy. Recent works propose methods for transforming schema mappings to logically equivalent ones of a simplified form. In many cases, this transformation is impossible, and one might be interested in finding simplifications based on a weaker notion, namely logical implication, rather than equivalence. More precisely, given a schema mapping M, find a simplified (LAV, or GAV) schema mapping M' such that M' logically implies M. In this paper we formally introduce this problem, and study it in a variety of cases, providing techniques and complexity bounds. The various cases we consider depend on three parameters: the simplified form to achieve (LAV, or GAV), the type of schema mapping considered (sound, or exact), and the query language used in the schema mapping specification (conjunctive queries and variants over relational databases, or regular path queries and variants over graph databases). Notably, this is the first work on comparing schema mappings for graph databases.

Managing interschema knowledge is an essential task when dealing with cooperative information systems. We propose a logical approach to the problem of both expressing interschema knowledge, and reasoning about it. In particular, we set up a structured representation language for expressing semantic interdependencies between classes belonging to different database schemas, and present a method for reasoning over such. interdependencies. The language and the associated reasoning technique makes it possible to build a logic-based module that can draw useful inferences whenever the need arises of both comparing and combining the knowledge represented in the various schemas. Notable examples of such inferences include checking the coherence of interschema knowledge, and providing integrated access to a cooperative information system.

We address the problem of dealing with inconsistencies in Description Logic (DL) knowledge bases. Our general goal is both to study DL semantical frameworks which are inconsistency-tolerant, and to devise techniques for answering unions of conjunctive queries posed to DL knowledge bases under such inconsistency-tolerant semantics. Our work is inspired by the approaches to consistent query answering in databases, which are based on the idea of living with inconsistencies in the database, but trying to obtain only consistent information during query answering, by relying on the notion of database repair. We show that, if we use the notion of repair studied in databases, inconsistency-tolerant query answering is intractable, even for the simplest form of queries. Therefore, we study different variants of the repair-based semantics, with the goal of reaching a good compromise between expressive power of the semantics and computational complexity of inconsistency-tolerant query answering.

One of the outcomes of the research work carried out on data integration in the last years is a clear architecture, comprising a global schema, the source schema and the mapping between the source and the global schema. In this chapter, we study data integration under this framework when the global schema is specified in OWL, the standard language for the Semantic Web and discuss the impact of this choice on computational complexity of query answering under different instantiations of the framework in terms of query language and form and interpretation of the mapping. We show that query answering in the resulting setting is computationally too complex, and discuss in detail the various sources of complexity. Then, we show how to limit the expressive power of the various components of the framework in order to have efficient query answering, in principle as efficient as query processing in relational DBMSs. In particular, we adopt OWL 2 QL as the ontology language used to express the global schema. OWL 2 QL is one of the tractable profiles of OWL 2, and essentially corresponds to a member of the DL-Lite family, a family of Description Logics designed to have a good trade-off between expressive power of the language and computational complexity of reasoning.

The goal of data integration is to provide a uniform access to a set of heterogeneous data sources, freeing the user from the knowledge about where the data are, how they are stored, and how they can be accessed. One of the outcomes of the research work carried out on data integration in the last years is a clear architecture, comprising a global schema, the source schema, and the mapping between the source and the global schema. Although in many research works and commercial tools the global schema is simply a data structure integrating the data at the sources, we argue that the global schema should represent, instead, the conceptual model of the domain. However, to fully pursue such an approach, several challenging issues are to be addressed. The main goal of this paper is to analyze one of them, namely, how to express the conceptual model representing the global schema. We start our analysis with the case where such a schema is expressed in terms of a UML class diagram, and we end up with a proposal of a particular Description Logic, called $\textit{DL-Lite}_{\mathcal A,id}$. We show that the data integration framework based on such a logic has several interesting properties, including the fact that both reasoning at design time, and answering queries at run time can be done efficiently.

The type data base is the part of the data base in which all the information about classes, membership relationships between objects and classes and interdependencies among classes are collected. We study specific type data bases, which allow for representing different kinds of incomplete information, such as existential and disjunctive assertions. In particular, we address the problems of consistency checking and query answering, proposing efficient solutions for both of them. Our analysis is carried out in the context of the logical approach to data bases and knowledge representation, which provides a suitable formal basis for the treatment of incomplete information.

The goal of data integration is to provide a uniform access to a set of heterogeneous data sources, freeing the user from the knowledge about where the data are, how they are stored, and how they can be accessed. One of the outcomes of the research work carried out on data integration in the last years is a clear conceptual architecture, comprising a global schema, the source schema, and the mapping between the source and the global schema. In this paper, we present a comprehensive approach to, and a complete system for, ontology-based data integration. In this system, the global schema is expressed in terms of a TBox of the tractable Description Logics ${\textit{DL-Lite}_{\mathcal A}}$, the sources are relations, and the mapping language allows for expressing GAV sound mappings between the sources and the global schema. The mapping language has specific mechanisms for addressing the so-called impedance mismatch problem, arising from the fact that, while the data sources store values, the instances of concepts in the ontology are objects. By virtue of the careful design of the various languages used in our system, answering unions of conjunctive queries can be done through a very efficient technique ( LogSpace with respect to data complexity) which reduces this task to standard SQL query evaluation. We also show that even very slight extensions of the expressive abilities of our system lead beyond this complexity bound.

Data exchange and virtual data integration have been the subject of several investigations in the recent literature. At the same time, the notion of peer data management has emerged as a powerful abstraction of many forms of flexible and dynamic data-centere ddistributed systems. Although research on the above issues has progressed considerably in the last years, a clear understanding on how to combine data exchange and data integration in peer data management is still missing. This is the subject of the present paper. We start our investigation by first proposing a novel framework for peer data exchange, showing that it is a generalization of the classical data exchange setting. We also present algorithms for all the relevant data exchange tasks, and show that they can all be done in polynomial time with respect to data complexity. Based on the motivation that typical mappings and integrity constraints found in data integration are not captured by peer data exchange, we extend the framework to incorporate these features. One of the main difficulties is that the constraints of this new class are not amenable to materialization. We address this issue by resorting to a suitable combination of virtual and materialized data exchange, showing that the resulting framework is a generalization of both classical data exchange and classical data integration, and that the new setting incorporates the most expressive types of mapping and constraints considered in the two contexts. Finally, we present algorithms for all the relevant data management tasks also in the new setting, and show that, again, their data complexity is polynomial.

Welcome to the Fifth International Conference on Ontologies, Databases, and Applications of Semantics (ODBASE 2006) This year's ODBASE conference was held in Montpellier, France from October 29 to November 3, 2006. The ODBASE conferences provide a forum for exchanging the latest research results on ontologies, data semantics, and other areas of computing related to the Semantic Web We encourage participation of both researchers and practitioners in order to facilitate exchange of ideas and results on semantic issues in Web information systems Towards this goal, we accepted both research and experience papers.

Much of the research on terminological reasoning aims at characterizing concept languages (also called terminological languages) with respect to both expressive power and computational complexity of computing subsumption. On the other hand, little attention has been paid to studying concept languages as query languages, i.e. as a means for extracting information from a concept-based knowledge base. In this paper we address this problem by exploring the possibility of using two different concept languages, one for asserting facts about individual objects, and the other for querying a set of such assertions. Contrary to many negative results on the complexity of terminological reasoning, our work shows that, provided that a limited language is used for the assertions, it is possible to employ a richer query language while keeping the reasoning process tractable. We also show that, on the other hand, there are constructs that make query answering inherently intractable.

In this paper we discuss an effective technique for automatic service composition and we present the prototype software that implements it. In particular, we characterize the behavior of a service in terms of a finite state machine. In this setting we discuss a technique based on satisfiability in a variant of Propositional Dynamic Logic that solves the automatic composition problem. Specifically, given (i) a client specification of his desired service, i.e., the service he would like to interact with, and (ii) a set of available services, our technique synthesizes the orchestration schema of a composite service that uses only the available services and fully realizes the client specification. The developed system is an open-source software tool, called (e-service composer), that implements our composition technique starting from services, each of them described in terms of a WSDL specification and a behavioral description expressed in any language that can capture finite state machines.

The paper presents INFOMIX a successful application of ASP technology to the domain of Data Integration. INFOMIX is a novel system which supports powerful information integration, utilizing the ASP system DLV. While INFOMIX is based on solid theoretical foundations, it is a user-friendly system, endowed with graphical user interfaces for the average database user and administrator, respectively. The main features of the INFOMIX system are: (i) a comprehensive information model, through which the knowledge about the integration domain can be declaratively specified, (ii) capability of dealing with data that may result incomplete and/or inconsistent with respect to global constraints, (iii) advanced information integration algorithms, which reduce (in a sound and complete way) query answering to cautious reasoning on disjunctive Datalog programs, (iv) sophisticated optimization techniques guaranteeing the effectiveness of query evaluation in INFOMIX, (v) a rich data acquisition and transformation framework for accessing heterogeneous data in many formats including relational, XML, and HTML data.

This paper addresses the issue of automatic service composition. We first develop a framework in which the exported behavior of a service is described in terms of a so-called execution tree, that is an abstraction for its possible executions. We then study the case in which such exported behavior (i.e. the execution tree of the service) can be represented by a finite state machine (i.e. finite state transition system). In this specific setting, we devise sound, complete and terminating techniques both to check for the existence of a composition, and to return a composition, if one exists. We also analyze the computational complexity of the proposed algorithms. Finally, we present an open source prototype tool, called [Formula: see text] (E-Service Composer), that implements our composition technique. To the best of our knowledge, our work is the first attempt to provide a provably correct technique for the automatic synthesis of service composition, in a framework where the behavior of services is explicitly specified.

We study peer-to-peer (P2P) data integration, where each peer models an autonomous system that exports data in terms of its own schema, and data interoperation is achieved by means of mappings among the peer schemas, rather than through a unique global schema. We propose a multi-modal epistemic logical formalization based on the idea that each peer is conceived as a rational agent that exchanges knowledge/belief with other peers, thus nicely modeling the modular structure of the system. We then address the issue of dealing with possible inconsistencies, and distinguish between two types of inconsistencies, called local and P2P, respectively. We define a nonmonotonic extension of our logic that is able to reason on the beliefs of peers under both local and P2P inconsistency tolerance. Tolerance to local inconsistency essentially means that the presence of inconsistency within one peer does not affect the consistency of the whole system. Tolerance to P2P inconsistency means being able to resolve inconsistencies arising from the interaction between peers. We study query answering in the new nonmonotonic logic, with the main goal of establishing its decidability and its computational complexity. Indeed, we show that, under reasonable assumptions on peer schemas, query answering is decidable, and is coNP-complete with respect to data complexity, i.e., the size of the data stored at the peers.

Dealing with inconsistencies is one the main challenges in data integration systems, where data stored in the local sources may violate integrity constraints specified at the global level. Recently, declarative approaches have been proposed to deal with such a problem. Existing declarative proposals do not take into account preference assertions specified between sources when trying to solve inconsistency. On the other hand, the designer of an integration system may often include in the specification preference rules indicating the quality of data sources. In this paper, we consider Local-As-View integration systems, and propose a method that allows one to assign formal semantics to a data integration system whose declarative specification includes information on source preferences. To the best of our knowledge, our approach is the first one to consider in a declarative way information on source quality for dealing with inconsistent data in Local-As-View integration systems.

Data integration systems provide access to a set of heterogeneous, autonomous data sources through a so-called global schema. There are basically two approaches for designing a data integration system. In the global-as-view approach, one defines the elements of the global schema as views over the sources, whereas in the local-as-view approach, one characterizes the sources as views over the global schema. It is well known that processing queries in the latter approach is similar to query answering with incomplete information, and, therefore, is a complex task. On the other hand, it is a common opinion that query processing is much easier in the former approach. In this paper we show the surprising result that, when the global schema is expressed in the relational model with integrity constraints, even of simple types, the problem of incomplete information implicitly arises, making query processing difficult in the global-as-view approach as well. We then focus on global schemas with key and foreign key constraints, which represents a situation which is very common in practice, and we illustrate techniques for effectively answering queries posed to the data integration system in this case.

In this paper we propose a foundational vision of e-Services, in which we distinguish between the external behavior of an e-Service as seen by clients, and the internal behavior as seen by a deployed application running the e-Service. Such behaviors are formally expressed as execution trees describing the interactions of the e-Service with its client and with other e-Services. Using these notions we formally define e-Service composition in a general way, without relying on any specific representation formalism.

In this paper, we study the problem of data integration in P2P systems. Differently from the traditional setting, data integration in these systems is not based on the existence of a global view. Instead, each peer exports data in terms of its own schema, and information integration is achieved by establishing mappings among the various peer schemas. We present a framework that captures this general architecture, and then we discuss the problem of characterizing the semantics of such framework. We show that the usual approach of resorting to a first-order logic intepretation of P2P mappings, leads both to a poor modeling of the whole system, and to undecidability of query answering, even for mappings of a restricted form. This motivates the need of a new semantics for P2P system. We then present a novel proposal, based on epistemic logic, and show that not only it adequately models the interactions among peers, but it also supports decidable query answering. In particular, for the restricted form of mapping mentioned above, query answering is polynomial with respect to the size of data stored in the peers.

In contrast to the relatively complex information that can be expressed in DL ABoxes (which we might call knowledge or information), databases and other sources such as files, semistructured data, and the World Wide Web provide rather simpler data, which must however be managed effectively. This chapter surveys the major classes of application of Description Logics and their reasoning facilities to the issues of data management, including: (i) expressing the conceptual domain model/ontology of the data source, (ii) integrating multiple data sources, and (iii) expressing and evaluating queries. In each case we utilize the standard properties of Description Logics such as the ability to express ontologies at a level closer to that of human conceptualization (e.g., representing conceptual schemas), determining consistency of descriptions (e.g., determining if a query or the integration of some schemas is consistent), and automatically classifying descriptions that are definitions (e.g., queries are really definitions, so we can classify them and determine subsumption between them).

In the traditional view, data warehouses provide large-scale caches of historic data. They sit between (a) information sources gained externally or through online transaction processing systems (OLTP) and (b) decision support or data mining queries following the vision of online analytic processing (OLAP). Three main arguments have been put forward in favor of this caching approach: 1. Performance and safety considerations. The concurrency control methods of most DBMS do not react well to a mix of short update transactions (as in OLTP) and OLAP queries that typically search a large portion of the database. Moreover, the OLTP systems are often critical for the operation of the organization and must not be in danger of corruption by other applications. 2. Logical interpretability problems. Inspired by the success of spreadsheet techniques, OLAP users tend to think in terms of highly structured multidimensional data models, whereas information sources offer at best relational, often just semistructured data models or even flat files. 3. Temporal and granularity mismatch. OLTP systems focus on current operational support in great detail, whereas OLAP often considers historical developments in a somewhat lesser detail.

Since the beginning of data warehousing in the early 1990s, an informal consensus has been reached concerning the major terms and components involved in data warehousing. In this chapter, we first explain the main terms and components. Data warehouse vendors are pursuing different strategies in supporting this basic framework. We review a few of the major product families and show in the next chapter a brief survey of the basic problem areas data warehouse practice and research is faced with today. These issues are then treated in more depth in the remainder of this book.

In this paper we present IBIS (Internet-Based Information System), a system for the semantic integration of heterogeneous data sources, which adopts innovative and state-of-the-art solutions to deal with all aspects of a complex data-integration environment, including query answering under integrity constraints and limitations on source access. IBIS is based on the global-as-view approach, using a relational mediated schema to query the data at the sources. Sources are wrapped so as to provide a relational view on them. A key issue is that the system allows the specification of integrity constraints (modeling constraints in the domain of interest) in the global schema. Since sources are autonomous, the extracted data in general do not satisfy the constraints. IBIS adapts and integrates the data extracted from the sources making use of the constraints in the global schema, so as to answer queries at best with the information available. IBIS deals with limitations in accessing data sources, and exploits techniques developed for querying sources with access limitations in order to retrieve the maximum set of answers. In particular, it may use integrity constraints available on the sources to improve the efficiency of the extraction process.

Source integration is one of the core problems in data warehousing. Two critical factors for the design and maintenance of applications requiring source integration, and in particular data warehouse applications, are conceptual modeling of the domain, and reasoning support over the conceptual representation. We present a novel approach to conceptual modeling for source integration, which allows for suitably modeling the global concepts of the application, the individual information sources, and the constraints among different sources. Our methodological framework relies on the reasoning services associated with the modeling formalism to support an incremental source integration phase within the data warehouse design process.

Class-based languages express knowledge in terms of objects and classes, and have inspired a huge number of formalisms in computer science. Description logics forma family of both class-based and logic-based knowledge representation languages which allow for modeling an application domain in terms of objects, classes and relationships between classes, and for reasoning about them. This paper presents an overview of the research carried out in the last years in description logics, with the main goal of illustrating how these logics provide the foundations for class-based knowledge representation formalisms.

Information integration is the problem of combining the data residing at different, heterogeneous sources, and providing the user with a unified view of these data, called mediated schema. The mediated schema is therefore a reconciled view of the information, which can be queried by the user. It is the task of the system to free the user from the knowledge on where data are, and how data are structured at the sources.In this chapter, we discuss data integration in general, and describe a logic-based approach to data integration. A logic of the Description Logics family is used to model the information managed by the integration system, to formulate queries posed to the system, and to perform several types of automated reasoning supporting both the modeling, and the query answering process. We focus, in particular, on a specific Description Logic, called DLR, specifically designed for database applications. In the chapter, we illustrate how DLR is used to model a mediated schema of an integration system, to specify the semantics of the data sources, and finally to support the query answering process by means of the associated reasoning methods.