Abstract not found

This paper aims at analyzing the key trends and challenges of the ontology matching field. The main motivation behind this work is the fact that despite many component matching solutions that have been developed so far, there is no integrated solution that is a clear success, which is robust enough to be the basis for future development, and which is usable by non expert users. In this paper we first provide the basics of ontology matching with the help of examples. Then, we present general trends of the field and discuss ten challenges for ontology matching, thereby aiming to direct research into the critical path and to facilitate progress of the field.

The need to understand and manage provenance arises in almost every scientific application. In many cases, information about provenance constitutes the proof of correctness of results that are generated by scientific applications. It also determines the quality and amount of trust one places on the results. For these reasons, the knowledge of provenance of a scientific result is typically regarded to be as important as the result itself. In this paper, we provide an overview of research in provenance in databases and discuss some future research directions. The content of this paper is largely based on the tutorial presented at SIGMOD 2007 [11].

Translating data and data access operations between applications and databases is a longstanding data management problem. We present a novel approach to this problem, in which the relationship between the application data and the persistent storage is specified using a declarative mapping, which is compiled into bidirectional views that drive the data transformation engine. Expressing the application model as a view on the database is used to answer queries, while viewing the database in terms of the application model allows us to leverage view maintenance algorithms for update translation. This approach has been implemented in a commercial product. It enables developers to interact with a relational database via a conceptual schema and an object-oriented programming surface. We outline the implemented system and focus on the challenges of mapping compilation, which include rewriting queries under constraints and supporting non-relational constructs. Categories and Subject Descriptors:

A schema mapping is a specification that describes how data from a source schema is to be mapped to a target schema. Once the data has been transferred from the source to the target, a natural question is whether one can undo the process and recover the initial data, or at least part of it. In fact, it would be desirable to find a reverse schema mapping from target to source that specifies how to bring the exchanged data back. In this paper, we introduce the notion of a recovery of a schema mapping: it is a reverse mapping M ′ for a mapping M that recovers sound data with respect to M. We further introduce an order relation on recoveries. This allows us to choose mappings that recover the maximum amount of sound information. We call such mappings maximum recoveries. We study maximum recoveries in detail, providing a necessary and sufficient condition for their existence. In particular, we prove that maximum recoveries exist for the class of mappings specified by FO-TO-CQ source-to-target dependencies. This class subsumes the class of source-to-target tuple-generating dependencies used in previous work on data exchange. For the class of mappings specified by FO-TO-CQ dependencies, we provide an exponential-time algorithm for computing maximum recoveries, and a simplified version for full dependencies that works in quadratic time. We also characterize the language needed to express maximum recoveries, and we include a detailed comparison with the notion of inverse (and quasi-inverse) mapping previously proposed in the data exchange literature. In particular, we show that maximum recoveries strictly generalize inverses. We finally study the complexity of some decision problems related to the notions of recovery and maximum recovery.

A schema mapping is a high-level specification that describes the relationship between two database schemas. As schema mappings constitute the essential building blocks of data exchange and data integration, an extensive investigation of the foundations of schema mappings has been carried out in recent years. Even though several different aspects of schema mappings have been explored in considerable depth, the study of schema-mapping optimization remains largely uncharted territory to date. In this paper, we lay the foundation for the development of a theory of schema-mapping optimization. Since schema mappings are constructs that live at the logical level of information integration systems, the first step is to introduce concepts and to develop techniques for transforming schema mappings to “equivalent ” ones that are more manageable from the standpoint of data exchange or of some other data interoperability task. In turn, this has to start by introducing and studying suitable notions of “equivalence ” between schema mappings. To this effect, we introduce the concept of dataexchange equivalence and the concept of conjunctive-query equivalence. These two concepts of equivalence are natural relaxations of the classical notion of logical equivalence; the first captures indistinguishability for data-exchange purposes, while the second captures indistinguishability for conjunctive-query-answering purposes. Moreover, they coincide with logical equivalence on schema mappings specified by source-to-target tuple-generating dependencies (s-t tgds), but differ on richer classes of dependencies, such as second-order tuple-generating dependencies (SO tgds) and sets of s-t tgds and target tuple-generating dependencies (target tgds). After exploring the basic properties of these three notions of equivalence between schema mappings, we focus on the following question: under what conditions is a schema mapping conjunctivequery equivalent to a schema mapping specified by a finite set of s-t tgds? We answer this question by obtaining complete characteriza-

The amount of information produced in the world increases by 30 % every year and this rate will only go up. With advanced network technology, more and more sources are available either over the Internet or in enterprise intranets. Modern data management applications, such as setting up Web portals, managing enterprise data, managing community data, and sharing scientific data, often require integrating available data sources and providing a uniform interface for users to access data from different sources; such requirements have been driving fruitful research on data integration over the last two decades [11, 13]. Data integration systems face two folds of challenges. First, data from disparate sources are often heterogeneous. Heterogeneity can exist at the schema level, where different data sources often describe the same domain using different schemas; it can also exist at the instance level, where different sources can represent the same real-world entity in different ways. There has been rich body of work on resolving heterogeneity in data, including, at the schema level, schema mapping and matching [14], model management [1], answering queries using views [12], data exchange [8], and at the instance level, record linkage (entity resolution, object matching, reference linkage, etc.) [7, 15], string similarity comparison [4], etc. Second, different sources can provide conflicting data. Conflicts can arise because of incomplete data, erroneous data, and out-of-date data. Returning incorrect data in a query result can be misleading and even harmful: one may contact a person by an out-of-date phone number, visit a clinic at a wrong address, and even make poor business decisions. It is thus critical for data integration systems to resolve conflicts from various sources and identify true values. This problem becomes especially prominent with the ease of publishing and spreading false information on the Web. This tutorial focuses on data fusion, which addresses the second challenge by fusing records on the same real-world entity into a single record and resolving possible conflicts from different data sources. Data fusion plays an important

Abstract. Ontology mapping is the key to data interoperability in the semantic web. This problem has received a lot of research attention, however, the research emphasis has been mostly devoted to automating the mapping process, even though the creation of mappings often involve the user. As industry interest in semantic web technologies grows and the number of widely adopted semantic web applications increases, we must begin to support the user. In this paper, we combine data gathered from background literature, theories of cognitive support and decision making, and an observational case study to propose a theoretical framework for cognitive support in ontology mapping tools. We also describe a tool called COGZ that is based on this framework. 1

In the study of data exchange one usually assumes an open-world semantics, making it possible to extend instances of target schemas. An alternative closed-world semantics only moves ‘as much data as needed’ from the source to the target to satisfy constraints of a schema mapping. It avoids some of the problems exhibited by the open-world semantics, but limits the expressivity of schema mappings. Here we propose a mixed approach: one can designate different attributes of target schemas as open or closed, to combine the additional expressivity of the open-world semantics with the better behavior of query answering in closed worlds. We define such schema mappings, and show that they cover a large space of data exchange solutions with two extremes being the known open and closed-world semantics. We investigate the problems of query answering and schema mapping composition, and prove two trichotomy theorems, classifying their complexity based on the number of open attributes. We find conditions under which schema mappings compose, extending known results to a wide range of closed-world mappings. We also provide results for restricted classes of queries and mappings guaranteeing lower complexity.

Abstract — A fundamental problem in information integration is that of designing the relationships, called schema mappings, between two schemas. The specification of a semantically correct schema mapping is typically a complex task. Automated tools can suggest potential mappings, but few tools are available for helping a designer understand mappings and design alternative mappings. We describe Muse, a mapping design wizard that uses data examples to assist designers in understanding and refining a schema mapping towards the desired specification. We present novel algorithms behind Muse and show how Muse systematically guides the designer on two important components of a mapping design: the specification of the desired grouping semantics for sets of data and the choice among alternative interpretations for semantically ambiguous mappings. In every component, Muse infers the desired semantics based on the designer’s actions on a short sequence of small examples. Whenever possible, Muse draws examples from a familiar database, thus facilitating the design process even further. We report our experience with Muse on some publicly available schemas. I.