Researchers in the ontology-design field have developed the content for ontologies in many domain areas. Recently, ontologies have become increasingly common on the WorldWide Web where they provide semantics for annotations in Web pages. This distributed nature of ontology development has led to a large number of ontologies covering overlapping domains. In order for these ontologies to be reused, they first need to be merged or aligned to one another. The processes of ontology alignment and merging are usually handled manually and often constitute a large and tedious portion of the sharing process. We have developed and implemented PROMPT, an algorithm that provides a semi-automatic approach to ontology merging and alignment. PROMPT performs some tasks automatically and guides the user in performing other tasks for which his intervention is required.

Data exchange is the problem of taking data structured under a source schema and creating an instance of a target schema that reflects the source data as accurately as possible. In this paper, we address foundational and algorithmic issues related to the semantics of data exchange and to query answering in the context of data exchange. These issues arise because, given a source instance, there may be many target instances that satisfy the constraints of the data exchange problem. We give an algebraic specification that selects, among all solutions to the data exchange problem, a special class of solutions that we call universal. A universal solution has no more and no less data than required for data exchange and it represents the entire space of possible solutions. We then identify fairly general, and practical, conditions that guarantee the existence of a universal solution and yield algorithms to compute a canonical universal solution efficiently. We adopt the notion of "certain answers" in indefinite databases for the semantics for query answering in data exchange. We investigate the computational complexity of computing the certain answers in this context and also study the problem of computing the certain answers of target queries by simply evaluating them on a canonical universal solution.

Ontology mapping is seen as a solution provider in today's landscape of ontology research. As the number of ontologies that are made publicly available and accessible on the Web increases steadily, so does the need for applications to use them. A single ontology is no longer enough to support the tasks envisaged by a distributed environment like the Semantic Web. Multiple ontologies need to be accessed from several applications. Mapping could provide a common layer from which several ontologies could be accessed and hence could exchange information in semantically sound manners. Developing such mappings has been the focus of a variety of works originating from diverse communities over a number of years. In this article we comprehensively review and present these works. We also provide insights on the pragmatics of ontology mapping and elaborate on a theoretical approach for defining ontology mapping.

A broad spectrum of data is available on the Web in distinct heterogeneous sources, and stored under different formats. As the number of systems that utilize this heterogeneous data grows, the importance of data translation and conversion mechanisms increases greatly. In this paper we present a new translation system, based on schemamatching, aimed to simplify the intricate task of data conversion. We observe that in many cases the schema of the data in the source system is very similar to the that of the target system. In such cases, much of the translation work can be done automatically, based on the schemas similarity. This saves a lot of effort for the user, limiting the amount of programming needed. We define common schema and data models, in which schemas and data (resp.) from many common models can be represented. Using a rulebased method, the source schema is compared with the target one, and each component in the source schema is matched with a corresponding compone...

Due to the development of the World Wide Web, the integration of heterogeneous data sources has become a major concern of the database community. Appropriate architectures and query languages have been proposed. Yet, the problem of data conversion which is essential for the development of mediators/wrappers architectures has remained largely unexplored. In this paper, we present the YAT system for data conversion. This system provides tools for the specification and the implementation of data conversions among heterogeneous data sources. It relies on a middleware model, a declarative language, a customization mechanism and a graphical interface. The model is based on named trees with ordered and labeled nodes. Like semistructured data models, it is simple enough to facilitate the representation of any data. Its main originality is that it allows to reason at various levels of representation. The YAT conversion language (called YATL) is declarative, rule-based and features enhanced patt...

We propose a novel approach to the view update problem for tree-structured data: a domainspecific programming language in which all expressions denote bi-directional transformations on trees. In one direction, these transformations—dubbed lenses—map a “concrete ” tree into a simplified “abstract view”; in the other, they map a modified abstract view, together with the original concrete tree, to a correspondingly modified concrete tree. Our design emphasizes both robustness and ease of use, guaranteeing strong well-behavedness and totality properties for welltyped lenses. We identify a natural mathematical space of well-behaved bi-directional transformations over arbitrary structures, study definedness and continuity in this setting, and state a precise connection with the classical theory of “update translation under a constant complement ” from databases. We then instantiate this semantic framework in the form of a collection of lens combinators that can be assembled to describe transformations on trees. These combinators include familiar constructs from functional programming (composition, mapping, projection, conditionals, recursion) together with some novel primitives for manipulating trees (splitting, pruning, copying, merging, etc.). We illustrate the expressiveness of these combinators by developing a number of bi-directional listprocessing transformations as derived forms. An extended example shows how our combinators can be used to define a lens that translates between a native HTML representation of browser bookmarks and a generic abstract bookmark format.

As ontology development becomes a more ubiquitous and collaborative process, the developers face the problem of maintaining versions of ontologies akin to maintaining versions of software code in large software projects. Versioning systems for software code provide mechanisms for tracking versions, checking out versions for editing, comparing different versions, and so on. We can directly reuse many of these mechanisms for ontology versioning. However, version comparison for code is based on comparing text files---an approach that does not work for comparing ontologies. Two ontologies can be identical but have different text representation. We have

Introduction The Web is emerging as a universal data repository, offering access to sources whose data organization varies from strictly structured databases to almost completely unstructured pages, and everything in between. Consequently, much research has recently focused on data integration and data translation systems [10, 6, 9, 8, 17, 13, 2, 19], whose goals are to allow applications to utilize data from many sources, with possibly widely varying formats. These research efforts have established a common data model of semistructured data, for uniformly representing data from any source. Recently, however, it is being realized that having a common schema model is also beneficial, and even necessary, in translation and integration systems to support tasks such as query formulation, decomposition and optimization, or declarative specification of data translation. As an example, which we use for motivation throughout the paper, recently suggested tools for data translation [2, 11, 19

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.

As researchers in the ontology-design field develop the content of a growing number of ontologies, the need for sharing and reusing this body of knowledge becomes increasingly critical. Aligning and merging existing ontologies, which is usually handled manually, often constitutes a large and tedious portion of the sharing process. We have developed SMART, an algorithm that provides a semi-automatic approach to ontology merging and alignment. SMART assists the ontology developer by performing certain tasks automatically and by guiding the developer to other tasks for which his intervention is required. SMART also determines possible inconsistencies in the state of the ontology that may result from the user's actions, and suggests ways to remedy these inconsistencies. We define the set of basic operations that are performed during merging and alignment of ontologies, and determine the effects that invocation of each of these operations has on the process. SMART is based on an extremely general knowledge model and, therefore, can be applied across various platforms.