Inamultidatabase system, schematic con icts between two objects are usually of interest only when the objects have some semantic similarity. We use the concept of semantic proximity, which is essentially an abstraction/mapping between the domains of the two objects associated with the context of comparison. An explicit though partial context representation is proposed and the speci city relationship between contexts is de ned. The contexts are organized as a meet semi-lattice and associated operations like the greatest lower bound (glb) are de ned. The context of comparison and the type of abstractions used to relate the two objects form the basis of a semantic taxonomy. Atthesemantic level, the intensional description of database objects provided by the context is expressed in a description logic language. Schema correspondences are used to store mappings from the semantic level to the data level and are associated with the respective contexts. Inferences about database content at the federation level are modeled as changes in the context and the associated schema correspondences. We try to reconcile the dual (schematic and semantic) perspecitves by: enumerating possible semantic similarities between objects having schema and data conicts, and modeling schema correspondences as the projection of semantic proximity wrt context. 1

Semantic similarity measures play an important role in information retrieval and information integration. Traditional approaches to modeling semantic similarity compute the semantic distance between definitions within a single ontology. This single ontology is either a domain-independent ontology or the result of the integration of existing ontologies. We present an approach to computing semantic similarity that relaxes the requirement of a single ontology and accounts for differences in the levels of explicitness and formalization of the different ontology specifications. A similarity function determines similar entity classes by using a matching process over synonym sets, semantic neighborhoods, and distinguishing features that are classified into parts, functions, and attributes. Experimental results with different ontologies indicate that the model gives good results when ontologies have complete and detailed representations of entity classes. While the combination of word matching and semantic neighborhood matching is adequate for detecting equivalent entity classes, feature matching allows us to discriminate among similar, but not necessarily equivalent, entity classes.

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Semantic integration has been a long-standing challenge for the database community. It has received steady attention over the past two decades, and has now become a prominent area of database research. In this article, we first review database applications that require semantic integration, and discuss the difficulties underlying the integration process. We then describe recent progress and identify open research issues. We will focus in particular on schema matching, a topic that has received much attention in the database community, but will also discuss data matching (e.g., tuple deduplication), and open issues beyond the match discovery context (e.g., reasoning with matches, match verification and repair, and reconciling inconsistent data values). For previous surveys of database research on semantic integration, see (Rahm & Bernstein 2001;

Ontologies in current computer science parlance are computer based resources that represent agreed domain semantics. Unlike data models, the fundamental asset of ontologies is their relative independence of particular applications, i.e. an ontology consists of relatively generic knowledge that can be reused by different kinds of applications/tasks. The first part of this paper concerns some aspects that help to understand the differences and similarities between ontologies and data models. In the second part we present an ontology engineering framework that supports and favours the genericity of an ontology. We introduce the DOGMA ontology engineering approach that separates “atomic ” conceptual relations from “predicative” domain rules. A DOGMA ontology consists of an ontology base that holds sets of intuitive context-specific conceptual relations and a layer of “relatively generic ” ontological commitments that hold the domain rules. This constitutes what we shall call the double articulation of a DOGMA ontology 1.

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.

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A large-scale interoperable database system operating in a dynamic environment should provide uniform access user interface to its components, scalability to larger networks, evolution of database schema and applications, flexible composability of client and server components, and preserve component autonomy. To address the research issues presented by such systems, we introduce the Distributed Interoperable Object Model (DIOM). DIOM's main features include the explicit repre- sentation of and access to semantics in data sources through the DIOM base interfaces, the use of interface abstraction mechanisms (such as specialization, generalization, aggregation and import) to support incremental design and construction of compound interoperation interfaces, the deferment of conflict resolution to the query submission time instead of at the time of schema integration, and a clean interface between distributed interoperable objects that supports the independent evolution and management of such objects. To make DIOM concrete, we outline the Diorama architecture, which includes important auxiliary services such as domain-specific library functions, object linking databases, and query decomposition and packaging strategies. Several practical examples and appli- cation scenarios illustrate the usefulness of DIOM.

This is the second of a two-part paper to review ontology research and development, in particular, ontology mapping and evolving. Ontology is defined as a formal explicit specification of a shared conceptualization. Ontology itself is not a static model so that it must have the potential to capture changes of meanings and relations. As such, mapping and evolving ontologies is part of an essential task of ontology learning and development. Ontology mapping is concerned with reusing existing ontologies, expanding and combining them by some means and enabling a larger pool of information and knowledge in different domains to be integrated to support new communication and use. Ontology evolving, likewise, is concerned with maintaining existing ontologies and extending them as appropriate when new information or knowledge is acquired. It is apparent from the reviews that current research into semi-automatic or automatic ontology research in all the three aspects of generation, mapping and evolving have so far achieved limited success. Expert

Abstract. We discuss the applicability of schema integration techniques developed for tightly-coupled database interoperation to interoperation of databases stemming from different modelling contexts. We illustrate that in such an environment, it is typically quite difficult to infer the real-world semantics of remote classes from their definition in remote databases. However, defining relationships between the real-world se-mantics of schema elements is essential in existing schema integration techniques. We propose to base database interoperation in such environ-ments on instance-level semantic relationships, to be defined using what we call object comparison rules. Both the local and the remote classifi-cations of the appropriately merged instances are maintained, allowing for the derivation of a global class hierarchy if desired. 1