Research on ontology is becoming increasingly widespread in the computer science community, and its importance is being recognized in a multiplicity of research fields and application areas, including knowledge engineering, database design and integration, information retrieval and extraction. We shall use the generic term information systems, in its broadest sense, to collectively refer to these application perspectives. We argue in this paper that so-called ontologies present their own methodological and architectural peculiarities: on the methodological side, their main peculiarity is the adoption of a highly interdisciplinary approach, while on the architectural side the most interesting aspect is the centrality of the role they can play in an information system, leading to the perspective of ontology-driven information systems.

this paper is to describe the MOMIS [4, 5] (Mediator envirOnment for Multiple Information Sources) approach to the integration and query of multiple, heterogeneous information sources, containing structured and semistructured data. MOMIS has been conceived as a joint collaboration between University of Milano and Modena in the framework of the INTERDATA national research project, aiming at providing methods and tools for data management in Internet-based information systems. Like other integration projects [1, 10, 14], MOMIS follows a "semantic approach" to information integration based on the conceptual schema, or metadata, of the information sources, and on the following architectural elements: i) a common object-oriented data model, defined according to the ODL I 3 language, to describe source schemas for integration purposes. The data model and ODL I 3 have been defined in MOMIS as subset of the ODMG-93 ones, following the proposal for a standard mediator language developed by the I

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.

Systems and infrastructures are currently being developed to support Web services. The main idea is to encapsulate an organization’s functionality within an appropriate interface and advertise it as Web services. While in some cases Web services may be utilized in an isolated form, it is normal to expect Web services to be integrated as part of workflow processes. The composition of workflow processes that model e-service applications differs from the design of traditional workflows, in terms of the number of tasks (Web services) available to the composition process, in their heterogeneity, and in their autonomy. Therefore, two problems need to be solved: how to efficiently discover Web services – based on functional and operational requirements – and how to facilitate the interoperability of heterogeneous Web services. In this paper, we present a solution within the context of the emerging Semantic Web, that includes use of ontologies to overcome some of the problems. We start by illustrating the steps involved in the composition of a workflow. Two of these steps are the discovery of Web services and their posterior integration into a workflow. To assist designers with those two steps, we have devised an algorithm to simultaneously discover Web services and resolve heterogeneity among their interfaces and the workflow host. Finally, we describe a prototype that has been implemented to illustrate how discovery and interoperability functions are achieved.

This paper introduces a geographic information system architecture based on ontologies. Ontology plays a central role in the definition of all aspects and components of an information system in the so-called ontology-driven information systems. The system presented here uses a container of interoperable geographic objects. The objects are extracted from multiple independent data sources and are derived from a strongly typed mapping of classes from multiple ontologies. This approach provides a great level of interoperability and allows partial integration of information when completeness is impossible.

Semantic similarity plays an important role in geographic information systems as it supports the identification of objects that are conceptually close, but not identical. Similarity assessments are particularly important for retrieval of geospatial data in such settings as digital libraries, heterogeneous databases, and the World Wide Web. Although some computational models for semantic similarity assessment exist, these models are typically limited by their inability to handle such important cognitive properties of similarity judgments as their inherent asymmetry and their dependence on context. This paper defines the Matching-Distance Similarity Measure (MDSM) for determining semantic similarity among spatial entity classes, taking into account the distinguishing features of these classes (parts, functions, and attributes) and their semantic interrelations (is-a and part-whole relations). A matching process is combined with a semantic-distance calculation to obtain asymmetric values of similarity that depend on the degree of generalization of entity classes. MDSM's matching process is also driven by contextual considerations, where the context determines the relative importance of distinguishing features. Based on a human-subject experiment, MDSM results correlate well with people's judgments of similarity. When contextual information is used for determining the importance of distinguishing features, this correlation increases; however, the major component of the correlation between MDSM results and people's judgments is due to a detailed definition of entity classes.

Abstract. Ontology translation is one of the most difficult problems that webbased agents must cope with. An ontology is a formal specification of a vocabulary, including axioms relating its terms. Ontology translation is best thought of in terms of ontology merging. The merge of two related ontologies is obtained by taking the union of the terms and the axioms defining them. We add bridging axioms not only as “bridges ” between terms in two related ontologies but also to make this merge into a complete new ontology for further merging with other ontologies. Translation is implemented using an inference engine (OntoEngine), running in either a demand-driven (backwardchaining) or data-driven (forward chaining) mode. We illustrate our method by describing its application in an online ontology translation system, OntoMerge, which translates a dataset in the DAML notation to a new DAML dataset that captures the same information, but in a different ontology. A uniform internal representation, Web-PDDL is used for representing merged ontologies and datasets for automated reasoning. 1.

Abstract The presents the TuCSoN coordination model for Internet applications based on network-aware (possibly mobile) agents. The model is based on the notion of tuple centre, an enhanced tuple space whose behaviour can be extended according to the application needs. Every node of a TuCSoN environment provides its local communication space, made up of a multiplicity of independently-programmable tuple centres. This makes it possible both to embed global system properties into the space of components ' interaction, thus enabling flexible cooperation over space and time between agents and permitting to easily face many issues critical to Internet applications, such as heterogeneity and dynamicity of the execution environments.

Interoperability and integration of data sources are becoming ever more important issues as both, the amount of data and the number of data producers are growing. Interoperability not only has to resolve the differences in data structures, it also has to deal with semantic heterogeneity. Semantics refer to the meaning of data in contrast to syntax, which only defines the structure of the schema items (e.g., classes and attributes). We focus on the part of semantics related to the meanings of the terms used as identifiers in schema definitions. This paper presents an approach to integrate schemas from different communities, where each such community is using its own ontology. The approach is based on merging ontologies based on similarity relations among concepts of different ontologies. We present formal definitions of similarity relations based on intensional definitions and conclude the extensional consequences. The process of merging ontologies based on the detected similarity relations is discussed. The merged ontology is finally used to derive an integrated schema. The resulting schema can be used as the global schema in a federated database system.

The advent of web services that use XML-based message exchanges has spurred many eorts that address issues related to inter-enterprise service electronic commerce interactions. Currently emerging standards and technologies enable enterprises to describe and advertise their own Web Services and to discover and determine how to interact with services fronted by other businesses. However, these technologies do not address the problem of how to reconcile structural dierences between similar types of documents supported by dierent enterprises. Transformations between such documents must thus be created manually on a case-by-case basis. In this paper, we explore the problem of how to automate the transformation of XML E-business documents. We develop an integrated solution that automates as much as possible all steps of the document transformation process. One, we propose a set of schema transformation operations that establish semantic relationships between two XML document schemas. Two, we dene a model that allows us to compare the cost of performing these operations. Three, we introduce an algorithm that discovers an ecient sequence of operations for transforming a source document schema into a target document schema based on our cost model. The operation sequence then is used to generate an equivalent XSLT transformation script. Experimental results indicate that our algorithm can satisfactorily discover acceptable transformations. 1.