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.

This paper deals with the problem of handling semantic heterogeneity during schema integration. Semantics refer to the meaning of data in contrast to syntax, which solely defines the structure of schema elements. We focus on the part of semantics related to the meanings of terms used to name schema elements. Our approach does not rely on the names of the schema elements or the structure of the schema. Instead, we present an approach based on formal ontologies presented in a logical language for integrating schemas to produce global schemas. Semantic similarity relations between definitions in formal ontologies are defined and used for merging formal ontologies. We show how similarity relations are discovered by a reasoning system using a higher level ontology. The result of the merging process is used for schema integration. The result of schema integration can be used as a global schema in a tightly coupled federated database. Afterwards, we illustrate how the produced global schema can help for the mapping of data elements.

If the word ontology is more and more used in a number of domain, the capabilities and benefits of ontology for Information Systems management are still unclear. Therefore, the usage of ontology-based Information Systems in industry and services is not widespread. This paper analyses the concept of a domain ontology from a database perspective. As a result, firstly, we provide three criteria that distinguish domain ontology from other existing domain modeling approach which lead us to propose a new definition of domain ontologies. Secondly, based on the various approaches of ontology modeling followed by different communities, we propose a taxonomy of domain ontology. We show how they may be organized into a layered model, called the onion model, allowing to design and to use the capabilities of each category of ontology in anintegrated environment. Finally, this paper presents several information systems based on ontology technologies and describe the kinds of services that should be provided to allow a powerful usage of ontology in data management.

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Developing intelligent systems to integrate numerous heterogeneous data sources in order to give end users an uniform query interface is a great challenging issue. The process of constructing a global schema of the integrated system is usually done manually. This is because of the mechanisms of solving different conflicts existing in an heterogeneous context. Even more these data sources do not contain enough knowledge to help solving these conflicts and then generating the global schema. In this paper, we present a new approach to integrate heterogeneous sources called a priori approach for data integration. The originality of our approach is that each data source participating in the integration process contains an ontology that defines the meaning of its own data using an ontology. This approach ensures the automation of the integration process when (1) all data sources have the same ontology, and (2) all sources reference a shared ontology, and each one can extent this ontology by adding new concepts. Finally, we present integration algorithms for the previous cases using the PLIB ontology model.

Semantic integration is a complex process influenced by different factors or characteristics. The problem is further complicated by the confusion concerning many issues entailed in the integration process. The present paper (a) analyzes and compares existing integration approaches through the adoption of an ontological framework and (b) describes the principal directions to perform semantic integration of geographic ontologies. In order to make the above analysis more comprehensive, a scenario is demonstrated, which involves the semantic integration of two terminological geographic ontologies. The aim of the paper is to assist the user in following the proper procedure and selecting the appropriate approach in an integration endeavour. The focus is put on semantic integration of geographic ontologies. 1.

Rapidity of decision making process is an important factor for different areas of human life (business, healthcare, industry, military applications, etc.). Since responsible persons make decisions using available knowledge a delivery of necessary and timely information for knowledge management systems is important. Knowledge logistics is a new direction of knowledge management addressing this issue. It provides a set of activities for knowledge search, acquisition and integration from distributed sources located in information grid environment. The paper proposes a developed Knowledge Source Network approach (KSNet-approach) to knowledge logistics and its multi-agent architecture, and describes a research prototype of the system “KSNet ” based on this approach.

this paper, while the former part is still subject of research (e.g., heterogeneity of OODB schemas and RDB schemas) it will not be discussed here

Some of the most outstanding problems in Computer Science (e.g. access to heterogeneous information sources, use of different e-commerce standards, ontology translation, etc.) are often approached through the identification of ontology mappings. A manual mapping generation slows down, or even makes unfeasible, the solution of particular cases of the aforementioned problems via ontology mappings. Some algorithms and formal models for partial tasks of automatic generation of mappings have been proposed. However, an integrated system to solve this problem is still missing. In this paper, we present AMON, a platform for automatic ontology mapping generation. First of all, we show the general structure. Then, we describe the current version of the system, including the ontology in which it is based, the similarity measures that it uses, the access to external sources, etc.

Abstract – An efficient knowledge sharing between multiple participating parties is required to provide for situation awareness and consequently to manage any OOTW operation. Thereby it is necessary that the right knowledge from distributed sources is integrated and transferred to the right person within the right context at the right time to the right purpose. The aggregate of these interrelated activities is referred to as knowledge logistics (KL). The paper presents an approach that implements KL for an intelligent support of OOTW. Within the approach KL is coupled with information fusion based on constraint satisfaction methodology. Health service logistics is here considered as one of the approach applications what is illustrated via a case study of on-the-fly portable hospital configuration.