be inserted by the editor)

Abstract. In recent years ontologies – shared conceptualizations of some domain – are increasingly seen as the key to further automation of information processing. Although many approaches for representing and applying ontologies have already been devised, they haven’t found their way into enterprise applications. In this paper we argue that ontology-based systems lack critical technical features, such as scalability, reliability, concurrency and integration with existing data sources, as well as the support for modularization and meta-concept modeling from the conceptual modeling perspective. We present a conceptual modeling approach that balances some of the trade-offs to more easily integrate into existing enterprise information infrastructure. Our approach is implemented within KAON, the Karlsruhe Ontology and Semantic Web tool suite. 1

Abstract: Ontologging is an ontology-driven environment to enable next generation knowledge management applications building on Semantic Web technology. In this paper we first present the conceptual architecture underlying Ontologging. Second, we focus on two important challenges for ontology-based knowledge management, namely the supporting multiple ontologies and managing ontology evolution. We will provide a general approach for handling these two essential issues within the Ontologging architecture. Key words: Knowledge management, ontology mapping, ontology evolution. 1.

Ontology mapping is the process whereby two ontologies are semantically related at conceptual level and the source ontology instances are transformed into target ontology entities according to those semantic relations. Ontology mapping faces new challenges in the context of Semantic Web, especially concerning heterogeneity, dynamics, distribution and limitations on representation technology. This paper introduces a new methodology and transformation process based on the notion of Service, which represents system transformation capabilities. MAFRA Toolkit is a specific implementation of MAFRA-Mapping FRAmework, where these new methodology and transformation process are being validated. MAFRA Toolkit is being applied in the European project Harmonise, which aims to provide solutions for (semi-) automatic interoperability between major operators in tourism e-business. MAFRA plays a major role in the specification, representation and reconciliation phases of the semantic mapping within the scope of the Harmonise technology. 1.

Abstract — In this paper we formally define the problem of automatic detection of thematic categories in a semantically indexed document, and identify the main obstacles to overcome in this process. Furthermore, we explain how detection of thematic categories can be achieved, with the use of a fuzzy quasi-taxonomic relation. Our approach relies on a fuzzy hierarchical clustering algorithm; this algorithm uses a similarity measure that is based on the notion of context. I.

Existing literature in the field of transitive relations focuses mainly on dense, Boolean, undirected relations. With the emergence of a new area of intelligent retrieval, where sparse transitive fuzzy ordering relations are utilized, existing theory and methodologies need to be extended, as to cover the new needs. This paper discusses the incremental update of such fuzzy binary relations, while focusing on both storage and computational complexity issues. Moreover, it proposes a novel transitive closure algorithm that has a remarkably low computational complexity (below O(n²)) for the average sparse relation; such are the relations encountered in intelligent retrieval.

Abstract. The use of different modeling languages in software development makes their integration a must. Most existing integration approaches are metamodel-based with these metamodels representing both an abstract syntax of the corresponding modeling language and also a data structure for storing models. This implementation specific focus, however, does not make explicit certain language concepts, which can complicate integration tasks. Hence, we propose a process which semi-automatically lifts metamodels into ontologies by making implicit concepts in the metamodel explicit in the ontology. Thus, a shift of focus from the implementation of a certain modeling language towards the explicit reification of the concepts covered by this language is made. This allows matching on a solely conceptual level, which helps to achieve better results in terms of mappings that can in turn be a basis for deriving implementation specific transformation code. 1

Abstract- Semantic integration remains a serious challenge in the context of the Semantic Web. Current research aims to solve this issue through the development of ontology mapping processes. However, there is a lack of rules and guidance for the use of those processes. This paper introduces an ontology mapping framework which covers the entire ontology mapping process from mapping discovery to mapping execution. The goal is to provide a basic rule set to guide future ontology mapping development that is generic enough to apply to the largest number of ontology mapping scenarios.

Abstract—In the last few years, numerous multimedia archives have made extensive use of digitized storage and annotation technologies. Still, the development of single points of access, providing common and uniform access to their data, despite the efforts and accomplishments of standardization organizations, has remained an open issue as it involves the integration of various large-scale heterogeneous and heterolingual systems. This paper describes a mediator system that achieves architectural integration through an extended three-tier architecture and content integration through semantic modeling. The described system has successfully integrated five multimedia archives, quite different in nature and content from each other, while also providing easy and scalable inclusion of more archives in the future. Index Terms—Architectural integration, concept taxonomy, document analysis, mediator, message-oriented middleware, MPEG-7, multimedia archives, semantic modeling, three-tier architecture. I.

Abstract. The notion of context plays a significant role in multimedia content search and retrieval systems. In this paper we focus our research efforts on a visual context knowledge representation, to be utilized for multimedia high-level concept detection. We propose and describe in detail types of contextual relations evident within the multimedia content, model them and provide a clear methodology on how to extract them. A visual context ontology is introduced, containing relations among different types of content entities, such as images, regions, region types and high-level concepts. In this manner, we facilitate traditional object detection approaches towards semantical interpretation. The application of the proposed knowledge structure provides encouraging initial results, improving the efficacy of related multimedia analysis techniques. 1