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Abstract

A large on-going activity for digitization, dissemination and preservation of cultural heritage is taking place in Europe, United States and the world, which involves all types of cultural institutions, i.e., galleries, libraries, museums, archives and all types of cultural content. The development of Europeana, as a single point of access to European Cultural Heritage, has probably been the most important result of the activities in the field till now. Semantic interoperability, linked open data, user involvement and user generated content are key issues in these developments. This paper presents a system that provides content providers and users the ability to map, in an effective way, their own metadata schemas to common domain standards and the Europeana (ESE, EDM) data models. The system is currently largely used by many European research projects and the Europeana. Based on these mappings, semantic query answering techniques are proposed as a means for effective access to digital cultural heritage, providing users with content enrichment, linking of data based on their involvement and facilitating content search and retrieval. An experimental study is presented, involving content from national content aggregators, as well as thematic content aggregators and the Europeana, which illustrates the proposed system

Type

a

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Abstract

In this document we provide an overall view of the state of the art in ontology alignment. It is organised as a description of the need for ontology alignment, a presentation of the techniques currently in use for ontology alignment and a presentation of existing systems. The state of the art is not restricted to any discipline and consider as some form of ontology alignment the work made on schema matching within the database area for instance. Heterogeneity problems on the semantic web can be solved, for some of them, by aligning heterogeneous ontologies. This is illustrated through a number of use cases of ontology alignment. Aligning ontologies consists of providing the corresponding entities in these ontologies. This process is precisely defined in deliverable D2.2.1. The current deliverable presents the many techniques currently used for implementing this process. These techniques are classified along the many features that can be found in ontologies (labels, structures, instances, semantics). They resort to many different disciplines such as statistics, machine learning or data analysis. The alignment itself is obtained by combining these techniques towards a particular goal (obtaining an alignment with particular features, optimising some criterion). Several combination techniques are also presented. Finally, these techniques have been experimented in various systems for ontology alignment or schema matching. Several such systems are presented briefly in the last section and characterized by the above techniques they rely on. The conclusion is that many techniques are available for achieving ontology alignment and many systems have been developed based on these techniques. However, few comparisons and few integration is actually provided by these implementations. This deliverable serves as a basis for considering further action along these two lines. It provide a first inventory of what should be evaluated and suggests what evaluation criterion can be used.

Content

This document is part of a research project funded by the IST Programme of the Commission of the European Communities as project number IST-2004-507482.

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Type

a