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Abstract

This paper gives a comprehensive overview over the problem of Semantic Interoperability in the Cultural Heritage domain, with a particular focus on solutions centered around extensional, i.e., instance-based, ontology matching methods. It presents three typical scenarios requiring interoperability, one with homogenous collections, one with heterogeneous collections, and one with multi-lingual collection. It discusses two different ways to evaluate potential alignments, one based on the application of re-indexing, one using a reference alignment. To these scenarios we apply extensional matching with different similarity measures which gives interesting insights. Finally, we firmly position our work in the Cultural Heritage context through an extensive discussion of the relevance for, and issues related to this specific field. The findings are as unspectacular as expected but nevertheless important: the provided methods can really improve interoperability in a number of important cases, but they are not universal solutions to all related problems. This paper will provide a solid foundation for any future work on Semantic Interoperability in the Cultural Heritage domain, in particular for anybody intending to apply extensional methods.

Type

a

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Abstract

Most libraries and other cultural heritage institutions use controlled knowledge organisation systems, such as thesauri, to describe their collections. Unfortunately, as most of these institutions use different such systems, united access to heterogeneous collections is difficult. Things are even worse in an international context when concepts have labels in different languages. In order to overcome the multilingual interoperability problem between European Libraries, extensive work has been done to manually map concepts from different knowledge organisation systems, which is a tedious and expensive process. Within the TELplus project, we developed and evaluated methods to automatically discover these mappings, using different ontology matching techniques. In experiments on major French, English and German subject heading lists Rameau, LCSH and SWD, we show that we can automatically produce mappings of surprisingly good quality, even when using relatively naive translation and matching methods.