Search (385 results, page 20 of 20)

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Isbn

978-1-555-70746-0

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Isbn

978-0-262-52851-1

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Footnote

https://app.uff.br/riuff/handle/1/13904.

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Abstract

In the last few years, the size of Linked Open Data (LOD) describing artworks, in general or domain-specific Knowledge Graphs (KGs), is gradually increasing. This provides (art-)historians and Cultural Heritage professionals with a wealth of information to explore. Specifically, structured data about iconographical and iconological (icon) aspects, i.e. information about the subjects, concepts and meanings of artworks, are extremely valuable for the state-of-the-art of computational tools, e.g. content recognition through computer vision. Nevertheless, a data quality evaluation for art domains, fundamental for data reuse, is still missing. The purpose of this study is filling this gap with an overview of art-historical data quality in current KGs with a focus on the icon aspects. Design/methodology/approach This study's analyses are based on established KG evaluation methodologies, adapted to the domain by addressing requirements from art historians' theories. The authors first select several KGs according to Semantic Web principles. Then, the authors evaluate (1) their structures' suitability to describe icon information through quantitative and qualitative assessment and (2) their content, qualitatively assessed in terms of correctness and completeness. Findings This study's results reveal several issues on the current expression of icon information in KGs. The content evaluation shows that these domain-specific statements are generally correct but often not complete. The incompleteness is confirmed by the structure evaluation, which highlights the unsuitability of the KG schemas to describe icon information with the required granularity. Originality/value The main contribution of this work is an overview of the actual landscape of the icon information expressed in LOD. Therefore, it is valuable to cultural institutions by providing them a first domain-specific data quality evaluation. Since this study's results suggest that the selected domain information is underrepresented in Semantic Web datasets, the authors highlight the need for the creation and fostering of such information to provide a more thorough art-historical dimension to LOD.

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Abstract

Metadata is not monolithic. Instead, it is helpful to think of metadata as multiple views that can be projected from a single information object. Such views can form the basis of customized information services, such as search engines. Multiple views -- different types of metadata associated with a Web resource -- can facilitate a "drill-down" search paradigm, whereby people start their searches at a high level and later narrow their focus using domain-specific search categories. In Figure 1, for example, Mona Lisa may be viewed from the perspective of non-specialized searchers, with categories that are valid across domains (who painted it and when?); in the context of a museum (when and how was it acquired?); in the geo-spatial context of a walking tour using mobile devices (where is it in the gallery?); and in a legal framework (who owns the rights to its reproduction?). Multiple descriptive views imply a modular approach to metadata. Modularity is the basis of metadata architectures such as the Resource Description Framework (RDF), which permit different communities of expertise to associate and maintain multiple metadata packages for Web resources. As noted elsewhere, static association of multiple metadata packages with resources is but one way of achieving modularity. Another method is to computationally derive order-making views customized to the current needs of a client. This paper examines the evolution and scope of the Dublin Core from this perspective of metadata modularization. Dublin Core began in 1995 with a specific goal and scope -- as an easy-to-create and maintain descriptive format to facilitate cross-domain resource discovery on the Web. Over the years, this goal of "simple metadata for coarse-granularity discovery" came to mix with another goal -- that of community and domain-specific resource description and its attendant complexity. A notion of "qualified Dublin Core" evolved whereby the model for simple resource discovery -- a set of simple metadata elements in a flat, document-centric model -- would form the basis of more complex descriptions by treating the values of its elements as entities with properties ("component elements") in their own right.

Source

D-Lib magazine. 7(2001) no.1, xx S