2013
DOI: 10.1007/978-3-642-38288-8_44
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Semantic Web for the Humanities

Abstract: Researchers have been interested recently in publishing and linking Humanities datasets following Linked Data principles. This has given rise to some issues that complicate the semantic modelling, comparison, combination and longitudinal analysis of these datasets. In this research proposal we discuss three of these issues: representation roundtripping, concept drift, and contextual knowledge. We advocate an integrated approach to solve them, and present some preliminary results.

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Cited by 6 publications
(3 citation statements)
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“…There is a vast diversity of domains to publish LSD about, and lots of dimensions and codes can be heterogeneous, domain specific and hardly comparable [2,3,5,6]. To this end, QB allows users to mint their own URIs to create arbitrary dimensions and associated codes.…”
Section: Motivationmentioning
confidence: 99%
“…There is a vast diversity of domains to publish LSD about, and lots of dimensions and codes can be heterogeneous, domain specific and hardly comparable [2,3,5,6]. To this end, QB allows users to mint their own URIs to create arbitrary dimensions and associated codes.…”
Section: Motivationmentioning
confidence: 99%
“…There are many manuscripts transcribed to XML-using TEI-that can be converted to RDF. But transcribers are hesitant to deal with the underlying technology although they can benefit from it [26]. Those are the cases where generic approaches, as the one introduced here, can offer a solution and where automatic conversion of schemata has its place when transformations are to be checked.…”
Section: Introductionmentioning
confidence: 99%
“…Their scattered distribution on the Web, and their diversity in syntactically and semantically heterogeneous languages, hamper their use, integration, and potential (Meroño-Peñuela et al 2013). Moreover, the lack of explicitly and semantically meaningful links between these datasets -which very often share common resources and concepts -prevents an automatic and intelligent retrieval and use by applications that consume data.…”
Section: Introductionmentioning
confidence: 99%