Ruben Verborgh’s data

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Matches in dataset index for { ?s ?p "Digital publications host a large amount of data that currently is not harvested, due to its unstructured nature. However, manually annotating these publications is tedious. Current tools that automatically analyze unstructured text are too fine-grained for larger amounts of text such as books. A workable machine-interpretable version of larger bodies of text is thus necessary. In this paper, we therefore suggest a workflow to automatically create and publish a machine-interpretable version of digital publications as linked data via DBpedia Spotlight. Furthermore, we make use of the Everything is Connected Engine on top of this published linked data to link digital publications using a Web application dubbed “StoryBlink”. StoryBlink shows the added value of publishing machine-interpretable content of unstructured digital publications by finding relevant books that are connected to selected classic works. Currently, the time to find a connecting path can be quite long, but this can be overcome by using caching mechanisms, and the relevancy of found paths can be improved by better denoising the DBpedia Spotlight results, or by using alternative disambiguation engines."@en }

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