Matches in Ruben’s data for { <https://dx.doi.org/10.1007/978-3-319-98192-5_40> ?p ?o }
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- 978-3-319-98192-5_40 author me.
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- 978-3-319-98192-5_40 about R_RML.
- 978-3-319-98192-5_40 about RML.
- 978-3-319-98192-5_40 about Annotation.
- 978-3-319-98192-5_40 about rules.
- 978-3-319-98192-5_40 about Linked_Data.
- 978-3-319-98192-5_40 author me.
- 978-3-319-98192-5_40 author me.
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- 978-3-319-98192-5_40 type Article.
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- 978-3-319-98192-5_40 P50 me.
- 978-3-319-98192-5_40 P50 me.
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- 978-3-319-98192-5_40 P50 me.
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- 978-3-319-98192-5_40 maker me.
- 978-3-319-98192-5_40 maker me.
- 978-3-319-98192-5_40 title "Declarative Rules for Linked Data Generation at your Fingertips!".
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- 978-3-319-98192-5_40 name "Declarative Rules for Linked Data Generation at your Fingertips!".
- 978-3-319-98192-5_40 label "Declarative Rules for Linked Data Generation at your Fingertips!".
- 978-3-319-98192-5_40 name "Declarative Rules for Linked Data Generation at your Fingertips!".
- 978-3-319-98192-5_40 topic R_RML.
- 978-3-319-98192-5_40 topic RML.
- 978-3-319-98192-5_40 topic Annotation.
- 978-3-319-98192-5_40 topic rules.
- 978-3-319-98192-5_40 topic Linked_Data.
- 978-3-319-98192-5_40 subject R_RML.
- 978-3-319-98192-5_40 subject RML.
- 978-3-319-98192-5_40 subject Annotation.
- 978-3-319-98192-5_40 subject rules.
- 978-3-319-98192-5_40 subject Linked_Data.
- 978-3-319-98192-5_40 authorList b0_b2072.
- 978-3-319-98192-5_40 topic R_RML.
- 978-3-319-98192-5_40 topic RML.
- 978-3-319-98192-5_40 topic Annotation.
- 978-3-319-98192-5_40 topic rules.
- 978-3-319-98192-5_40 topic Linked_Data.
- 978-3-319-98192-5_40 abstract "Linked Data is often generated based on a set of declarative rules using languages such as R2RML and RML. These languages are built with machine-processability in mind. It is thus not always straightforward for users to define or understand rules written in these languages, preventing them from applying the desired annotations to the data sources. In the past, graphical tools were proposed. However, next to users who prefer a graphical approach, there are users who desire to understand and define rules via a text-based approach. For the latter, we introduce an enhancement to their workflow. Instead of requiring users to manually write machine-processable rules, we propose writing human-friendly rules, and generate machine-processable rules based on those human-friendly rules. At the basis is YARRRML: a human-readable text-based representation for declarative generation rules. We propose a novel browser-based integrated development environment called “Matey”, showcasing the enhanced workflow. In this work, we describe our demo. Users can experience first hand how to generate triples from data in different formats by using YARRRML’s representation of the rules. The actual machine-processable rules remain completely hidden when editing. Matey shows that writing human-friendly rules enhances the workflow for a broader range of users. As a result, more desired annotations will be added to the data sources which leads to more desired Linked Data.".
- 978-3-319-98192-5_40 datePublished "2018".
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