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- 27 title "Benchmarking the Performance of Linked Data Translation Systems".
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- item-1 hasContent carlos-r-rivero.
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- item-2 hasContent andreas-schultz.
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- item-3 hasContent christian-bizer.
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- 28 creator fabien-gandon.
- 28 creator luca-costabello.
- 28 creator nicolas-delaforge.
- 28 creator serena-villata.
- 28 type InProceedings.
- 28 label "Linked Data Access Goes Mobile: Context-Aware Authorization for Graph Stores".
- 28 sameAs 28.
- 28 abstract "To encourage data providers to publish a maximum of data on the Web, we propose a mechanism to define lightweight access control policies for graph stores. Influenced by the steep growth of the mobile web, our Linked Data access control framework features context-aware control policies. The proposed framework is exclusively grounded on standard Semantic Web languages. The framework architecture is designed as a pluggable filter for generic SPARQL endpoints and it has been evaluated on a test dataset.".
- 28 hasAuthorList authorList.
- 28 isPartOf proceedings.
- 28 keyword "Access Control".
- 28 keyword "Linked Data".
- 28 keyword "Ubiquitous Web".
- 28 title "Linked Data Access Goes Mobile: Context-Aware Authorization for Graph Stores".
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- item-1 hasContent luca-costabello.
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- item-2 hasContent serena-villata.
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- item-3 hasContent nicolas-delaforge.
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- item-4 hasContent fabien-gandon.
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- 29 creator giuseppe-rizzo.
- 29 creator martin-bruemmer.
- 29 creator raphael-troncy.
- 29 creator sebastian-hellmann.
- 29 type InProceedings.
- 29 label "NERD meets NIF: Lifting NLP Extraction Results to the Linked Data Cloud".
- 29 sameAs 29.
- 29 abstract "We have often heard that data is the new oil. In particular, extracting information from semi-structured textual documents on the Web is key to realize the Linked Data vision. Several attempts have been proposed to extract knowledge from textual documents, extracting named entities, classifying them according to pre-defined taxonomies and disambiguating them through URIs identifying real world entities. As a step towards interconnecting the Web of documents via those entities, different extractors have been proposed. Although they share the same main purpose (extracting named entity), they differ from numerous aspects such as their underlying dictionary or ability to disambiguate entities. We have developed NERD, an API and a front-end user interface powered by an ontology to unify various named entity extractors. The unified result output is serialized in RDF according to the NIF specification and published back on the Linked Data cloud. We evaluated NERD with a dataset composed of five TED talk transcripts, a dataset composed of 1000 New York Times articles and a dataset composed of the 217 abstracts of the papers published at WWW 2011.".
- 29 hasAuthorList authorList.
- 29 isPartOf proceedings.
- 29 keyword "Evaluation".
- 29 keyword "Information extraction".
- 29 keyword "Linked Data".
- 29 keyword "Named Entity extractors".
- 29 title "NERD meets NIF: Lifting NLP Extraction Results to the Linked Data Cloud".
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- item-1 hasContent giuseppe-rizzo.
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- item-2 hasContent raphael-troncy.
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- item-3 hasContent sebastian-hellmann.
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- item-4 hasContent martin-bruemmer.
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- 30 creator aldo-gangemi.
- 30 creator andrea-nuzzolese.
- 30 creator paolo-ciancarini.
- 30 creator valentina-presutti.
- 30 type InProceedings.
- 30 label "Type inference through the analysis of Wikipedia links".
- 30 sameAs 30.