SHELDON: Semantic Holistic framEwork for LinkeD ONtology data

author: Diego Reforgiato Recupero, Institute of Cognitive Sciences and Technologies (ISTC)
published: Dec. 19, 2014,   recorded: October 2014,   views: 20
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Description

SHELDON is the first true hybridization of NLP machine reading and Semantic Web. It is a framework that builds upon a ma- chine reader for extracting RDF graphs from text so that the output is compliant to Semantic Web and Linked Data patterns. It extends the current human-readable web by using Semantic Web practices and technologies in a machine-processable form. Given a sentence in any language, it provides different semantic functionalities (frame detection, topic extraction, named entity recognition, resolution and coreference, terminology extraction, sense tagging and disambiguation, taxonomy induction, semantic role labeling, type induction, sentiment analysis, citation inference, relation and event extraction) as well as nice visualization tools which make use of the JavaScript infoVis Toolkit and RelFinder, as well as a knowledge enrichment component that extends machine reading to Semantic Web data. The system can be freely used at http://wit.istc.cnr.it/stlab-tools/sheldon.

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