%0 Journal Article %1 recupero_sheldon_2014 %A Recupero, Diego %A Nuzzolese, Andrea %A Consoli, Sergio %A Gangemi, Aldo %A Presutti, Valentina %D 2014 %K ontologien texttechnologie semantic_web %T SHELDON : Semantic Holistic framEwork for LinkeD ONtology data %U http://challenge.semanticweb.org/2014/submissions/swc2014_submission_1.pdf %X SHELDON is the first true hybridization of NLP machine reading and Semantic Web. It is a framework that builds upon a machine 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.

@article{recupero_sheldon_2014, abstract = {SHELDON is the first true hybridization of NLP machine reading and Semantic Web. It is a framework that builds upon a machine 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.}, added-at = {2018-11-04T17:02:36.000+0100}, author = {Recupero, Diego and Nuzzolese, Andrea and Consoli, Sergio and Gangemi, Aldo and Presutti, Valentina}, biburl = {https://www.bibsonomy.org/bibtex/290b82d59a427ce87146e91d586e0b316/lepsky}, interhash = {6a252f7317b14e94dc58a3d8293b96ce}, intrahash = {90b82d59a427ce87146e91d586e0b316}, keywords = {ontologien texttechnologie semantic_web}, timestamp = {2018-11-07T09:16:47.000+0100}, title = {{SHELDON} : {Semantic} {Holistic} {framEwork} for {LinkeD} {ONtology} data}, url = {http://challenge.semanticweb.org/2014/submissions/swc2014_submission_1.pdf}, year = 2014 }