%0 Conference Paper %1 reforgiato_recupero_extracting_2015 %A Reforgiato Recupero, Diego %A Nuzzolese, Andrea %A Consoli, Sergio %A Presutti, Valentina %A Mongiovì, Misael %A Peroni, Silvio %D 2015 %I International World Wide Web Conferences Steering Committee %K semantic_web %P 235--238 %R 10.1145/2740908.2742842 %T Extracting knowledge from text using SHELDON : a semantic holistic framework for linked ontology data %U http://dx.doi.org/10.1145/2740908.2742842 %X SHELDON is the first true hybridization of NLP machine reading and the Semantic Web. It extracts RDF data from text using a machine reader: the extracted RDF graphs are compliant to Semantic Web and Linked Data. It goes further and applies Semantic Web practices and technologies to extend the current human-readable web. The input is represented by a sentence in any language. SHELDON includes different capabilities in order to extend machine reading to Semantic Web data: frame detection, topic ex- traction, 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, nice visualization tools which make use of the JavaScript infoVis Toolkit and RelFinder. A demo of SHELDON can be seen and used at http://wit.istc.cnr.it/stlab-tools/sheldon. %@ 978-1-4503-3473-0

@inproceedings{reforgiato_recupero_extracting_2015, abstract = {SHELDON is the first true hybridization of NLP machine reading and the Semantic Web. It extracts RDF data from text using a machine reader: the extracted RDF graphs are compliant to Semantic Web and Linked Data. It goes further and applies Semantic Web practices and technologies to extend the current human-readable web. The input is represented by a sentence in any language. SHELDON includes different capabilities in order to extend machine reading to Semantic Web data: frame detection, topic ex- traction, 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, nice visualization tools which make use of the JavaScript infoVis Toolkit and RelFinder. A demo of SHELDON can be seen and used at http://wit.istc.cnr.it/stlab-tools/sheldon.}, added-at = {2018-11-04T16:54:56.000+0100}, author = {Reforgiato Recupero, Diego and Nuzzolese, Andrea and Consoli, Sergio and Presutti, Valentina and Mongiovì, Misael and Peroni, Silvio}, biburl = {https://www.bibsonomy.org/bibtex/296eb20de6d22a74eb1c235a0aa616c76/lepsky}, doi = {10.1145/2740908.2742842}, interhash = {536d0f2634c317970fc33efd83858a3f}, intrahash = {96eb20de6d22a74eb1c235a0aa616c76}, isbn = {978-1-4503-3473-0}, keywords = {semantic_web}, pages = {235--238}, publisher = {International World Wide Web Conferences Steering Committee}, timestamp = {2018-11-07T09:16:47.000+0100}, title = {Extracting knowledge from text using {SHELDON} : a semantic holistic framework for linked ontology data}, url = {http://dx.doi.org/10.1145/2740908.2742842}, year = 2015 }