Semantic Web Evaluation Challenge

Semantic Web Evaluation Challenges pp 40-50 | Cite as

Using FRED for Named Entity Resolution, Linking and Typing for Knowledge Base Population

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 548)


FRED is a machine reader for extracting RDF graphs that are linked to LOD and compliant to Semantic Web and Linked Data patterns. We describe the capabilities of FRED as a semantic middleware for semantic web applications. In particular, we will show (i) how FRED recognizes and resolves named entities, (ii) how it links them to existing knowledge base, and (iii) how it gives them a type. 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) by means of a versatile user-interface, which can be recalled as REST Web service. The system can be freely used at


Name Entity Recognition Entity Recognition Entity Resolution Discourse Referent Exist Knowledge Base 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The research leading to these results has received funding from the European Union Horizons 2020 the Framework Programme for Research and Innovation (2014–2020) under grant agreement 643808 Project MARIO Managing active and healthy aging with use of caring service robots.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.STLab-ISTC Consiglio Nazionale Delle RicercheCataniaItaly

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