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

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Semantic Web Evaluation Challenges (SemWebEval 2015)

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

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Abstract

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 http://wit.istc.cnr.it/stlab-tools/fred.

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Notes

  1. 1.

    E.g. http://aksw.org/Projects/GERBIL.html.

  2. 2.

    D. U. L. Ontology. http://www.ontologydesignpatterns.org/ont/dul/dul.owl.

  3. 3.

    http://tagme.di.unipi.it/.

  4. 4.

    http://babelnet.org/.

  5. 5.

    Prefix dul: stands for http://www.ontologydesignpatterns.org/ont/dul/dul.owl#.

  6. 6.

    T. V. project. http://verbs.colorado.edu/~mpalmer/projects/verbnet.html.

  7. 7.

    http://www.w3.org/TR/wordnet-rdf/.

  8. 8.

    Available at http://wit.istc.cnr.it/stlab-tools/fred/.

  9. 9.

    Graphviz - Graph Visualization Software, http://www.graphviz.org/.

  10. 10.

    http://code.google.com/p/rdflib/.

  11. 11.

    https://networkx.github.io/.

  12. 12.

    http://wit.istc.cnr.it/stlab-tools/fred/fredlib.

  13. 13.

    Sheldon - available at http://wit.istc.cnr.it/stlab-tools/sheldon/.

  14. 14.

    http://www.microsoft.com/web/post/using-the-free-bing-translation-apis.

  15. 15.

    check http://msdn.microsoft.com/en-us/library/hh456380.aspx for the list of language codes.

  16. 16.

    These triples are not returned in the graph-view result of FRED at http://wit.istc.cnr.it/stlab-tools/fred/, they are returned with all other serialization output options.

  17. 17.

    Prefix fred: stands for http://www.ontologydesignpatterns.org/ont/fred/.

  18. 18.

    Prefix pos: stands for http://www.essepuntato.it/2008/12/earmark#, semio: stands for http://ontologydesignpatterns.org/cp/owl/semiotics.owl#, rdfs: stands for http://www.w3.org/2000/01/rdf-schema# and xmls: stands for http://www.w3. org/2001XMLSchema#.

  19. 19.

    http://www.visualdataweb.org/relfinder.php.

  20. 20.

    E.g. http://aksw.org/Projects/GERBIL.html.

  21. 21.

    Prefix owl: stands for http://www.w3.org/2002/07/owl#.

  22. 22.

    Prefix rdf: stands for http://www.w3.org/1999/02/22-rdf-syntax-ns#.

  23. 23.

    Hence, anaphora resolution has to be take into account for addressing the task.

  24. 24.

    Prefix oke: stands for http://www.ontologydesignpatterns.org/data/oke-challenge/task-1/, dul: stands for http://www.ontologydesignpatterns.org/ont/dul/DUL.owl# and dbpedia: stands for http://dbpedia.org/resource/.

  25. 25.

    Only full matches are counted as correct (e.g. if the system returns “Art School” instead of “National Art School” is counted as a miss).

  26. 26.

    Prefix oke: stands for http://www.ontologydesignpatterns.org/data/oke-challenge/task-2/ and dul: stands for http://www.ontologydesignpatterns.org/ont/dul/DUL. owl#.

  27. 27.

    https://github.com/anuzzolese/oke-challenge/tree/master/GoldStandard_sampleData.

  28. 28.

    https://github.com/anuzzolese/oke-challenge/tree/master/evaluation-data.

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Acknowledgement

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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Authors and Affiliations

  1. STLab-ISTC Consiglio Nazionale Delle Ricerche, Catania, Italy

    Sergio Consoli & Diego Reforgiato Recupero

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  1. Sergio Consoli
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  2. Diego Reforgiato Recupero
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Correspondence to Diego Reforgiato Recupero .

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Editors and Affiliations

  1. Inria, Sophia Antipolis, France

    Fabien Gandon

  2. INRIA Sophia-Antipolis Méditerranée, Sophia Antipolis, France

    Elena Cabrio

  3. Université Paris-Sorbonne, Paris, France

    Milan Stankovic

  4. École des Mines de Saint-Étienne, Saint-Étienne, France

    Antoine Zimmermann

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Consoli, S., Recupero, D.R. (2015). Using FRED for Named Entity Resolution, Linking and Typing for Knowledge Base Population. In: Gandon, F., Cabrio, E., Stankovic, M., Zimmermann, A. (eds) Semantic Web Evaluation Challenges. SemWebEval 2015. Communications in Computer and Information Science, vol 548. Springer, Cham. https://doi.org/10.1007/978-3-319-25518-7_4

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  • DOI: https://doi.org/10.1007/978-3-319-25518-7_4

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