Semantic Web Evaluation Challenge

Semantic Web Evaluation Challenges pp 40-50

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)

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.

References

  1. 1.
    Bizer, C., Heath, T., Berners-Lee, T.: Linked data - the story so far. Int. J. Semant. Web Inf. Syst. 5(3), 1–22 (2009)CrossRefGoogle Scholar
  2. 2.
    Bos, J.: Wide-coverage semantic analysis with boxer. In: Bos, J., Delmonte, R. (eds.) Semantics in Text Processing, pp. 277–286. College Publications, London (2008)Google Scholar
  3. 3.
    Ferragina, P., Scaiella, U.: Tagme: on-the-fly annotation of short text fragments (by wikipedia entities). In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM 2010, pp. 1625–1628. ACM, New York (2010)Google Scholar
  4. 4.
    Gangemi, A.: What’s in a Schema?. Cambridge University Press, Cambridge (2010)Google Scholar
  5. 5.
    Gangemi, A., Draicchio, F., Presutti, V., Nuzzolese, A.G., Recupero, D.R.: A machine reader for the semantic web. In: Blomqvist, E., Groza, T. (eds.) International Semantic Web Conference (Posters & Demos). CEUR Workshop Proceedings, vol. 1035, pp. 149–152. CEUR-WS.org (2013)Google Scholar
  6. 6.
    Gangemi, A., Nuzzolese, A.G., Presutti, V., Draicchio, F., Musetti, A., Ciancarini, P.: Automatic typing of DBpedia entities. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012, Part I. LNCS, vol. 7649, pp. 65–81. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  7. 7.
    Gangemi, A., Presutti, V.: Ontology design patterns. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies, 2nd edn. Springer, New York (2009)Google Scholar
  8. 8.
    Gangemi, A., Presutti, V., Recupero, D.R.: Frame-based detection of opinion holders and topics: a model and a tool. IEEE Comp. Int. Mag. 9(1), 20–30 (2014)CrossRefGoogle Scholar
  9. 9.
    Iorio, A.D., Nuzzolese, A.G., Peroni, S.: Towards the automatic identification of the nature of citations. In: Castro, A.G., Lange, C., Lord, P.W., Stevens, R. (eds.) SePublica. CEUR Workshop Proceedings, vol. 994, pp. 63–74. CEUR-WS.org (2013)Google Scholar
  10. 10.
    Kamp, H.: A theory of truth and semantic representation. In: Groenendijk, J.A.G., Janssen, T.M.V., Stokhof, M.B.J. (eds.) Formal Methods in the Study of Language, vol. 1, pp. 277–322. Mathematisch Centrum (1981)Google Scholar
  11. 11.
    Musetti, A., Nuzzolese, A.G., Draicchio, F., Presutti, V., Blomqvist, E., Gangemi, A., Ciancarini, P.: Aemoo: exploratory search based on knowledge patterns over the semantic web. In: Semantic Web Challenge (2012)Google Scholar
  12. 12.
    Nuzzolese, A.G., Gangemi, A., Presutti, V.: Gathering lexical linked data and knowledge patterns from FrameNet. In: Proceedings of the 6th International Conference on Knowledge Capture (K-CAP), Banff, Alberta, Canada, pp. 41–48 (2011)Google Scholar
  13. 13.
    Peroni, S., Gangemi, A., Vitali, F.: Dealing with markup semantics. In: Proceedings of the 7th International Conference on Semantic Systems, pp. 111–118. ACM (2011)Google Scholar
  14. 14.
    Presutti, V., Consoli, S., Nuzzolese, A.G., Recupero, D.R., Gangemi, A., Bannour, I., Zargayouna, H.: Uncovering the semantics of wikipedia wikilinks. In: 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2014) (2014)Google Scholar
  15. 15.
    Presutti, V., Draicchio, F., Gangemi, A.: Knowledge extraction based on discourse representation theory and linguistic frames. In: ten Teije, A., Völker, J., Handschuh, S., Stuckenschmidt, H., d’Acquin, M., Nikolov, A., Aussenac-Gilles, N., Hernandez, N. (eds.) EKAW 2012. LNCS, vol. 7603, pp. 114–129. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  16. 16.
    Recupero, D.R., Consoli, S., Gangemi, A., Nuzzolese, A.G., Spampinato, D.: A semantic web based core engine to efficiently perform sentiment analysis. In: Presutti, V., Blomqvist, E., Troncy, R., Sack, H., Papadakis, I., Tordai, A. (eds.) ESWC Satellite Events 2014. LNCS, vol. 8798, pp. 245–248. Springer, Heidelberg (2014) Google Scholar
  17. 17.
    Recupero, D.R., Presutti, V., Consoli, S., Gangemi, A., Nuzzolese, A.G.: Sentilo: Frame-based sentiment analysis. Cogn. Comput. 7, 211–225 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.STLab-ISTC Consiglio Nazionale Delle RicercheCataniaItaly

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