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dc.contributor.author Speck, René
dc.contributor.author Röder, Michael
dc.contributor.author Oramas, Sergio
dc.contributor.author Espinosa-Anke, Luis
dc.contributor.author Ngonga Ngomo, Axel-Cyrille
dc.date.accessioned 2018-03-19T11:09:05Z
dc.date.available 2018-03-19T11:09:05Z
dc.date.issued 2017
dc.identifier.citation Speck R, Röder M, Oramas S, Espinosa-Anke L, Ngonga Ngomo AC. Open Knowledge Extraction Challenge 2017. In: Semantic Web Challenges. 4th SemWebEval Challenge at ESWC 2017; 2017 May 28-Jun 1; Portoroz, Slovenia. p. 35-48. DOI: 10.1007/978-3-319-69146-6_4
dc.identifier.uri http://hdl.handle.net/10230/34198
dc.description Comunicació presentada al 4th SemWebEval Challenge at ESWC 2017, celebrat entre els dies 28 de maig i 1 de juny de 2017 a Portoroz, Eslovènia.
dc.description.abstract The Open Knowledge Extraction Challenge invites researchers and practitioners from academia as well as industry to compete to the aim of pushing further the state of the art of knowledge extraction from text for the Semantic Web. The challenge has the ambition to provide a reference framework for research in this field by redefining a number of tasks typically from information and knowledge extraction by taking into account Semantic Web requirements and has the goal to test the performance of knowledge extraction systems. This year, the challenge goes in the third round and consists of three tasks which include named entity identification, typing and disambiguation by linking to a knowledge base depending on the task. The challenge makes use of small gold standard datasets that consist of manually curated documents and large silver standard datasets that consist of automatically generated synthetic documents. The performance measure of a participating system is twofold base on (1) Precision, Recall, F1-measure and on (2) Precision, Recall, F1-measure with respect to the runtime of the system.
dc.description.sponsorship This work has been supported by the H2020 project HOBBIT (GA no. 688227) as well as the EuroStars projects DIESEL (project no. 01QE1512C) and QAMEL (project no. 01QE1549C). Also this work was partially funded by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015–0502).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Springer
dc.relation.ispartof Semantic Web Challenges. 4th SemWebEval Challenge at ESWC 2017; 2017 May 28-Jun 1; Portoroz, Slovenia. p. 35-48.
dc.rights © Springer International Publishing AG 2017
dc.title Open Knowledge Extraction Challenge 2017
dc.type info:eu-repo/semantics/conferenceObject
dc.identifier.doi http://dx.doi.org/10.1007/978-3-319-69146-6_4
dc.subject.keyword Open Knowledge Extraction Challenge
dc.subject.keyword Semantic web
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/688227
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/acceptedVersion


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