Open Knowledge Extraction Challenge 2017

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 769)

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.

Keywords

Open Knowledge Extraction Challenge Semantic Web 

Notes

Acknowledgement

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).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.AKSW GroupUniversity of LeipzigLeipzigGermany
  2. 2.Universitat Pompeu FabraBarcelonaSpain
  3. 3.University of PaderbornPaderbornGermany

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