Speck R, Röder M, Oramas S, Espinosa-Anke L, Ngonga Ngomo AC. Open Knowledge Extraction Challenge 2017. Semantic Web Challenges. SemWebEval 2017. Communications in Computer and Information Science
List of results published directly linked with the projects co-funded by the Spanish Ministry of Economy and Competitiveness under the María de Maeztu Units of Excellence Program (MDM-2015-0502).
List of publications acknowledging the funding in Scopus.
The record for each publication will include access to postprints (following the Open Access policy of the program), as well as datasets and software used. Ongoing work with UPF Library and Informatics will improve the interface and automation of the retrieval of this information soon.
The MdM Strategic Research Program has its own community in Zenodo for material available in this repository 
as well as at the UPF e-repository 
Speck R, Röder M, Oramas S, Espinosa-Anke L, Ngonga Ngomo AC. Open Knowledge Extraction Challenge 2017. Semantic Web Challenges. SemWebEval 2017. Communications in Computer and Information Science
Speck R, Röder M, Oramas S, Espinosa-Anke L, Ngonga Ngomo AC. Open Knowledge Extraction Challenge 2017. Semantic Web Challenges. SemWebEval 2017. Communications in Computer and Information Science
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.
Additional material:
- Link of the challenge https://project-hobbit.eu/challenges/oke2017-challenge-eswc-2017/