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
We develop a large number of software tools and hosting infrastructures to support the research developed at the Department. We will be detailing in this section the different tools available. You can take a look for the moment at the offer available within the UPF Knowledge Portal, the innovations created in the context of EU projects in the Innovation Radar and the software sections of some of our research groups:
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Artificial Intelligence |
Nonlinear Time Series Analysis |
Web Research |
Music Technology |
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Interactive Technologies |
Barcelona MedTech |
Natural Language Processing |
Nonlinear Time Series Analysis |
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UbicaLab |
Wireless Networking |
Educational Technologies |
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/