REL 2.0

Multilingual and Multipurpose Entity Linking Toolkit

entity linking

Machine understanding of text is an extremely challenging task for intelligent systems; machines need to understand the meaning behind words and reason about the text and existing knowledge. A highly effective method to obtain this understanding is connecting unstructured text to semi-structured information in the knowledge graphs (e.g., Wikipedia and Wikidata). In this process, referred to as Entity Linking, entities such as people and locations are identified and mapped to their corresponding entries in a knowledge graph. Entity linking is a language-dependent, compute, and data hungry process. How can we make this technology usable for multilingual, formal, and informal texts, requiring only limited computational power?

In this project, we introduce REL 2.0, a publicly available entity linking toolkit that can operate on texts in a variety of languages and forms (e.g., long documents, queries, and conversations), in a reasonable time and using commonly available hardware.

Participating organisations

Radboud University Nijmegen
Netherlands eScience Center
Social Sciences & Humanities
Social Sciences & Humanities

Team

FH
Faegheh Hasibi
Principal investigator
Radboud University Nijmegen
Erik Tjong Kim Sang
Research Software Engineer
Netherlands eScience Center
Stef Smeets
Research Software Engineer
Netherlands eScience Center
Flavio Hafner
Flavio Hafner
Research Software Engineer
Netherlands eScience Center
Jisk Attema
Programme Manager
Netherlands eScience Center
AdV
Arjen de Vries
HJ
Hideaki Joko
PhD Student
Radboud University
KD
Koen Dercksen
CK
Chris Kamphuis
PhD Student
Radboud University

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