Integrating Knowledge-Supported Search into the INCEpTION Annotation Platform

Beto Boullosa, Richard Eckart de Castilho, Naveen Kumar, Jan-Christoph Klie, Iryna Gurevych


Abstract
Annotating entity mentions and linking them to a knowledge resource are essential tasks in many domains. It disambiguates mentions, introduces cross-document coreferences, and the resources contribute extra information, e.g. taxonomic relations. Such tasks benefit from text annotation tools that integrate a search which covers the text, the annotations, as well as the knowledge resource. However, to the best of our knowledge, no current tools integrate knowledge-supported search as well as entity linking support. We address this gap by introducing knowledge-supported search functionality into the INCEpTION text annotation platform. In our approach, cross-document references are created by linking entity mentions to a knowledge base in the form of a structured hierarchical vocabulary. The resulting annotations are then indexed to enable fast and yet complex queries taking into account the text, the annotations, and the vocabulary structure.
Anthology ID:
D18-2022
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
127–132
Language:
URL:
https://aclanthology.org/D18-2022
DOI:
10.18653/v1/D18-2022
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/D18-2022.pdf