Evaluating and Improving End-to-End Systems for Knowledge Base Population

Maxime Prieur, Cédric Mouza, Guillaume Gadek, Bruno Grilheres

2023

Abstract

Knowledge Bases (KB) are used in many fields, such as business intelligence or user assistance. They aggregate knowledge that can be exploited by computers to help decision making by providing better visualization or predicting new relations. However, their building remains complex for an expert who has to extract and link each new information. In this paper, we describe an entity-centric method for evaluating an end-to-end Knowledge Base Population system. This evaluation is applied to ELROND, a complete system designed as a workflow composed of 4 modules (Named Entity Recognition, Coreference Resolution, Relation Extraction and Entity Linking) and MERIT, a dynamic entity linking model made of a textual encoder to retrieve similar entities and a classifier.

Paper Citation

in Harvard Style

Prieur M., Mouza C., Gadek G. and Grilheres B. (2023). Evaluating and Improving End-to-End Systems for Knowledge Base Population. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 641-649. DOI: 10.5220/0011726000003393

in Bibtex Style

@conference{icaart23,
author={Maxime Prieur and Cédric Mouza and Guillaume Gadek and Bruno Grilheres},
title={Evaluating and Improving End-to-End Systems for Knowledge Base Population},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={641-649},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011726000003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Evaluating and Improving End-to-End Systems for Knowledge Base Population
SN - 978-989-758-623-1
AU - Prieur M.
AU - Mouza C.
AU - Gadek G.
AU - Grilheres B.
PY - 2023
SP - 641
EP - 649
DO - 10.5220/0011726000003393