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

Maxime Prieur (1, 2) , Cédric Du Mouza (1) , Guillaume Gadek (2) , Bruno Grilheres (2)

1 CEDRIC - ISID - CEDRIC. Ingénierie des Systèmes d'Information et de Décision
2 Airbus Defence and Space (AIRBUS)

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.

Mots clés: Knowledge Base Population; Entity Linking; Supervised Learning; Data Mining

Domaines: Informatique [cs]

Maxime Prieur, Cédric Du Mouza, Guillaume Gadek, Bruno Grilheres. Evaluating and Improving End-to-End Systems for Knowledge Base Population. International Conference on Agents and Artificial Intelligence (ICAART), Feb 2023, Lisbonne, France. ⟨hal-03937780⟩