TY - CHAP

T1 - DISCIE–Discriminative Closed Information Extraction

AU - Möller, Cedric

AU - Usbeck, Ricardo

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

PY - 2024/11/27

Y1 - 2024/11/27

N2 - This paper introduces a novel method for closed information extraction. The method employs a discriminative approach that incorporates type and entity-specific information to improve relation extraction accuracy, particularly benefiting long-tail relations. Notably, this method demonstrates superior performance compared to state-of-the-art end-to-end generative models. This is especially evident for the problem of large-scale closed information extraction where we are confronted with millions of entities and hundreds of relations. Furthermore, we emphasize the efficiency aspect by leveraging smaller models. In particular, the integration of type-information proves instrumental in achieving performance levels on par with or surpassing those of a larger generative model. This advancement holds promise for more accurate and efficient information extraction techniques.

AB - This paper introduces a novel method for closed information extraction. The method employs a discriminative approach that incorporates type and entity-specific information to improve relation extraction accuracy, particularly benefiting long-tail relations. Notably, this method demonstrates superior performance compared to state-of-the-art end-to-end generative models. This is especially evident for the problem of large-scale closed information extraction where we are confronted with millions of entities and hundreds of relations. Furthermore, we emphasize the efficiency aspect by leveraging smaller models. In particular, the integration of type-information proves instrumental in achieving performance levels on par with or surpassing those of a larger generative model. This advancement holds promise for more accurate and efficient information extraction techniques.

KW - Informatics

UR - http://www.scopus.com/inward/record.url?scp=85211208993&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-77850-6_2

DO - 10.1007/978-3-031-77850-6_2

M3 - Article in conference proceedings

SN - 978-3-031-77849-0

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 23

EP - 40

BT - The Semantic Web – ISWC 2024 - 23rd International Semantic Web Conference, Proceedings

A2 - Demartini, Gianluca

A2 - Hose, Katja

A2 - Acosta, Maribel

A2 - Palmonari, Matteo

A2 - Cheng, Gong

A2 - Skaf-Molli, Hala

A2 - Ferranti, Nicolas

A2 - Hernández, Daniel

A2 - Hogan, Aidan

PB - Springer Nature Switzerland AG

CY - Cham

T2 - 23rd International Semantic Web Conference, ISWC 2024

Y2 - 11 November 2024 through 15 November 2024

ER -