DISCIE–Discriminative Closed Information Extraction
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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The Semantic Web – ISWC 2024 - 23rd International Semantic Web Conference, Proceedings. ed. / Gianluca Demartini; Katja Hose; Maribel Acosta; Matteo Palmonari; Gong Cheng; Hala Skaf-Molli; Nicolas Ferranti; Daniel Hernández; Aidan Hogan. Cham: Springer Nature Switzerland AG, 2025. p. 23-40 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 15232 LNCS).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - DISCIE–Discriminative Closed Information Extraction
AU - Möller, Cedric
AU - Usbeck, Ricardo
N1 - Conference code: 23
PY - 2025
Y1 - 2025
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 -