DISCIE-Discriminative Closed Information Extraction
Abstract: 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.
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@inproceedings{DBLP:conf/semweb/MollerU24,
author = {Cedric M{\"{o}}ller and
Ricardo Usbeck},
editor = {Gianluca Demartini and
Katja Hose and
Maribel Acosta and
Matteo Palmonari and
Gong Cheng and
Hala Skaf{-}Molli and
Nicolas Ferranti and
Daniel Hern{\'{a}}ndez and
Aidan Hogan},
title = {DISCIE-Discriminative Closed Information Extraction},
booktitle = {The Semantic Web - {ISWC} 2024 - 23rd International Semantic Web Conference,
Baltimore, MD, USA, November 11-15, 2024, Proceedings, Part {II}},
series = {Lecture Notes in Computer Science},
volume = {15232},
pages = {23--40},
publisher = {Springer},
year = {2024},
url = {https://doi.org/10.1007/978-3-031-77850-6\_2},
doi = {10.1007/978-3-031-77850-6\_2},
timestamp = {Sun, 02 Nov 2025 21:27:22 +0100},
biburl = {https://dblp.org/rec/conf/semweb/MollerU24.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}