Well-Written Knowledge Graphs: Most Effective RDF Syntaxes for Triple Linearization in End-to-End Extraction of Relations from Texts (Student Abstract) - Archive ouverte HAL Access content directly
Conference Papers Year : 2024

Well-Written Knowledge Graphs: Most Effective RDF Syntaxes for Triple Linearization in End-to-End Extraction of Relations from Texts (Student Abstract)

Abstract

Seq-to-seq generative models recently gained attention for solving the relation extraction task. By approaching this prob- lem as an end-to-end task, they surpassed encoder-based-only models. Little research investigated the effects of the ouput syntaxes on the training process of these models. Moreover, a limited number of approaches were proposed for extracting ready-to-load knowledge graphs following the RDF standard. In this paper, we consider that a set of triples can be linearized in many different ways, and we evaluate the combined effect of the size of the language models and different RDF syntaxes on the task of relation extraction from Wikipedia abstracts.
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hal-04384724 , version 1 (29-03-2024)

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Célian Ringwald, Fabien Gandon, Catherine Faron, Franck Michel, Hanna Abi Akl. Well-Written Knowledge Graphs: Most Effective RDF Syntaxes for Triple Linearization in End-to-End Extraction of Relations from Texts (Student Abstract): Well-Written Knowledge Graphs. AAAI 2024 - 38th Annual AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence, Feb 2024, Vancouver, Canada. pp.23631-23632, ⟨10.1609/aaai.v38i21.30502⟩. ⟨hal-04384724⟩

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