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Célian Ringwald

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2 papers
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2

AAAI Conference 2024 Short Paper

Learning Pattern-Based Extractors from Natural Language and Knowledge Graphs: Applying Large Language Models to Wikipedia and Linked Open Data

  • Célian Ringwald

Seq-to-seq transformer models have recently been successfully used for relation extraction, showing their flexibility, effectiveness, and scalability on that task. In this context, knowledge graphs aligned with Wikipedia such as DBpedia and Wikidata give us the opportunity to leverage existing texts and corresponding RDF graphs in order to extract, from these texts, the knowledge that is missing in the corresponding graphs and meanwhile improve their coverage. The goal of my thesis is to learn efficient extractors targeting specific RDF patterns and to do so by leveraging the latest language models and the dual base formed by Wikipedia on the one hand, and DBpedia and Wikidata on the other hand.

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AAAI Conference 2024 Short Paper

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

  • Célian Ringwald
  • Fabien Gandon
  • Catherine Faron
  • Franck Michel
  • Hanna Abi Akl

Seq-to-seq generative models recently gained attention for solving the relation extraction task. By approaching this problem as an end-to-end task, they surpassed encoder-based-only models. Little research investigated the effects of the output 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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