Learning Pattern-Based Extractors from Natural Language and Knowledge Graphs: Applying Large Language Models to Wikipedia and Linked Open Data
Abstract
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.
Cite
Text
Ringwald. "Learning Pattern-Based Extractors from Natural Language and Knowledge Graphs: Applying Large Language Models to Wikipedia and Linked Open Data." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30406Markdown
[Ringwald. "Learning Pattern-Based Extractors from Natural Language and Knowledge Graphs: Applying Large Language Models to Wikipedia and Linked Open Data." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/ringwald2024aaai-learning/) doi:10.1609/AAAI.V38I21.30406BibTeX
@inproceedings{ringwald2024aaai-learning,
title = {{Learning Pattern-Based Extractors from Natural Language and Knowledge Graphs: Applying Large Language Models to Wikipedia and Linked Open Data}},
author = {Ringwald, Célian},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {23411-23412},
doi = {10.1609/AAAI.V38I21.30406},
url = {https://mlanthology.org/aaai/2024/ringwald2024aaai-learning/}
}