Analyzing the Influence of Knowledge Graph Information on Relation Extraction
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Authors
We examine the impact of incorporating knowledge graph information on the performance of relation extraction models across a range of datasets. Our hypothesis is that the positions of entities within a knowledge graph provide important insights for relation extraction tasks. We conduct experiments on multiple datasets, each varying in the number of relations, training examples, and underlying knowledge graphs. Our results demonstrate that integrating knowledge graph information significantly enhances performance, especially when dealing with an imbalance in the number of training examples for each relation. We evaluate the contribution of knowledge graph-based features by combining established relation extraction methods with graph-aware Neural Bellman-Ford networks. These features are tested in both supervised and zero-shot settings, demonstrating consistent performance improvements across various datasets.
Original language | English |
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Title of host publication | The Semantic Web |
Editors | Edward Curry, Maribel Acosta, Maria Poveda-Villalón, Marieke van Erp, Adegboyega Ojo, Katja Hose, Cogan Shimizu, Pasquale Lisena |
Number of pages | 21 |
Place of Publication | Cham |
Publisher | Springer Nature Switzerland AG |
Publication date | 2025 |
Pages | 460-480 |
ISBN (print) | 978-3-031-94575-5 |
Publication status | Published - 2025 |