TY - CHAP

T1 - Analyzing the Influence of Knowledge Graph Information on Relation Extraction

AU - Möller, Cedric

AU - Usbeck, Ricardo

PY - 2025

Y1 - 2025

N2 - 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.

AB - 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.

KW - Business informatics

UR - https://d-nb.info/136741640X

U2 - 10.1007/978-3-031-94575-5_25

DO - 10.1007/978-3-031-94575-5_25

M3 - Article in conference proceedings

SN - 978-3-031-94574-8

VL - 1

T3 - Lecture Notes in Computer Science

SP - 460

EP - 480

BT - The Semantic Web

A2 - Curry, Edward

A2 - Acosta, Maribel

A2 - Poveda-Villalón, Maria

A2 - van Erp, Marieke

A2 - Ojo, Adegboyega

A2 - Hose, Katja

A2 - Shimizu, Cogan

A2 - Lisena, Pasquale

PB - Springer Nature Switzerland AG

CY - Cham

ER -