Abstract
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.
Abstract (translated)
我们研究了在各种数据集上将知识图信息纳入关系抽取模型对性能的影响。我们的假设是，实体在知识图中的位置为关系抽取任务提供了重要线索。我们在多个数据集上进行了实验，这些数据集在关系数量、训练示例和底层知识图方面各不相同。结果表明，在处理每个关系的训练样本不平衡时，整合知识图信息显著提高了模型性能。 我们通过将现有的关系提取方法与基于图的认知贝尔曼-福特（Neural Bellman-Ford）网络相结合，评估了知识图特征的贡献，并在有监督和零样本学习设置中测试了这些特征的有效性。结果显示，在各种数据集中均能实现一致的性能改进。
URL
https://arxiv.org/abs/2506.16343
