论文标题
Kermit-一种基于变压器的知识图匹配的方法
KERMIT -- A Transformer-Based Approach for Knowledge Graph Matching
论文作者
论文摘要
自动匹配的知识图和本体学信号之一是文本概念描述。随着基于变压器的语言模型的兴起，研究人员可以使用基于含义（而不是词汇特征）的文本比较。但是，在两个知识图中对所有文本描述进行所有文本描述进行成对比较是昂贵的，并且四边形（如果概念具有多个描述以上的描述，甚至更糟）。为了克服这个问题，我们遵循两步方法：我们首先使用预训练的句子变压器（所谓的双架）生成匹配的候选者。在第二步中，我们使用微调的变压器交叉编码器来生成最佳的候选者。我们在多个数据集上评估了我们的方法，并表明它是可行的并产生竞争结果。
One of the strongest signals for automated matching of knowledge graphs and ontologies are textual concept descriptions. With the rise of transformer-based language models, text comparison based on meaning (rather than lexical features) is available to researchers. However, performing pairwise comparisons of all textual descriptions of concepts in two knowledge graphs is expensive and scales quadratically (or even worse if concepts have more than one description). To overcome this problem, we follow a two-step approach: we first generate matching candidates using a pre-trained sentence transformer (so called bi-encoder). In a second step, we use fine-tuned transformer cross-encoders to generate the best candidates. We evaluate our approach on multiple datasets and show that it is feasible and produces competitive results.
