论文标题
在语义和结构变化下的知识图中的链接预测的基准测试神经嵌入
Benchmarking neural embeddings for link prediction in knowledge graphs under semantic and structural changes
论文作者
论文摘要
最近，基于神经嵌入的链接预测算法在语义网络社区中广受欢迎，并广泛用于知识图完成。尽管算法的进步强烈地集中在有效学习嵌入方式上，但对可以评估其性能和鲁棒性的不同方式的关注更少。在这项工作中，我们提出了一条开源评估管道，该管道基准了知识图可能会经历语义和结构变化的情况下神经嵌入的准确性。我们定义了以关系为中心的连接度量，使我们能够将链接预测能力连接到知识图的结构。这样的评估管道对于模拟嵌入的准确性尤其重要，这些知识图的嵌入式性预计经常更新。
Recently, link prediction algorithms based on neural embeddings have gained tremendous popularity in the Semantic Web community, and are extensively used for knowledge graph completion. While algorithmic advances have strongly focused on efficient ways of learning embeddings, fewer attention has been drawn to the different ways their performance and robustness can be evaluated. In this work we propose an open-source evaluation pipeline, which benchmarks the accuracy of neural embeddings in situations where knowledge graphs may experience semantic and structural changes. We define relation-centric connectivity measures that allow us to connect the link prediction capacity to the structure of the knowledge graph. Such an evaluation pipeline is especially important to simulate the accuracy of embeddings for knowledge graphs that are expected to be frequently updated.
