论文标题
使用路径级方法进行指导图嵌入方法，用于错误检测Innoisy知识图
Guiding Graph Embeddings using Path-Ranking Methods for Error Detection innoisy Knowledge Graphs
论文作者
论文摘要
如今，知识图构成了一种主流方法，用于表示大型异质数据的关系信息，但是，当自动构造时，它们可能包含大量的估算噪声。为了解决此问题，已经提出了不同的错误检测方法，主要集中于路径排名和表示学习。这项工作介绍了各种主流方法，并为任务提出了混合和模块化方法。我们比较了两个基准和一个现实的生物医学出版物数据集上的不同方法，展示了我们方法的潜力，并在处理嘈杂的知识图时就图形嵌入式提供了见解。
Nowadays Knowledge Graphs constitute a mainstream approach for the representation of relational information on big heterogeneous data, however, they may contain a big amount of imputed noise when constructed automatically. To address this problem, different error detection methodologies have been proposed, mainly focusing on path ranking and representation learning. This work presents various mainstream approaches and proposes a hybrid and modular methodology for the task. We compare different methods on two benchmarks and one real-world biomedical publications dataset, showcasing the potential of our approach and providing insights on graph embeddings when dealing with noisy Knowledge Graphs.
