基于路径排序的引导图嵌入算法在无干扰知识图谱错误检测中的应用
Guiding Graph Embeddings using Path-Ranking Methods for Error Detection innoisy Knowledge Graphs
摘要：
知识图谱是目前异构大数据关系信息表示的主流方法，但其自动构建时可能包含大量的输入噪声。为了解决这个问题，已经提出了不同的错误检测方法，主要集中在路径排序和表示学习。这项工作提出了各种主流的方法，并提出了一个混合和模块化的方法的任务。我们在两个基准和一个真实世界的生物医学出版物数据集上比较了不同的方法，展示了我们方法的潜力，并提供了在处理有噪知识图谱时图嵌入的见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
