基于交叉图表示学习的知识图谱有效性验证
Efficient Knowledge Graph Validation via Cross-Graph Representation Learning
摘要：
信息抽取技术的最新进展促使人们从大规模文本语料库中挖掘出大量的知识图谱来自动构建知识图谱。然而，噪声的事实是不可避免地引入到KG，可能会导致自动提取。为了验证事实的正确性（即，三元组），一种可能的方法是通过捕获事实的语义含义来将三元组映射到向量表示中。虽然已经为知识图谱开发了许多表示学习方法，但是这些方法对于验证不是有效的。他们通常假设事实是正确的，因此可能过度拟合噪声事实，而无法检测到这些事实。为了有效的KG验证，我们建议利用外部人工策划的KG作为辅助信息源，以帮助检测目标KG中的错误。外部KG建立在人工管理的知识库之上，往往具有很高的精度。另一方面，尽管通过从文本中信息抽取构建的目标KG具有低精度，但它可以覆盖任何人类策划的知识库中没有的新的或特定于领域的事实。为了解决这个具有挑战性的任务，我们提出了一个交叉图表示学习框架，即，CrossVal，它可以利用外部KG来有效地验证目标KG中的事实。这是通过基于语义嵌入三元组、绘制跨KG负样本并基于其正确性程度估计每个三元组的置信度得分来实现的。我们在不同领域的数据集上评估了所提出的框架。实验结果表明，该框架实现了最好的性能相比，国家的最先进的方法在大规模的幼儿园。
英文摘要：
Recent advances in information extraction have motivated the automatic construction of huge Knowledge Graphs (KGs) by mining from large-scale text corpus. However, noisy facts are unavoidably introduced into KGs that could be caused by automatic extraction. To validate the correctness of facts (i.e., triplets) inside a KG, one possible approach is to map the triplets into vector representations by capturing the semantic meanings of facts. Although many representation learning approaches have been developed for knowledge graphs, these methods are not effective for validation. They usually assume that facts are correct, and thus may overfit noisy facts and fail to detect such facts. Towards effective KG validation, we propose to leverage an external human-curated KG as auxiliary information source to help detect the errors in a target KG. The external KG is built upon human-curated knowledge repositories and tends to have high precision. On the other hand, although the target KG built by information extraction from texts has low precision, it can cover new or domain-specific facts that are not in any human-curated repositories. To tackle this challenging task, we propose a cross-graph representation learning framework, i.e., CrossVal, which can leverage an external KG to validate the facts in the target KG efficiently. This is achieved by embedding triplets based on their semantic meanings, drawing cross-KG negative samples and estimating a confidence score for each triplet based on its degree of correctness. We evaluate the proposed framework on datasets across different domains. Experimental results show that the proposed framework achieves the best performance compared with the state-of-the-art methods on large-scale KGs.
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。
