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.

利用知识图谱中概念语义信息的交叉表示学习框架 CrossVal，通过嵌入三元组、绘制交叉负样本及估计每个三元组的正确性程度来有效验证知识图谱中的三元组。