Translation models tend to ignore the rich semantic information in triads in
the process of knowledge graph complementation. To remedy this shortcoming,
this paper constructs a knowledge graph complementation method that
incorporates adaptively enhanced semantic information. The hidden semantic
information inherent in the triad is obtained by fine-tuning the BERT model,
and the attention feature embedding method is used to calculate the semantic
attention scores between relations and entities in positive and negative triads
and incorporate them into the structural information to form a soft constraint
rule for semantic information. The rule is added to the original translation
model to realize the adaptive enhancement of semantic information. In addition,
the method takes into account the effect of high-dimensional vectors on the
effect, and uses the BERT-whitening method to reduce the dimensionality and
generate a more efficient semantic vector representation. After experimental
comparison, the proposed method performs better on both FB15K and WIN18
datasets, with a numerical improvement of about 2.6% compared with the original
translation model, which verifies the reasonableness and effectiveness of the
method.

该研究构建了一种知识图谱补全方法，通过微调 BERT 模型获取三元组中隐藏的语义信息，并使用注意力特征嵌入方法计算正负三元组中关系和实体之间的语义注意力得分，形成软约束规则来实现语义信息的自适应增强。此外，考虑到高维向量对影响，使用 BERT-Whitening 方法降低维度，提高语义向量的效率。经实验比较，在 FB15K 和 WIN18 数据集上，所提出的方法表现更好，比原始翻译模型有约 2.6％的数值改善。