Knowledge Graph Embedding (KGE) has proven to be an effective approach to
solving the Knowledge Graph Completion (KGC) task. Relational patterns which
refer to relations with specific semantics exhibiting graph patterns are an
important factor in the performance of KGE models. Though KGE models'
capabilities are analyzed over different relational patterns in theory and a
rough connection between better relational patterns modeling and better
performance of KGC has been built, a comprehensive quantitative analysis on KGE
models over relational patterns remains absent so it is uncertain how the
theoretical support of KGE to a relational pattern contributes to the
performance of triples associated to such a relational pattern. To address this
challenge, we evaluate the performance of 7 KGE models over 4 common relational
patterns on 2 benchmarks, then conduct an analysis in theory, entity frequency,
and part-to-whole three aspects and get some counterintuitive conclusions.
Finally, we introduce a training-free method Score-based Patterns Adaptation
(SPA) to enhance KGE models' performance over various relational patterns. This
approach is simple yet effective and can be applied to KGE models without
additional training. Our experimental results demonstrate that our method
generally enhances performance over specific relational patterns. Our source
code is available from GitHub at
this https URL

知识图谱嵌入（KGE）是解决知识图谱完成（KGC）任务的有效方法。本文通过对 4 种常见关系模式的性能评估和理论分析，引入了一种无需额外训练的训练无关方法（SPA）来提高 KGE 模型的性能。