Wang Jie, Zhang Zhanqiu, Shi Zhihao, Cai Jianyu, Ji Shuiwang, Wu Feng
IEEE Trans Pattern Anal Mach Intell. 2023 Feb;45(2):1652-1667. doi: 10.1109/TPAMI.2022.3161804. Epub 2023 Jan 6.
Semantic matching models-which assume that entities with similar semantics have similar embeddings-have shown great power in knowledge graph embeddings (KGE). Many existing semantic matching models use inner products in embedding spaces to measure the plausibility of triples and quadruples in static and temporal knowledge graphs. However, vectors that have the same inner products with another vector can still be orthogonal to each other, which implies that entities with similar semantics may have dissimilar embeddings. This property of inner products significantly limits the performance of semantic matching models. To address this challenge, we propose a novel regularizer-namely, DUality-induced RegulArizer (DURA)-which effectively encourages the entities with similar semantics to have similar embeddings. The major novelty of DURA is based on the observation that, for an existing semantic matching KGE model (primal), there is often another distance based KGE model (dual) closely associated with it, which can be used as effective constraints for entity embeddings. Experiments demonstrate that DURA consistently and significantly improves the performance of state-of-the-art semantic matching models on both static and temporal knowledge graph benchmarks.
语义匹配模型（假设具有相似语义的实体具有相似的嵌入）在知识图谱嵌入（KGE）中展现出了强大的能力。许多现有的语义匹配模型在嵌入空间中使用内积来衡量静态和时态知识图谱中三元组和四元组的合理性。然而，与另一个向量具有相同内积的向量仍可能彼此正交，这意味着具有相似语义的实体可能具有不相似的嵌入。内积的这一特性显著限制了语义匹配模型的性能。为应对这一挑战，我们提出了一种新颖的正则化器，即对偶诱导正则化器（DURA），它有效地促使具有相似语义的实体具有相似的嵌入。DURA的主要新颖之处基于这样的观察：对于现有的语义匹配KGE模型（原模型），通常存在另一个与之密切相关的基于距离的KGE模型（对偶模型），它可以用作实体嵌入的有效约束。实验表明，DURA在静态和时态知识图谱基准上持续且显著地提高了当前最先进的语义匹配模型的性能。
