Entity alignment (EA) is a fundamental data integration task that identifies equivalent entities between different knowledge graphs (KGs). Temporal Knowledge graphs (TKGs) extend traditional knowledge graphs by introducing timestamps, which have received increasing attention. State-of-the-art time-aware EA studies have suggested that the temporal information of TKGs facilitates the performance of EA. However, existing studies have not thoroughly exploited the advantages of temporal information in TKGs. Also, they perform EA by pre-aligning entity pairs, which can be labor-intensive and thus inefficient. In this paper, we present DualMatch which effectively fuses the relational and temporal information for EA. DualMatch transfers EA on TKGs into a weighted graph matching problem. More specifically, DualMatch is equipped with an unsupervised method, which achieves EA without necessitating seed alignment. DualMatch has two steps: (i) encoding temporal and relational information into embeddings separately using a novel label-free encoder, Dual-Encoder; and (ii) fusing both information and transforming it into alignment using a novel graph-matching-based decoder, GM-Decoder. DualMatch is able to perform EA on TKGs with or without supervision, due to its capability of effectively capturing temporal information. Extensive experiments on three real-world TKG datasets offer the insight that DualMatch outperforms the state-of-the-art methods in terms of H@1 by 2.4% - 10.7% and MRR by 1.7% - 7.6%, respectively.
翻译:实体对齐(EA) 是一项基本的数据整合任务, 确定不同知识图( KGs) 之间的等效实体 。 时间知识图( TKGs) 通过引入时间戳来扩展传统知识图, 这引起了越来越多的关注。 最先进的时间觉悟EA 研究表明, TKGs 的时间信息有助于EA的运行。 然而, 现有的研究并没有彻底利用TKGs 中时间信息的优势。 另外, 它们通过预先对齐实体对齐来执行EA, 这可以是劳动密集型的, 因而效率低下 。 在本文件中, 我们展示了 DualMatch(TalMatchs), 它有效地将EAGs 上的Speal和时间段信息连接到一个加权图表匹配问题。 更具体地说, DialMatch 配备了一种不超超前的方法, 在不需要种子调整的情况下实现EAG1 的时间信息。 双匹配有两个步骤:(i) 将时间和关联信息用新的无标签化实体对齐, 可能是劳动密集型的, 因而效率低下 。 (ii) 将信息同时使用信息, 将它转化为直径直径定位, 的GMGMs- deal- demat- degrealmax mill massal 上, 将它运行的3 的直径定位, 。