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）是一项基础性数据集成任务，旨在识别不同知识图谱（KGs）间的等价实体。时序知识图谱（TKGs）通过引入时间戳扩展了传统知识图谱，近年来受到广泛关注。现有最先进的时间感知型EA研究表明，TKGs中的时序信息有助于提升EA性能。然而，现有研究尚未充分挖掘TKGs中时序信息的优势，且常通过预对齐实体对执行EA，这种方法劳动密集且效率低下。本文提出DualMatch方法，有效融合了关系信息与时序信息进行EA。DualMatch将TKGs上的EA问题转化为加权图匹配问题。具体而言，DualMatch采用无监督方法，无需种子对齐即可实现EA。该方法包含两个步骤：（i）利用新型无标签编码器Dual-Encoder分别将时序信息和关系信息编码为嵌入表示；（ii）通过基于图匹配的新型解码器GM-Decoder融合两种信息并将其转化为对齐结果。由于能有效捕获时序信息，DualMatch支持在监督或无监督条件下对TKGs执行EA。在三个真实世界TKG数据集上的大量实验表明，DualMatch在H@1指标上超越现有最优方法2.4%-10.7%，在MRR指标上超越1.7%-7.6%。