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）是一项基础数据整合任务，旨在识别不同知识图谱（KGs）间的等价实体。时间知识图谱（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。最新时间感知EA研究表明，TKGs中的时间信息有助于提升EA性能。然而，现有研究未充分挖掘TKGs中时间信息的优势，且通过预对齐实体对执行EA的方式耗时且低效。本文提出DualMatch方法，有效融合关系信息与时间信息进行EA。该方法将TKG上的EA转化为加权图匹配问题。具体而言，DualMatch采用无监督方法实现EA，无需种子实体对。其包含两个步骤：（i）使用新型无标签编码器Dual-Encoder分别对时间和关系信息进行嵌入编码；（ii）通过基于图匹配的新型解码器GM-Decoder融合两类信息并转化为对齐结果。由于能够有效捕获时间信息，DualMatch可在有监督或无监督条件下执行TKG实体对齐。在三个真实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指标上提升1.7%-7.6%。