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TopIntroduction
Temporal knowledge graphs (TKGs) store entities, their relationships, and time information with a structured knowledge representation. They are widely used to facilitate downstream tasks in the field of artificial intelligence, such as recommender systems (Schall, 2015) and natural language understanding (Choudhury et al., 2022). A TKG stores knowledge in the form of 
, where 
is the subject entity, 
is the object entity, 
denotes the relation between entities, and 
represents a specific timestamp or a time interval with beginning time and ending time. Typical TKGs include ICEWS14 (García-Durán et al., 2018), DELT (Trivedi et al., 2017), and Wikidata (Erxleben et al., 2014) which contain temporal facts; for example, the triple 
is valid only from January 2021.
Since most TKGs are developed independently, existing TKGs are often incomplete but complementary to each other. Thus, several TKG fusion approaches are put forward and attempt to combine several TKGs into a single and comprehensive one. As an important stage of TKG fusion, temporal entity alignment (TEA) is the task of detecting the equivalent entities (i.e., the entities that refer to the same object) from different TKGs.
The task of TEA is inherently challenging from at least the following aspects:
- •
Usage of Time Information: In TKGs, most of the events have specific time stamps or time intervals, such as 
and 
. If the observer neglecst the time information and only considers the relation 
and the object entity 
, they may mistakenly match 
and 
. Figure 1 provides a more specific example. Therefore, in TEA, it is critical to make good use of the time information.
- •
Heterogeneity: Since most different TKGs are constructed individually and obtain source data from various channels, the same entities in different graphs may have different relations and time information with other entities. Different TKGs may cover the different parts of factual events, which makes matching more difficult. For instance, in Figure 2, the two entities 
and 
refer to the same entity in the real world, but it is hard to match them since they are associated with different events.