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In recently year, temporal knowledge graphs (TKG), as an important branch of knowledge graphs, have made tremendous progress. Unlike traditional knowledge graphs, TKG embed temporal information, further enhancing their ability to represent the real world. A TKG is defined as a sequence of static knowledge graphs in different timestamps. TKG composed of quadruples (s,r,o,t), which represents relation r exists between head entity s and tail entity o at timestamp t.
Knowledge graphs (KGs) act as repositories of factual information within the human world comprising a large array of data utilized throughout different intelligent applications. These applications extend from information retrieval (Gaur et al., 2022; Liu et al., 2018; Wu et al., 2023a), recommendation systems (Wang et al., 2018, 2019; Zhang et al., 2021, 2023), and question answering (Bakhshi et al., 2022; Zhang et al., 2022b, 2022c).
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