StreamE: Learning to Update Representations for Temporal Knowledge Graphs in Streaming Scenarios

Learning representations for temporal knowledge graphs (TKGs) is a fundamental task. Most existing methods regard TKG as a sequence of static snapshots and recurrently learn representations by retracing the previous snapshots. However, new knowledge can be continuously accrued to TKGs as streams. These methods either cannot handle new entities or fail to update representations in real time, making them unfeasible to adapt to the streaming scenarios. In this paper, we propose a lightweight framework called StreamE towards the efficient generation of TKG representations in streaming scenarios. To reduce the parameter size, entity representations in StreamE are decoupled from the model training to serve as the memory module to store the historical information of entities. To achieve efficient update and generation, the process of generating representations is decoupled as two functions in StreamE. An update function is learned to incrementally update entity representations based on the newly-arrived knowledge and a read function is learned to predict the future semantics of entity representations. The update function avoids the recurrent modeling paradigm and thus gains high efficiency while the read function considers multiple semantic change properties. We further propose a joint training strategy with two temporal regularizations to effectively optimize the framework. Experimental results show that StreamE can achieve better performance than baseline methods with 100x faster in inference, 25x faster in training, and only 1/5 parameter size, which demonstrates its superiority. Code is available at https://github.com/zjs123/StreamE.