  Temporal knowledge graph (TKG) reasoning aims to predict the future missing
facts based on historical information and has gained increasing research
interest recently. Lots of works have been made to model the historical
structural and temporal characteristics for the reasoning task. Most existing
works model the graph structure mainly depending on entity representation.
However, the magnitude of TKG entities in real-world scenarios is considerable,
and an increasing number of new entities will arise as time goes on. Therefore,
we propose a novel architecture modeling with relation feature of TKG, namely
aDAptivE path-MemOry Network (DaeMon), which adaptively models the temporal
path information between query subject and each object candidate across history
time. It models the historical information without depending on entity
representation. Specifically, DaeMon uses path memory to record the temporal
path information derived from path aggregation unit across timeline considering
the memory passing strategy between adjacent timestamps. Extensive experiments
conducted on four real-world TKG datasets demonstrate that our proposed model
obtains substantial performance improvement and outperforms the
state-of-the-art up to 4.8% absolute in MRR.
