  Temporal Knowledge Graph (TKG) reasoning that forecasts future events based
on historical snapshots distributed over timestamps is denoted as extrapolation
and has gained significant attention. Owing to its extreme versatility and
variation in spatial and temporal correlations, TKG reasoning presents a
challenging task, demanding efficient capture of concurrent structures and
evolutional interactions among facts. While existing methods have made strides
in this direction, they still fall short of harnessing the diverse forms of
intrinsic expressive semantics of TKGs, which encompass entity correlations
across multiple timestamps and periodicity of temporal information. This
limitation constrains their ability to thoroughly reflect historical
dependencies and future trends. In response to these drawbacks, this paper
proposes an innovative reasoning approach that focuses on Learning Multi-graph
Structure (LMS). Concretely, it comprises three distinct modules concentrating
on multiple aspects of graph structure knowledge within TKGs, including
concurrent and evolutional patterns along timestamps, query-specific
correlations across timestamps, and semantic dependencies of timestamps, which
capture TKG features from various perspectives. Besides, LMS incorporates an
adaptive gate for merging entity representations both along and across
timestamps effectively. Moreover, it integrates timestamp semantics into graph
attention calculations and time-aware decoders, in order to impose temporal
constraints on events and narrow down prediction scopes with historical
statistics. Extensive experimental results on five event-based benchmark
datasets demonstrate that LMS outperforms state-of-the-art extrapolation
models, indicating the superiority of modeling a multi-graph perspective for
TKG reasoning.
