  Temporal knowledge graph (TKG) reasoning has two settings: interpolation
reasoning and extrapolation reasoning. Both of them draw plenty of research
interest and have great significance. Methods of the former de-emphasize the
temporal correlations among facts sequences, while methods of the latter
require strict chronological order of knowledge and ignore inferring clues
provided by missing facts of the past. These limit the practicability of TKG
applications as almost all of the existing TKG reasoning methods are designed
specifically to address either one setting. To this end, this paper proposes an
original Temporal PAth-based Reasoning (TPAR) model for both the interpolation
and extrapolation reasoning. TPAR performs a neural-driven symbolic reasoning
fashion that is robust to ambiguous and noisy temporal data and with fine
interpretability as well. Comprehensive experiments show that TPAR outperforms
SOTA methods on the link prediction task for both the interpolation and the
extrapolation settings. A novel pipeline experimental setting is designed to
evaluate the performances of SOTA combinations and the proposed TPAR towards
interpolation and extrapolation reasoning. More diverse experiments are
conducted to show the robustness and interpretability of TPAR.
