A Unified Temporal Knowledge Graph Reasoning Model Towards Interpolation and Extrapolation

TL;DR

This paper introduces TPAR, a unified model for temporal knowledge graph reasoning that effectively handles both interpolation and extrapolation tasks, demonstrating superior performance and interpretability.

Contribution

The paper presents TPAR, a novel neural-symbolic reasoning model that unifies interpolation and extrapolation in TKG reasoning, addressing limitations of existing methods.

Findings

TPAR outperforms SOTA on link prediction for both tasks

Designed a new evaluation pipeline for TKG reasoning

Demonstrates robustness and interpretability of TPAR

Abstract

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.