Temporal Knowledge Graph Question Answering with Self-Improvement Programming

The core message of this paper is to propose a semantic-parsing-based framework called Prog-TQA for Temporal Knowledge Graph Question Answering (TKGQA) that leverages the in-context learning ability of Large Language Models (LLMs) to generate programs with designed temporal operators, and further enhances the LLM's understanding of complex temporal questions through an effective self-improvement strategy.