AEGI: Anchor Event Guided Inference for TKGQA

Temporal knowledge graph question answering (TKGQA) aims to answer temporal questions by leveraging dynamic knowledge, which is crucial for various downstream tasks. In this process, aligning the temporal questions and TKGs presents a significant challenge. Current methods either perform implicit similarity ranking through time-aware representations or concentrate on explicit semantic parsing and LLM-based interpretable reasoning. The first approach often struggles with a lack of semantic understanding when dealing with question-rich content, making it challenging to manage complex temporal information. The second approach typically relies on external knowledge retrieval, which can be adversely affected by noise from inaccuracies in the retrieval process that occurs before answering the questions. To overcome these limitations, we introduce an Anchor Event Guided Inference (AEGI) framework. This framework comprises a coarse-grained answer retrieval process and a TKG fact retrieval chain, providing both implicit and explicit reasoning perspectives for LLM-based TKGQA. By aligning temporal evidence with anchor events, AEGI improves knowledge consistency and reduces hallucinations in LLMs, enabling interpretable and accurate reasoning. Extensive experiments on two TKGQA datasets, MultiTQ, TimeQuestions demonstrate the effectiveness of our approach. The code is available at: https://github.com/yqingFU1007/AEGI-public .