# TEQUILA: Temporal Question Answering over Knowledge Bases

## TL;DR

TEQUILA is a method that enhances knowledge base question answering by effectively handling complex temporal questions through decomposition, temporal constraint reasoning, and integration with existing KB-QA systems.

## Contribution

It introduces a novel four-stage approach for temporal question answering that can be applied on top of any KB-QA engine, improving handling of temporal queries.

## Key findings

- TEQUILA outperforms state-of-the-art baselines.
- It effectively decomposes complex temporal questions.
- The method accurately computes answers using temporal constraint reasoning.

## Abstract

Question answering over knowledge bases (KB-QA) poses challenges in handling complex questions that need to be decomposed into sub-questions. An important case, addressed here, is that of temporal questions, where cues for temporal relations need to be discovered and handled. We present TEQUILA, an enabler method for temporal QA that can run on top of any KB-QA engine. TEQUILA has four stages. It detects if a question has temporal intent. It decomposes and rewrites the question into non-temporal sub-questions and temporal constraints. Answers to sub-questions are then retrieved from the underlying KB-QA engine. Finally, TEQUILA uses constraint reasoning on temporal intervals to compute final answers to the full question. Comparisons against state-of-the-art baselines show the viability of our method.