Papers › TEQUILA: Temporal Question Answering over Knowledge Bases
TEQUILA: Temporal Question Answering over Knowledge Bases
Zhen Jia, Abdalghani Abujabal, Rishiraj Saha Roy, Jannik Stroetgen, Gerhard Weikum
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Question Answering | TempQuestions | AQQU+TEQUILA | F1 | 37.5 | #2 of 8 | Archive leaderboard | report |
| Question Answering | TempQuestions | AQQU+TEQUILA | Hits@1 | 36.2 | #2 of 8 | Archive leaderboard | report |
| Question Answering | TempQuestions | QUINT+TEQUILA | F1 | 32 | #3 of 8 | Archive leaderboard | report |
| Question Answering | TempQuestions | QUINT+TEQUILA | Hits@1 | 31.7 | #3 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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