Papers › Semantic Framework based Query Generation for Temporal Question Answering over Knowledge Graphs
Semantic Framework based Query Generation for Temporal Question Answering over Knowledge Graphs
Wentao Ding, Hao Chen, Huayu Li, Yuzhong Qu
Answering factual questions with temporal intent over knowledge graphs (temporal KGQA) attracts rising attention in recent years. In the generation of temporal queries, existing KGQA methods ignore the fact that some intrinsic connections between events can make them temporally related, which may limit their capability. We systematically analyze the possible interpretation of temporal constraints and conclude the interpretation structures as the Semantic Framework of Temporal Constraints, SF-TCons. Based on the semantic framework, we propose a temporal question answering method, SF-TQA, which generates query graphs by exploring the relevant facts of mentioned entities, where the exploring process is restricted by SF-TCons. Our evaluations show that SF-TQA significantly outperforms existing methods on two benchmarks over different knowledge graphs.
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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 | SF-TQA | F1 | 41.1 | #1 of 8 | Archive leaderboard | report |
| Question Answering | TempQuestions | SF-TQA | Hits@1 | 41.2 | #1 of 8 | Archive leaderboard | report |
| Question Answering | TimeQuestions | SF-TQA | P@1 | 53.9 | #5 of 21 | 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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