发表机构
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SCoP通过结构化约束解析将时间决策外部化，实现证据空间控制，在TKGQA中无需参数更新即在MultiTQ和TimelineCronQ-R上取得领先性能。
AI 中文摘要
时间知识图谱问答（TKGQA）要求从既在结构上有效又在时间上可接受的证据中进行答案推理。现有方法通常将锚定事件绑定、时间可接受性和序数选择隐含在模型推理、任务特定训练或相似性驱动的检索中，使得局部相关但无效的事实进入答案上下文。我们将复杂TKGQA表述为证据空间控制，并提出SCoP（结构化约束解析），一个以约束为中心的框架，在答案推理之前将时间决策外部化。SCoP不将检索到的事实默认视为可接受的证据，而是将寻求答案的事件模式与时间锚定事件分离，保守地将它们映射到规范化的TKG实体和关系，并将时间意图转化为带有可选排序要求的可执行约束。这些约束在规范化的点和区间范围上操作，能够对结构兼容的候选进行确定性过滤，并为生成产生紧凑的证据空间。在MultiTQ和TimelineCronQ-R上的实验评估了SCoP在带时间戳的点事实和区间导向设置中的表现，这些设置具有更丰富的时间关系和排序依赖。在没有任务特定参数更新的情况下，SCoP在MultiTQ上达到0.825的Hits@1，在TimelineCronQ-R上达到0.761的Hits@1，并在约束密集型问题类型上获得提升。这些结果支持显式证据空间控制优于无约束检索或隐式时间推理。
英文摘要
Temporal Knowledge Graph Question Answering (TKGQA) requires answer inference from evidence that is both structurally valid and temporally admissible. Existing methods often leave anchor-event binding, temporal admissibility, and ordinal selection implicit in model reasoning, task-specific training, or similarity-driven retrieval, allowing locally relevant but invalid facts to enter the answer context. We formulate complex TKGQA as evidence-space control and propose SCoP (Structured Constraint Parsing), a constraint-centric framework that externalizes temporal decisions before answer inference. Instead of treating retrieved facts as admissible evidence by default, SCoP separates answer-seeking event patterns from temporal anchor events, conservatively grounds them to canonical TKG entities and relations, and translates temporal intent into executable constraints with optional ranking requirements. These constraints operate over normalized point and interval ranges, enabling deterministic filtering of structurally compatible candidates and producing a compact evidence space for generation. Experiments on MultiTQ and TimelineCronQ-R assess SCoP across timestamped point-fact and interval-oriented settings with richer temporal relations and ordering dependencies. Without task-specific parameter updates, SCoP achieves 0.825 Hits@1 on MultiTQ and 0.761 Hits@1 on TimelineCronQ-R, with gains on constraint-intensive question types. These results support explicit evidence-space control over unconstrained retrieval or implicit temporal reasoning.
