Temporal Knowledge Graph (TKG) is an extension of traditional Knowledge Graph (KG) that incorporates the dimension of time. Reasoning on TKGs is a crucial task that aims to predict future facts based on historical occurrences. The key challenge lies in uncovering structural dependencies within historical subgraphs and temporal patterns. Most existing approaches model TKGs relying on entity modeling, as nodes in the graph play a crucial role in knowledge representation. However, the real-world scenario often involves an extensive number of entities, with new entities emerging over time. This makes it challenging for entity-dependent methods to cope with extensive volumes of entities, and effectively handling newly emerging entities also becomes a significant challenge. Therefore, we propose Temporal Inductive Path Neural Network (TiPNN), which models historical information in an entity-independent perspective. Specifically, TiPNN adopts a unified graph, namely history temporal graph, to comprehensively capture and encapsulate information from history. Subsequently, we utilize the defined query-aware temporal paths to model historical path information related to queries on history temporal graph for the reasoning. Extensive experiments illustrate that the proposed model not only attains significant performance enhancements but also handles inductive settings, while additionally facilitating the provision of reasoning evidence through history temporal graphs.

翻译：时序知识图谱（TKG）是传统知识图谱（KG）的扩展，引入了时间维度。基于TKG的推理是一项关键任务，旨在根据历史事件预测未来事实。其核心挑战在于揭示历史子图中的结构依赖关系与时间模式。现有方法大多依赖实体建模来构建TKG，因为图中的节点在知识表示中起着关键作用。然而，现实场景中往往涉及海量实体，且新实体会随时间不断涌现。这使得基于实体的方法难以应对大规模实体，同时有效处理新出现的实体也成为重大挑战。为此，我们提出时序归纳路径神经网络（TiPNN），从独立于实体的视角对历史信息进行建模。具体而言，TiPNN采用统一的历史时序图（history temporal graph），全面捕获并封装历史信息。随后，我们利用定义的查询感知时序路径（query-aware temporal paths），对历史时序图中与查询相关的历史路径信息进行建模，以完成推理。大量实验表明，所提模型不仅显著提升了性能，还能处理归纳设置，同时通过历史时序图提供推理依据。