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 emporal nductive ath eural etwork (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 on a history temporal graph to model historical path information related to queries for 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.

中文翻译：

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用于时间知识图推理的时间归纳路径神经网络

时态知识图谱（TKG）是传统知识图谱（KG）的扩展，融入了时间维度。TKG 推理是一项至关重要的任务，旨在根据历史事件预测未来事实。关键的挑战在于揭示历史子图和时间模式中的结构依赖性。大多数现有方法都依赖于实体建模来对 TKG 进行建模，因为图中的节点在知识表示中起着至关重要的作用。然而，现实世界的场景通常涉及大量实体，并且随着时间的推移新的实体不断出现。这使得依赖于实体的方法难以应对大量实体，并且有效处理新出现的实体也成为一项重大挑战。因此，我们提出了 emporal nducing ath eural etwork (TiPNN)，它以独立于实体的视角对历史信息进行建模。具体来说，TiPNN采用统一的图，即历史时序图，全面捕获和封装历史信息。随后，我们利用历史时间图上定义的查询感知时间路径来对与推理查询相关的历史路径信息进行建模。大量的实验表明，所提出的模型不仅获得了显着的性能增强，而且还可以处理归纳设置，同时还有助于通过历史时间图提供推理证据。