论文标题
时间知识图形推理，具有低级别和模型不可屈服的表示
Temporal Knowledge Graph Reasoning with Low-rank and Model-agnostic Representations
论文作者
论文摘要
时间知识图完成（TKGC）已成为事件和时间知识图推理的一种流行方法，以准确但缺失的信息来针对知识的完成。在这种情况下，张量分解已成功地模拟了实体与关系之间的相互作用。它们在静态知识图完成中的有效性促使我们引入时间延时，这是一个参数效率高，且具有时间感知的扩展，对低级张量分解模型较低。我们指出了当前方法的几个限制以表示时间，我们为时间特征提出了一个周期感知的时间来编码方案，该方案是模型 - 敏捷的，并提供了更概括的时间表示。我们在统一的时间知识图嵌入框架中实现我们的方法，重点关注时间敏感的数据处理。实验表明，我们所提出的方法在两个基准上的最新语义匹配模型上执行或更好。
Temporal knowledge graph completion (TKGC) has become a popular approach for reasoning over the event and temporal knowledge graphs, targeting the completion of knowledge with accurate but missing information. In this context, tensor decomposition has successfully modeled interactions between entities and relations. Their effectiveness in static knowledge graph completion motivates us to introduce Time-LowFER, a family of parameter-efficient and time-aware extensions of the low-rank tensor factorization model LowFER. Noting several limitations in current approaches to represent time, we propose a cycle-aware time-encoding scheme for time features, which is model-agnostic and offers a more generalized representation of time. We implement our methods in a unified temporal knowledge graph embedding framework, focusing on time-sensitive data processing. The experiments show that our proposed methods perform on par or better than the state-of-the-art semantic matching models on two benchmarks.
