论文标题
TFLEX：用于时间知识图的复杂推理的时间功能逻辑嵌入框架
TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph
论文作者
论文摘要
多跳的逻辑推理在知识图上（KG）在许多人工智能任务中起着基本作用。最近的复杂查询嵌入方法（CQE）方法用于推理静态kg，而时间知识图（TKG）尚未得到充分探索。 TKGS推理有两个挑战：1。查询应回答实体或时间戳； 2。操作员应考虑实体集合的设置逻辑和时间戳设置的时间逻辑。为了弥合这一差距，我们在TKGS上定义了多跳的逻辑推理问题。使用生成的三个数据集，我们提出了第一个名为“时间特征逻辑嵌入框架”（TFLEX）的时间CQE来回答时间复杂查询。我们利用矢量逻辑来计算时间特征逻辑嵌入的逻辑部分，因此自然地对实体集对所有一阶逻辑（FOL）操作进行了建模。此外，我们的框架将矢量逻辑扩展到时间戳设置，以应对三个额外的临时操作员（之后，之前和之间）。许多查询模式的实验证明了我们方法的有效性。
Multi-hop logical reasoning over knowledge graph (KG) plays a fundamental role in many artificial intelligence tasks. Recent complex query embedding (CQE) methods for reasoning focus on static KGs, while temporal knowledge graphs (TKGs) have not been fully explored. Reasoning over TKGs has two challenges: 1. The query should answer entities or timestamps; 2. The operators should consider both set logic on entity set and temporal logic on timestamp set. To bridge this gap, we define the multi-hop logical reasoning problem on TKGs. With generated three datasets, we propose the first temporal CQE named Temporal Feature-Logic Embedding framework (TFLEX) to answer the temporal complex queries. We utilize vector logic to compute the logic part of Temporal Feature-Logic embeddings, thus naturally modeling all First-Order Logic (FOL) operations on entity set. In addition, our framework extends vector logic on timestamp set to cope with three extra temporal operators (After, Before and Between). Experiments on numerous query patterns demonstrate the effectiveness of our method.
