论文标题
时间知识基础完成：新算法和评估协议
Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols
论文作者
论文摘要
时间知识基础辅助关系（s，r，o）在关系有效时具有一组时间（或一组时间）的三倍。虽然时间不足的KB完成（KBC）见证了大量研究，但暂时的KB完成（TKBC）正处于早期。在本文中，我们考虑预测缺失实体（链接预测）和缺少的时间间隔（时间预测）作为联合TKBC任务，在该任务中，实体，关系和时间都嵌入到统一的兼容空间中。我们提出了TimePlex，这是一种新颖的时间感知KBC方法，它也自动利用了关系对之间的某些关系的复发性和时间相互作用。 TimePlex在这两个预测任务上都达到了最新的性能。 我们还发现，由于不完善的评估机制，现有的TKBC模型大量高估了链接预测性能。作为回应，我们提出了针对链接和时间预测任务的改进的TKBC评估协议，以处理金在黄金实例和系统预测中的时间间隔的部分重叠引起的细微问题。
Temporal knowledge bases associate relational (s,r,o) triples with a set of times (or a single time instant) when the relation is valid. While time-agnostic KB completion (KBC) has witnessed significant research, temporal KB completion (TKBC) is in its early days. In this paper, we consider predicting missing entities (link prediction) and missing time intervals (time prediction) as joint TKBC tasks where entities, relations, and time are all embedded in a uniform, compatible space. We present TIMEPLEX, a novel time-aware KBC method, that also automatically exploits the recurrent nature of some relations and temporal interactions between pairs of relations. TIMEPLEX achieves state-of-the-art performance on both prediction tasks. We also find that existing TKBC models heavily overestimate link prediction performance due to imperfect evaluation mechanisms. In response, we propose improved TKBC evaluation protocols for both link and time prediction tasks, dealing with subtle issues that arise from the partial overlap of time intervals in gold instances and system predictions.
