论文标题
搜索传递消息以获取时间知识图的完成
Search to Pass Messages for Temporal Knowledge Graph Completion
论文作者
论文摘要
完成缺失的事实是时间知识图（TKG）的基本任务。最近，基于图形神经网络（GNN）的方法可以同时探索拓扑和时间信息，已成为完成TKGS的最先进（SOTA）。但是，这些研究基于手工设计的体系结构，无法探索TKG的各种拓扑和时间特性。为了解决此问题，我们建议使用神经体系结构搜索（NAS）来设计特定数据的消息传递体系结构，以完成TKG。特别是，我们开发了一个广义框架，以探索TKG中的拓扑和时间信息。基于此框架，我们设计了一个表现力的搜索空间，以完全捕获不同TKG的各种属性。同时，我们采用了一种搜索算法，该算法通过对单个路径进行采样以较低的成本来训练超网结构。我们进一步在三个基准数据集上进行了广泛的实验。结果表明，通过我们的方法搜索的体系结构实现了SOTA性能。此外，搜索模型还可以隐式揭示不同TKG中的各种特性。我们的代码在https://github.com/striderdu/spa中发布。
Completing missing facts is a fundamental task for temporal knowledge graphs (TKGs). Recently, graph neural network (GNN) based methods, which can simultaneously explore topological and temporal information, have become the state-of-the-art (SOTA) to complete TKGs. However, these studies are based on hand-designed architectures and fail to explore the diverse topological and temporal properties of TKG. To address this issue, we propose to use neural architecture search (NAS) to design data-specific message passing architecture for TKG completion. In particular, we develop a generalized framework to explore topological and temporal information in TKGs. Based on this framework, we design an expressive search space to fully capture various properties of different TKGs. Meanwhile, we adopt a search algorithm, which trains a supernet structure by sampling single path for efficient search with less cost. We further conduct extensive experiments on three benchmark datasets. The results show that the searched architectures by our method achieve the SOTA performances. Besides, the searched models can also implicitly reveal diverse properties in different TKGs. Our code is released in https://github.com/striderdu/SPA.
