标题： 基于霍克斯的表示学习用于在无标度社区结构时序知识图谱上的推理 显示英文标题

标题： Hawkes based Representation Learning for Reasoning over Scale-free Community-structured Temporal Knowledge Graphs

摘要： 时间知识图谱（TKG）推理由于在许多实际任务中的巨大价值而成为热门话题。 TKG推理的关键在于对TKG的结构信息和演化模式进行建模。 尽管已经投入了大量努力进行TKG推理，但现实世界网络的结构和演化特性并未被考虑。 在结构方面，现实世界网络通常表现出清晰的社区结构和无标度（长尾分布）特性。 在演化方面，事件的影响会随着时间的推移而衰减。 在本文中，我们提出了一种名为基于霍克斯过程的演化表示学习网络（HERLN）的新TKG推理模型，该模型同时学习TKG的结构信息和演化模式，考虑了现实世界网络的特性：社区结构、无标度性和时间衰减性。 首先，我们在输入的TKG中找到社区，以使编码获得更相似的社区内嵌入。 其次，我们设计了一个基于霍克斯过程的关系图卷积网络来应对事件影响衰减现象。 第三，我们设计了一种条件解码方法，以缓解由长尾分布引起的对频繁实体的偏差。 实验结果表明，HERLN在最先进的模型上取得了显著改进。 显示英文摘要

摘要： Temporal knowledge graph (TKG) reasoning has become a hot topic due to its great value in many practical tasks. The key to TKG reasoning is modeling the structural information and evolutional patterns of the TKGs. While great efforts have been devoted to TKG reasoning, the structural and evolutional characteristics of real-world networks have not been considered. In the aspect of structure, real-world networks usually exhibit clear community structure and scale-free (long-tailed distribution) properties. In the aspect of evolution, the impact of an event decays with the time elapsing. In this paper, we propose a novel TKG reasoning model called Hawkes process-based Evolutional Representation Learning Network (HERLN), which learns structural information and evolutional patterns of a TKG simultaneously, considering the characteristics of real-world networks: community structure, scale-free and temporal decaying. First, we find communities in the input TKG to make the encoding get more similar intra-community embeddings. Second, we design a Hawkes process-based relational graph convolutional network to cope with the event impact-decaying phenomenon. Third, we design a conditional decoding method to alleviate biases towards frequent entities caused by long-tailed distribution. Experimental results show that HERLN achieves significant improvements over the state-of-the-art models.