FS-Net: 面向时序知识图谱推理的频次统计网络

时序知识图谱推理吸引了研究人员的极大关注.现有的时序知识图谱推理技术通过建模历史信息取得了巨大的进步.但是,时变性问题和不可见实体(关系)问题仍然是阻碍时序知识图谱推理模型性能进一步提升的两大挑战;而且由于需要对历史子图序列的结构信息和时间依赖信息进行建模,传统的基于嵌入的方法往往在训练和预测过程中具有较高的时间消耗,这极大地限制了推理模型在现实场景中的应用.针对以上困境,提出了一个用于时序知识图谱推理的频次统计网络, FS-Net.一方面, FS-Net不断基于最新的短期历史的事实频次统计,动态地为变化的时间戳上的预测生成时变的得分;另一方面,FS-Net基于当前时间戳上的事实频次统计,为预测补充历史不可见实体(关系);特别地,FS-Net不需要进行训练,而且具有极高的时间效率.在两个时序知识图谱基准数据集上的大量实验,表明了FS-Net相较于基准模型的巨大提升.

FS-Net: Frequency Statistical Network for Temporal Knowledge Graph Reasoning

Temporal knowledge graph (TKG) reasoning has attracted significant attention of researchers. Existing TKG reasoning methods have made great progress through modeling historical information. However, the time-variability problem and unseen entity (relation) problem are still two major challenges that hinder the further improvement of this field; moreover, since the structural information and temporal dependencies of the historical subgraph sequence have to be modeled, the traditional embedding-based methods often have high time consumption in the training and predicting processes, which greatly limits the application of the reasoning model in real-world scenarios. To address these issues, this paper proposes a frequency statistical network for TKG reasoning, namely FS-Net. On the one hand, FS-Net continuously generates time-varying scores for the predictions at the changing timestamps based on the latest short-term historical fact frequency statistics; on the other hand, based on the fact frequency statistics at the current timestamp, FS-Net supplements the historical unseen entities (relations) for the predictions; specially, FS-Net does not need training, and has a very high time efficiency. Plenty of experiments on two TKG benchmark datasets demonstrate that FS-Net has a great improvement compared with the baseline models.