Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks
By Cunchao Zhu, Muhao Chen, Changjun Fan, Guangquan Cheng, Yang Zhan. AAAI Conference on Artificial Intelligence, 2020
366 citations recorded by the source corpus.
Large knowledge graphs often grow to store temporal facts that model the dynamic relations or interactions of entities along the timeline. Since such temporal knowledge graphs often suffer from incompleteness, it is important to develop time-aware representation learning models that help to infer the missing temporal facts. While the temporal facts are typically evolving, it is observed that many facts often show a repeated pattern along the timeline, such as economic crises and diplomatic activities. This observation indicates that a model could potentially learn much from the known facts appeared in history. To this end, we propose a new representation learning model for temporal knowledge graphs, namely CyGNet, based on a novel time-aware copy-generation mechanism. CyGNet is not only able to predict future facts from the whole entity vocabulary, but also capable of identifying facts with repetition and accordingly predicting such future facts with reference to the known facts in the past. We evaluate the proposed method on the knowledge graph completion task using five benchmark datasets. Extensive experiments demonstrate the effectiveness of CyGNet for predicting future facts with repetition as well as de novo fact prediction.
Subfields: Artificial Intelligence, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/40cfa0263e7d39d4a61cfcb844a5093c8c283152
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In Artificial Intelligence
In Management Science and Operations Research
Citations in the corpus
- A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-Modal (2022, background): “CyGNet [11] attempts to solve the entity prediction task by encoding the historical facts related to the subject entity in each query and generating the time-indexing vector.”