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Diachronic Embedding for Temporal Knowledge Graph Completion
Knowledge graphs (KGs) typically contain temporal facts indicating relat...
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Efficient Knowledge Graph Validation via Cross-Graph Representation Learning
Recent advances in information extraction have motivated the automatic c...
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Weakly-supervised Contextualization of Knowledge Graph Facts
Knowledge graphs (KGs) model facts about the world, they consist of node...
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KGEval: Estimating Accuracy of Automatically Constructed Knowledge Graphs
Automatic construction of large knowledge graphs (KG) by mining web-scal...
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Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification
Learning how to predict future events from patterns of past events is di...
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RTFE: A Recursive Temporal Fact Embedding Framework for Temporal Knowledge Graph Completion
In recent years, many efforts have been made to complete knowledge graph...
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Knowledge Representation in Graphs using Convolutional Neural Networks
Knowledge Graphs (KG) constitute a flexible representation of complex re...
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Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks
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 timeaware 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.
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