RTFE: A Recursive Temporal Fact Embedding Framework for Temporal Knowledge Graph Completion

Abstract

Static knowledge graph (SKG) embedding (SKGE) has been studied intensively in the past years. Recently, temporal knowledge graph (TKG) embedding (TKGE) has emerged. In this paper, we propose a Recursive Temporal Fact Embedding (RTFE) framework to transplant SKGE models to TKGs and to enhance the performance of existing TKGE models for TKG completion. Different from previous work which ignores the continuity of states of TKG in time evolution, we treat the sequence of graphs as a Markov chain, which transitions from the previous state to the next state. RTFE takes the SKGE to initialize the embeddings of TKG. Then it recursively tracks the state transition of TKG by passing updated parameters/features between timestamps. Specifically, at each timestamp, we approximate the state transition as the gradient update process. Since RTFE learns each timestamp recursively, it can naturally transit to future timestamps. Experiments on five TKG datasets show the effectiveness of RTFE.

Accepted as a main conference paper at NAACL-HLT 2021

References in corpus (8)

• RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
• SimplE Embedding for Link Prediction in Knowledge Graphs
• Tensor Decompositions for temporal knowledge base completion
• Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs
• Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition
• Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs
• Learning Sequence Encoders for Temporal Knowledge Graph Completion
• Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution