Temporal Knowledge Graph Reasoning Triggered by Memories

by   Mengnan Zhao, et al.

Inferring missing facts in temporal knowledge graphs is a critical task and has been widely explored. Extrapolation in temporal reasoning tasks is more challenging and gradually attracts the attention of researchers since no direct history facts for prediction. Previous works attempted to apply evolutionary representation learning to solve the extrapolation problem. However, these techniques do not explicitly leverage various time-aware attribute representations, i.e. the reasoning performance is significantly affected by the history length. To alleviate the time dependence when reasoning future missing facts, we propose a memory-triggered decision-making (MTDM) network, which incorporates transient memories, long-short-term memories, and deep memories. Specifically, the transient learning network considers transient memories as a static knowledge graph, and the time-aware recurrent evolution network learns representations through a sequence of recurrent evolution units from long-short-term memories. Each evolution unit consists of a structural encoder to aggregate edge information, a time encoder with a gating unit to update attribute representations of entities. MTDM utilizes the crafted residual multi-relational aggregator as the structural encoder to solve the multi-hop coverage problem. We also introduce the dissolution learning constraint for better understanding the event dissolution process. Extensive experiments demonstrate the MTDM alleviates the history dependence and achieves state-of-the-art prediction performance. Moreover, compared with the most advanced baseline, MTDM shows a faster convergence speed and training speed.



There are no comments yet.


page 1


Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning

Knowledge Graph (KG) reasoning that predicts missing facts for incomplet...

Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks

Large knowledge graphs often grow to store temporal facts that model the...

TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion

Inferring missing facts in temporal knowledge graphs (TKGs) is a fundame...

A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion

While knowledge graphs contain rich semantic knowledge of various entiti...

Multi-range Reasoning for Machine Comprehension

We propose MRU (Multi-Range Reasoning Units), a new fast compositional e...

Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning

A Temporal Knowledge Graph (TKG) is a sequence of KGs corresponding to d...

Physics-Informed Deep Neural Networks for Transient Electromagnetic Analysis

In this paper, we propose a deep neural network based model to predict t...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.