Pre-trained Language Model with Prompts for Temporal Knowledge Graph Completion
ACL (Findings)(2023)
Abstract
Temporal Knowledge graph completion (TKGC) is a crucial task that involves
reasoning at known timestamps to complete the missing part of facts and has
attracted more and more attention in recent years. Most existing methods focus
on learning representations based on graph neural networks while inaccurately
extracting information from timestamps and insufficiently utilizing the implied
information in relations. To address these problems, we propose a novel TKGC
model, namely Pre-trained Language Model with Prompts for TKGC (PPT). We
convert a series of sampled quadruples into pre-trained language model inputs
and convert intervals between timestamps into different prompts to make
coherent sentences with implicit semantic information. We train our model with
a masking strategy to convert TKGC task into a masked token prediction task,
which can leverage the semantic information in pre-trained language models.
Experiments on three benchmark datasets and extensive analysis demonstrate that
our model has great competitiveness compared to other models with four metrics.
Our model can effectively incorporate information from temporal knowledge
graphs into the language models.
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