Prompting Disentangled Embeddings for Knowledge Graph Completion with
  Pre-trained Language Model
Yuxia Geng Jiaoyan Chen Yuhang Zeng Zhuo Chen Wen Zhang Jeff Z. Pan Yuxiang Wang Xiaoliang Xu
Both graph structures and textual information play a critical role in
Knowledge Graph Completion (KGC). With the success of Pre-trained Language
Models (PLMs) such as BERT, they have been applied for text encoding for KGC.
However, the current methods mostly prefer to fine-tune PLMs, leading to huge
training costs and limited scalability to larger PLMs. In contrast, we propose
to utilize prompts and perform KGC on a frozen PLM with only the prompts
trained. Accordingly, we propose a new KGC method named PDKGC with two prompts
-- a hard task prompt which is to adapt the KGC task to the PLM pre-training
task of token prediction, and a disentangled structure prompt which learns
disentangled graph representation so as to enable the PLM to combine more
relevant structure knowledge with the text information. With the two prompts,
PDKGC builds a textual predictor and a structural predictor, respectively, and
their combination leads to more comprehensive entity prediction. Solid
evaluation on two widely used KGC datasets has shown that PDKGC often
outperforms the baselines including the state-of-the-art, and its components
are all effective. Our codes and data are available at
https://github.com/genggengcss/PDKGC.