  Concepts benefit natural language understanding but are far from complete in
existing knowledge graphs (KGs). Recently, pre-trained language models (PLMs)
have been widely used in text-based concept extraction (CE). However, PLMs tend
to mine the co-occurrence associations from massive corpus as pre-trained
knowledge rather than the real causal effect between tokens. As a result, the
pre-trained knowledge confounds PLMs to extract biased concepts based on
spurious co-occurrence correlations, inevitably resulting in low precision. In
this paper, through the lens of a Structural Causal Model (SCM), we propose
equipping the PLM-based extractor with a knowledge-guided prompt as an
intervention to alleviate concept bias. The prompt adopts the topic of the
given entity from the existing knowledge in KGs to mitigate the spurious
co-occurrence correlations between entities and biased concepts. Our extensive
experiments on representative multilingual KG datasets justify that our
proposed prompt can effectively alleviate concept bias and improve the
performance of PLM-based CE models.The code has been released on
https://github.com/siyuyuan/KPCE.
