  Knowledge Graph Completion (KGC) often requires both KG structural and
textual information to be effective. Pre-trained Language Models (PLMs) have
been used to learn the textual information, usually under the fine-tune
paradigm for the KGC task. However, the fine-tuned PLMs often overwhelmingly
focus on the textual information and overlook structural knowledge. To tackle
this issue, this paper proposes CSProm-KG (Conditional Soft Prompts for KGC)
which maintains a balance between structural information and textual knowledge.
CSProm-KG only tunes the parameters of Conditional Soft Prompts that are
generated by the entities and relations representations. We verify the
effectiveness of CSProm-KG on three popular static KGC benchmarks WN18RR,
FB15K-237 and Wikidata5M, and two temporal KGC benchmarks ICEWS14 and
ICEWS05-15. CSProm-KG outperforms competitive baseline models and sets new
state-of-the-art on these benchmarks. We conduct further analysis to show (i)
the effectiveness of our proposed components, (ii) the efficiency of CSProm-KG,
and (iii) the flexibility of CSProm-KG.
