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Knowledge Graph Completion (KGC) often requires both KG structural andtextual information to be effective. Pre-trained Language Models (PLMs) havebeen used to learn the textual information, usually under the fine-tuneparadigm for the KGC task. However, the fine-tuned PLMs often overwhelminglyfocus on the textual information and overlook structural knowledge. To tacklethis 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 aregenerated by the entities and relations representations. We verify theeffectiveness of CSProm-KG on three popular static KGC benchmarks WN18RR,FB15K-237 and Wikidata5M, and two temporal KGC benchmarks ICEWS14 andICEWS05-15. CSProm-KG outperforms competitive baseline models and sets newstate-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.

Dipping PLMs Sauce: Bridging Structure and Text for Effective Knowledge Graph Completion via Conditional Soft Prompting