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Knowledge Graph Completion (KGC) has been recently extended to multipleknowledge graph (KG) structures, initiating new research directions, e.g.static KGC, temporal KGC and few-shot KGC. Previous works often design KGCmodels closely coupled with specific graph structures, which inevitably resultsin two drawbacks: 1) structure-specific KGC models are mutually incompatible;2) existing KGC methods are not adaptable to emerging KGs. In this paper, wepropose KG-S2S, a Seq2Seq generative framework that could tackle differentverbalizable graph structures by unifying the representation of KG facts into"flat" text, regardless of their original form. To remedy the KG structureinformation loss from the "flat" text, we further improve the inputrepresentations of entities and relations, and the inference algorithm inKG-S2S. Experiments on five benchmarks show that KG-S2S outperforms manycompetitive baselines, setting new state-of-the-art performance. Finally, weanalyze KG-S2S's ability on the different relations and the Non-entityGenerations.

Knowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completion