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The task of completing knowledge triplets has broad downstream applications.Both structural and semantic information plays an important role in knowledgegraph completion. Unlike previous approaches that rely on either the structuresor semantics of the knowledge graphs, we propose to jointly embed the semanticsin the natural language description of the knowledge triplets with theirstructure information. Our method embeds knowledge graphs for the completiontask via fine-tuning pre-trained language models with respect to aprobabilistic structured loss, where the forward pass of the language modelscaptures semantics and the loss reconstructs structures. Our extensiveexperiments on a variety of knowledge graph benchmarks have demonstrated thestate-of-the-art performance of our method. We also show that our method cansignificantly improve the performance in a low-resource regime, thanks to thebetter use of semantics. The code and datasets are available athttps://github.com/pkusjh/LASS.

Joint Language Semantic and Structure Embedding for Knowledge Graph Completion