论文标题
联合语言语义和结构嵌入知识图完成
Joint Language Semantic and Structure Embedding for Knowledge Graph Completion
论文作者
论文摘要
完成知识三胞胎的任务具有广泛的下游应用程序。结构和语义信息在知识图完成中起着重要作用。与以前依赖于知识图的结构或语义的方法不同，我们建议将语义共同嵌入知识三胞胎的自然语言描述及其结构信息。我们的方法通过对概率结构化的损失进行微调预训练的语言模型嵌入了完成任务的知识图，其中语言模型的正向通过捕获语义和损失重建结构。我们对各种知识图基准的广泛实验证明了我们方法的最新性能。我们还表明，由于语义的更好使用，我们的方法可以显着提高低资源制度的性能。代码和数据集可在https://github.com/pkusjh/lass上找到。
The task of completing knowledge triplets has broad downstream applications. Both structural and semantic information plays an important role in knowledge graph completion. Unlike previous approaches that rely on either the structures or semantics of the knowledge graphs, we propose to jointly embed the semantics in the natural language description of the knowledge triplets with their structure information. Our method embeds knowledge graphs for the completion task via fine-tuning pre-trained language models with respect to a probabilistic structured loss, where the forward pass of the language models captures semantics and the loss reconstructs structures. Our extensive experiments on a variety of knowledge graph benchmarks have demonstrated the state-of-the-art performance of our method. We also show that our method can significantly improve the performance in a low-resource regime, thanks to the better use of semantics. The code and datasets are available at https://github.com/pkusjh/LASS.
