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Knowledge Graphs (KGs) often have two characteristics: heterogeneous graphstructure and text-rich entity/relation information. Text-based KG embeddingscan represent entities by encoding descriptions with pre-trained languagemodels, but no open-sourced library is specifically designed for KGs with PLMsat present. In this paper, we present LambdaKG, a library for KGE that equipswith many pre-trained language models (e.g., BERT, BART, T5, GPT-3), andsupports various tasks (e.g., knowledge graph completion, question answering,recommendation, and knowledge probing). LambdaKG is publicly open-sourced athttps://github.com/zjunlp/PromptKG/tree/main/lambdaKG, with a demo video athttp://deepke.zjukg.cn/lambdakg.mp4 and long-term maintenance.

LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings