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

翻译：知识图谱通常具有两个特点：异构图结构和富含文本的实体/关系信息。基于文本的知识图谱嵌入可以通过预训练语言模型编码描述信息来表示实体，但目前尚无专门针对结合预训练语言模型的知识图谱设计的开源库。本文中，我们提出了LambdaKG——一个配备多种预训练语言模型（如BERT、BART、T5、GPT-3）的知识图谱嵌入库，并支持知识图谱补全、问答、推荐和知识探测等多项任务。LambdaKG已在https://github.com/zjunlp/PromptKG/tree/main/lambdaKG 开源，并提供演示视频（http://deepke.zjukg.cn/lambdakg.mp4）及长期维护支持。