论文标题
Lambdakg：基于预训练的基于语言模型的知识图嵌入的库
LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings
论文作者
论文摘要
知识图（kgs）通常具有两个特征：异质图结构和文本丰富的实体/关系信息。基于文本的KG嵌入可以通过使用预训练的语言模型编码描述来代表实体，但是目前没有专门为具有PLM的KG设计的开源库。在本文中，我们介绍了Lambdakg，这是KGE的图书馆，该库为许多预训练的语言模型（例如Bert，Bart，T5，GPT-3），并支持各种任务（例如，知识图完成，问题答案，建议，建议，建议和知识探测）。 lambdakg在https://github.com/zjunlp/promptkg/tree/main/main/lambdakg上公开开放，并在http://deepke.zjukg.cn/lambdakg.mp4 and Longenth and Longermentabence和长期维护和长期维护和长期维护。
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.
