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

Abstract

Knowledge Graphs (KGs) often have two characteristics: heterogeneous graph structure and text-rich entity/relation information. Textbased 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 pretrained 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 1 , with a demo video 2 and long-term maintenance.