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arXiv · 2210.00305

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

Xin Xie·Zhoubo Li·Xiaohan Wang·Zekun Xi·Ningyu Zhang
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Abstract

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.

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Xin Xie, Zhoubo Li, Xiaohan Wang, Zekun Xi, Ningyu Zhang. 2022-10-01. LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings. https://arxiv.org/abs/2210.00305

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