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

• URL: http://arxiv.org/abs/2210.00305v3
• Date: Thu, 14 Sep 2023 07:06:03 GMT
• Title: LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings
• Authors: Xin Xie, Zhoubo Li, Xiaohan Wang, Zekun Xi, Ningyu Zhang
• Abstract summary: We present LambdaKG, a library for 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.
• Score: 32.371086902570205
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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