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PromptKG: A Prompt Learning Framework for Knowledge Graph Representation Learning and Application

2022-10-01 16:01:53
Xin Xie, Zhoubo Li, Xiaohan Wang, Shumin Deng, Feiyu Xiong, Huajun Chen, Ningyu Zhang
arXiv_CL
           
Abstract

Knowledge Graphs (KGs) often have two characteristics: heterogeneous graph structure and text-rich entity/relation information. KG representation models should consider graph structures and text semantics, but no comprehensive open-sourced framework is mainly designed for KG regarding informative text description. In this paper, we present PromptKG, a prompt learning framework for KG representation learning and application that equips the cutting-edge text-based methods, integrates a new prompt learning model and supports various tasks (e.g., knowledge graph completion, question answering, recommendation, and knowledge probing). PromptKG is publicly open-sourced at this https URL with long-term technical support.

Abstract (translated)

URL

https://arxiv.org/abs/2210.00305

PDF

https://arxiv.org/pdf/2210.00305.pdf

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