DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population

TL;DR

DeepKE is an open-source toolkit that enables flexible, modular, and extensible knowledge extraction from unstructured data for knowledge base population, supporting various complex scenarios and tasks.

Contribution

It introduces a unified framework for customizable, multi-task knowledge extraction, integrating modules for different scenarios and providing comprehensive resources and online tools.

Findings

Supports low-resource and multimodal scenarios

Provides modular and extensible architecture

Offers real-time extraction system

Abstract

We present an open-source and extensible knowledge extraction toolkit DeepKE, supporting complicated low-resource, document-level and multimodal scenarios in the knowledge base population. DeepKE implements various information extraction tasks, including named entity recognition, relation extraction and attribute extraction. With a unified framework, DeepKE allows developers and researchers to customize datasets and models to extract information from unstructured data according to their requirements. Specifically, DeepKE not only provides various functional modules and model implementation for different tasks and scenarios but also organizes all components by consistent frameworks to maintain sufficient modularity and extensibility. We release the source code at GitHub in https://github.com/zjunlp/DeepKE with Google Colab tutorials and comprehensive documents for beginners. Besides, we present an online system in http://deepke.openkg.cn/EN/re_doc_show.html for real-time extraction of various tasks, and a demo video.