DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
This toolkit addresses the problem of knowledge base population for developers and researchers by providing an extensible solution, though it is incremental as it builds on existing methods.
The authors introduced DeepKE, an open-source toolkit for knowledge extraction that supports low-resource, document-level, and multimodal scenarios, implementing tasks like named entity recognition and relation extraction with a unified framework for customization.
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.