D eep KE : A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population (2022)

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.

## introduction as information extraction (ie) techniques develop fast, many large-scale knowledge bases (kbs) have been constructed. those kbs can provide back-end support for knowledge-intensive tasks in real-world applications, such as language understanding @xcite , commonsense reasoning @xcite and recommendation systems @xcite . however, most kbs are far from complete due to the emerging entities and relations in real-world applications. therefore, knowledge base population (kbp) @xcite has been proposed, which aims to extract knowledge from the text corpus to complete the missing elements in kbs. for this target, ie is an effective technology that can extract entities and relations from raw texts and link them to kbs @xcite . to date, a few remarkable open-source and long-term maintained ie toolkits have been developed, such as spacy @xcite for named entity recognition (ner), opennre @xcite for relation extraction (re), stanford ope-nie @xcite for open information extraction, resin for event extraction @xcite and so on @xcite . however, there are still several non-trivial issues that hinder the applicability of real-world applications. firstly, there are various important ie tasks, but most existing toolkits only support one task. secondly, although ie models trained with those tools can achieve promising results, their performance may degrade dramatically when there are only a few training instances or in other complex realworld scenarios, such as encountering documentlevel and multimodal instances. therefore, it is necessary to build a knowledge extraction toolkit facilitating the knowledge base population that supports multiple tasks and complicated scenarios: low-resource, document-level and multimodal. in this paper, we share with the community a new open-source knowledge extraction toolkit called deepke (mit license), which supports knowledge extraction tasks (named entity recognition, relation extraction and attribute extraction) in the standard supervised setting and three complicated scenarios: low-resource, document-level and multimodal settings. to facilitate usage, we design a unified framework for data processing, model training and evaluation. developers and researchers can quickly customize their datasets and models for [loc] ## core functions deepke is designed for different knowledge extraction tasks, including named entity recognition, relation extraction and attribute extraction. as shown in figure 1 , deepke supports diverse ie tasks in standard single-sentence supervised, low-resource few-shot, document-level and multimodal settings, which makes it flexible to adapt to practical and complicated application scenarios. ## toolkit design and implementation we introduce the design principle of deepke as follows: 1) unified framework: deepke utilizes the same framework for various task objectives with respect to data, model and core components; 2) flexible usage: deepke offers convenient training and evaluation with auto-hyperparameter tuning and the docker for operational efficiency; 3) off-the-shelf models: deepke provides pretrained models (chinese models with pre-defined schemas) for information extraction. we will introduce details of components in deepke and the unified framework in the following sections. ## conclusion in practical application, the knowledge base population struggles with low-resource, document-level and multimodal scenarios. to this end, we propose deepke, an open-source and extensible knowledge extraction toolkit. we conduct extensive experiments that demonstrate the models implemented by deepke can achieve comparable performance compared to some state-of-the-art methods. besides, we provide an online system supporting realtime extraction (with the pre-defined schemas) without training. we will offer long-term maintenance to fix bugs, solve issues, add documents (tutorials) and meet new requests.