Ant Group and Zhejiang University jointly release OneKE, an open source large model knowledge extraction framework

Recently, OneKE, a large model knowledge extraction framework jointly developed by Ant Group and Zhejiang University, was announced as open source and donated to the OpenKG open knowledge graph community.

Knowledge graph is one of the key technologies to achieve trustworthiness and controllability of large models. Knowledge extraction can help build domain knowledge graphs. OneKE is committed to helping researchers and developers better handle issues such as information extraction, text data structuring, and knowledge graph construction.

Extracting risk events, person entities, institutional entities, etc. through OneKE can clearly present the event context, event development trends, and correlations between entities. The well-constructed graph can help large models realize complex reasoning across entities and documents. . OneKE is bilingual in Chinese and English, supports OpenSPG and DeepKE open source frameworks, and can be used out of the box.

Large language models have significantly improved the ability of artificial intelligence systems to process world knowledge. However, real-world information is highly fragmented and unstructured, so when large language models handle information extraction tasks, they will still have poor results due to the huge difference between the extracted content and natural language expressions; in addition, natural language text information There are many ambiguities, polysemy, metaphors, etc., which bring greater challenges to the knowledge extraction task. This also leads to the fact that generative artificial intelligence represented by large language models still has problems such as insufficient reasoning ability, lack of factual knowledge, and unstable generation results, which greatly hinders the industrialization of large language models.

The unified knowledge extraction framework can significantly reduce the cost of building domain knowledge graphs and has a wide range of application scenarios. This means that by extracting structured knowledge from massive data, building high-quality knowledge graphs and establishing logical connections between knowledge elements, explainable reasoning decisions can be achieved, and it can also be used to enhance large models to alleviate illusions and improve stability. , accelerating the application of large models in vertical fields.

In the medical field, knowledge management of doctors’ experience is realized through knowledge extraction, and controllable auxiliary diagnosis and treatment and medical Q&A are constructed. In the financial field, the knowledge extraction department is used for financial indicators, risk events, causal relationships, industrial chains, etc. to achieve automatic financial research report generation, risk prediction, industrial chain analysis, etc. In government affairs scenarios, the knowledge of government affairs regulations can be realized, improving the efficiency and accurate decision-making of government affairs services.

To accelerate the industrial implementation of production-based artificial intelligence, Ant Group and Zhejiang University have established a joint knowledge graph laboratory to focus on the construction of knowledge graphs enhanced by large models, trusted and controllable generation functions of knowledge enhancement, and domain knowledge. We have launched all-round cooperation on topics such as the World Graph, with a view to establishing a controllable generation functional paradigm with two-way enhancement of large language models and knowledge graphs through joint technical research.

Ant Group and Zhejiang University jointly established and upgraded the capabilities of the Ant Bailing large model in the field of knowledge extraction, and released the Chinese-English bilingual large model knowledge extraction framework OneKE. They also open sourced a version based on LLaMA2 full-parameter fine-tuning. Test indicators show that OneKE has achieved relatively good results on multiple fully supervised and zero-sample entity/relationship/event extraction tasks.

OneKE is an excellent bilingual generalizable knowledge extraction tool in Chinese and English. It performs well on Chinese NER named entity recognition tasks, RE relationship extraction tasks, and EE event extraction tasks. Relatively good results have been achieved.

Liang Lei, head of Ant Group’s knowledge graph, said that Ant will continue to optimize the performance of knowledge extraction to serve the controllable and trustworthy needs of large models in different scenarios. In the future, we will work with industry partners to apply relevant technical systems to various vertical fields such as finance, medical care, and government affairs, and promote the industrial implementation of controllable generation technology dual-driven by knowledge graphs and large language models.

OneKE official homepage: http://oneke.openkg.cn/

OpenSPG GitHub: https://github.com/OpenSPG/openspg

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