MMKE: A Multi-Model Knowledge Extraction System from Unstructured Texts

Authors

  • Qian-Wen Zhang Tencent
  • Zhao Yan Tencent
  • Tianyang Zhao Tencent Beihang University
  • Shi-Wei Zhang Tencent
  • Meng Yao Tencent
  • Meng-Liang Rao Tencent
  • Yunbo Cao Tencent

Keywords:

Knowledge Extraction, Multi-task Learning, Overlapping Problem, Non-predefined Relation

Abstract

In this work, we present a Multi-Model Knowledge Extraction (MMKE) System which consists of two unstructured text extraction models (RelationSO model and SubjectRO model) based on a multi-task learning framework. Instead of recognizing entity first and then predicting relationships between entity pairs in previous works, MMKE detects subject and corresponding relationships before extracting objects to cope with the diverse object-type problem, overlapping problem and non-predefined relation problem. Our system accepts unstructured text as input, from which it automatically extracts triplets knowledge (subject, relation, object). More importantly, we incorporate a number of user-friendly extraction functionalities, such as multi-format uploading, one-click extractions, knowledge editing and graphical displays. The demonstration video is available at this link: https://youtu.be/HtOPJrGhSxk.

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Published

2021-05-18

How to Cite

Zhang, Q.-W., Yan, Z., Zhao, T., Zhang, S.-W., Yao, M., Rao, M.-L., & Cao, Y. (2021). MMKE: A Multi-Model Knowledge Extraction System from Unstructured Texts. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 16124-16126. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/18032