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

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 knowledge in the form of (subject, relation, object) triples. More importantly, we incorporate a number of userfriendly 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.

Authors

• Qian-Wen Zhang
• Zhao Yan
• Tianyang Zhao AWS AI Labs University of California, Los Angeles
• Shi-Wei Zhang
• Meng Yao
• Meng-Liang Rao
• Yunbo Cao

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