A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is
Unsupervised Knowledge Graph Generation Using Semantic Similarity Matching
Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing
Knowledge Graphs (KGs) are directed labeled graphs representing entities and the relationships between them. Most prior work focuses on supervised or semi-supervised approaches which require large amounts of annotated data. While unsupervised approaches do not need labeled training data, most existing methods either generate too many redundant relations or require manual mapping of the extracted relations to a known schema. To address these limitations, we propose an unsupervised method for KGdoi:10.18653/v1/2022.deeplo-1.18 fatcat:swvzmif7t5awzgdfsknxski6oi