Unsupervised Knowledge Graph Generation Using Semantic Similarity Matching

Lixian Liu, Amin Omidvar, Zongyang Ma, Ameeta Agrawal, Aijun An


Abstract
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 KG generation that requires neither labeled data nor manual mapping to the predefined relation schema. Instead, our method leverages sentence-level semantic similarity for automatically generating relations between pairs of entities. Our proposed method outperforms two baseline systems when evaluated over four datasets.
Anthology ID:
2022.deeplo-1.18
Volume:
Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing
Month:
July
Year:
2022
Address:
Hybrid
Editors:
Colin Cherry, Angela Fan, George Foster, Gholamreza (Reza) Haffari, Shahram Khadivi, Nanyun (Violet) Peng, Xiang Ren, Ehsan Shareghi, Swabha Swayamdipta
Venue:
DeepLo
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
169–179
Language:
URL:
https://aclanthology.org/2022.deeplo-1.18
DOI:
10.18653/v1/2022.deeplo-1.18
Bibkey:
Cite (ACL):
Lixian Liu, Amin Omidvar, Zongyang Ma, Ameeta Agrawal, and Aijun An. 2022. Unsupervised Knowledge Graph Generation Using Semantic Similarity Matching. In Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing, pages 169–179, Hybrid. Association for Computational Linguistics.
Cite (Informal):
Unsupervised Knowledge Graph Generation Using Semantic Similarity Matching (Liu et al., DeepLo 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.deeplo-1.18.pdf
Code
 lixianliu12/kgss
Data
DBpediaTACRED