@inproceedings{rezayi-etal-2021-edge,
title = "Edge: Enriching Knowledge Graph Embeddings with External Text",
author = "Rezayi, Saed and
Zhao, Handong and
Kim, Sungchul and
Rossi, Ryan and
Lipka, Nedim and
Li, Sheng",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.221",
doi = "10.18653/v1/2021.naacl-main.221",
pages = "2767--2776",
abstract = "Knowledge graphs suffer from sparsity which degrades the quality of representations generated by various methods. While there is an abundance of textual information throughout the web and many existing knowledge bases, aligning information across these diverse data sources remains a challenge in the literature. Previous work has partially addressed this issue by enriching knowledge graph entities based on {``}hard{''} co-occurrence of words present in the entities of the knowledge graphs and external text, while we achieve {``}soft{''} augmentation by proposing a knowledge graph enrichment and embedding framework named Edge. Given an original knowledge graph, we first generate a rich but noisy augmented graph using external texts in semantic and structural level. To distill the relevant knowledge and suppress the introduced noise, we design a graph alignment term in a shared embedding space between the original graph and augmented graph. To enhance the embedding learning on the augmented graph, we further regularize the locality relationship of target entity based on negative sampling. Experimental results on four benchmark datasets demonstrate the robustness and effectiveness of Edge in link prediction and node classification.",
}
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<abstract>Knowledge graphs suffer from sparsity which degrades the quality of representations generated by various methods. While there is an abundance of textual information throughout the web and many existing knowledge bases, aligning information across these diverse data sources remains a challenge in the literature. Previous work has partially addressed this issue by enriching knowledge graph entities based on “hard” co-occurrence of words present in the entities of the knowledge graphs and external text, while we achieve “soft” augmentation by proposing a knowledge graph enrichment and embedding framework named Edge. Given an original knowledge graph, we first generate a rich but noisy augmented graph using external texts in semantic and structural level. To distill the relevant knowledge and suppress the introduced noise, we design a graph alignment term in a shared embedding space between the original graph and augmented graph. To enhance the embedding learning on the augmented graph, we further regularize the locality relationship of target entity based on negative sampling. Experimental results on four benchmark datasets demonstrate the robustness and effectiveness of Edge in link prediction and node classification.</abstract>
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%0 Conference Proceedings
%T Edge: Enriching Knowledge Graph Embeddings with External Text
%A Rezayi, Saed
%A Zhao, Handong
%A Kim, Sungchul
%A Rossi, Ryan
%A Lipka, Nedim
%A Li, Sheng
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F rezayi-etal-2021-edge
%X Knowledge graphs suffer from sparsity which degrades the quality of representations generated by various methods. While there is an abundance of textual information throughout the web and many existing knowledge bases, aligning information across these diverse data sources remains a challenge in the literature. Previous work has partially addressed this issue by enriching knowledge graph entities based on “hard” co-occurrence of words present in the entities of the knowledge graphs and external text, while we achieve “soft” augmentation by proposing a knowledge graph enrichment and embedding framework named Edge. Given an original knowledge graph, we first generate a rich but noisy augmented graph using external texts in semantic and structural level. To distill the relevant knowledge and suppress the introduced noise, we design a graph alignment term in a shared embedding space between the original graph and augmented graph. To enhance the embedding learning on the augmented graph, we further regularize the locality relationship of target entity based on negative sampling. Experimental results on four benchmark datasets demonstrate the robustness and effectiveness of Edge in link prediction and node classification.
%R 10.18653/v1/2021.naacl-main.221
%U https://aclanthology.org/2021.naacl-main.221
%U https://doi.org/10.18653/v1/2021.naacl-main.221
%P 2767-2776
Markdown (Informal)
[Edge: Enriching Knowledge Graph Embeddings with External Text](https://aclanthology.org/2021.naacl-main.221) (Rezayi et al., NAACL 2021)
ACL
- Saed Rezayi, Handong Zhao, Sungchul Kim, Ryan Rossi, Nedim Lipka, and Sheng Li. 2021. Edge: Enriching Knowledge Graph Embeddings with External Text. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2767–2776, Online. Association for Computational Linguistics.