@inproceedings{narvala-etal-2021-reldiff-enriching,
title = "{R}el{D}iff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification",
author = "Narvala, Hitarth and
McDonald, Graham and
Ounis, Iadh",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.311",
doi = "10.18653/v1/2021.findings-emnlp.311",
pages = "3671--3681",
abstract = "The relationships that exist between entities can be a reliable indicator for classifying sensitive information, such as commercially sensitive information. For example, the relation person-IsDirectorOf-company can indicate whether an individual{'}s salary should be considered as sensitive personal information. Representations of such relations are often learned using a knowledge graph to produce embeddings for relation types, generalised across different entity-pairs. However, a relation type may or may not correspond to a sensitivity depending on the entities that participate to the relation. Therefore, generalised relation embeddings are typically insufficient for classifying sensitive information. In this work, we propose a novel method for representing entities and relations within a single embedding to better capture the relationship between the entities. Moreover, we show that our proposed entity-relation-entity embedding approach can significantly improve (McNemar{'}s test, p {\textless}0.05) the effectiveness of sensitivity classification, compared to classification approaches that leverage relation embedding approaches from the literature. (0.426 F1 vs 0.413 F1)",
}
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<abstract>The relationships that exist between entities can be a reliable indicator for classifying sensitive information, such as commercially sensitive information. For example, the relation person-IsDirectorOf-company can indicate whether an individual’s salary should be considered as sensitive personal information. Representations of such relations are often learned using a knowledge graph to produce embeddings for relation types, generalised across different entity-pairs. However, a relation type may or may not correspond to a sensitivity depending on the entities that participate to the relation. Therefore, generalised relation embeddings are typically insufficient for classifying sensitive information. In this work, we propose a novel method for representing entities and relations within a single embedding to better capture the relationship between the entities. Moreover, we show that our proposed entity-relation-entity embedding approach can significantly improve (McNemar’s test, p \textless0.05) the effectiveness of sensitivity classification, compared to classification approaches that leverage relation embedding approaches from the literature. (0.426 F1 vs 0.413 F1)</abstract>
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%0 Conference Proceedings
%T RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification
%A Narvala, Hitarth
%A McDonald, Graham
%A Ounis, Iadh
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F narvala-etal-2021-reldiff-enriching
%X The relationships that exist between entities can be a reliable indicator for classifying sensitive information, such as commercially sensitive information. For example, the relation person-IsDirectorOf-company can indicate whether an individual’s salary should be considered as sensitive personal information. Representations of such relations are often learned using a knowledge graph to produce embeddings for relation types, generalised across different entity-pairs. However, a relation type may or may not correspond to a sensitivity depending on the entities that participate to the relation. Therefore, generalised relation embeddings are typically insufficient for classifying sensitive information. In this work, we propose a novel method for representing entities and relations within a single embedding to better capture the relationship between the entities. Moreover, we show that our proposed entity-relation-entity embedding approach can significantly improve (McNemar’s test, p \textless0.05) the effectiveness of sensitivity classification, compared to classification approaches that leverage relation embedding approaches from the literature. (0.426 F1 vs 0.413 F1)
%R 10.18653/v1/2021.findings-emnlp.311
%U https://aclanthology.org/2021.findings-emnlp.311
%U https://doi.org/10.18653/v1/2021.findings-emnlp.311
%P 3671-3681
Markdown (Informal)
[RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification](https://aclanthology.org/2021.findings-emnlp.311) (Narvala et al., Findings 2021)
ACL