RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification

Narvala, H. , McDonald, G. and Ounis, I. (2021) RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification. In: 2021 Conference on Empirical Methods in Natural Language Processing, Punta Cana, Dominican Republic, 07-11 Nov 2021, pp. 3671-3681. ISBN 9781955917100

Abstract

No abstract availableThe 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)

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Ounis, Professor Iadh and McDonald, Dr Graham and Narvala, Hitarth
• Authors: Narvala, H., McDonald, G., and Ounis, I.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Computing Science
• ISBN: 9781955917100
• Copyright Holders: Copyright © 2021 Association for Computational Linguistics
• First Published: First published in Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 3671-3681
• Publisher Policy: Reproduced with the permission of the publisher

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