RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification
Hitarth Narvala, Graham McDonald, Iadh Ounis · 2021
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 < 0.05) the effectiveness of sensitivity classification, compared to classification approaches that leverage relation embedding approaches from the literature (0.426 F 1 vs 0.413 F 1 ).