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- W3202624196 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 <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)" @default.
- W3202624196 created "2021-10-11" @default.
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- W3202624196 date "2021-01-01" @default.
- W3202624196 modified "2023-09-28" @default.
- W3202624196 title "RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification" @default.
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- W3202624196 doi "https://doi.org/10.18653/v1/2021.findings-emnlp.311" @default.
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