{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/reldiff-enriching-knowledge-graph-relation","title":"RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification","arxiv_id":null,"date":"2021-11-01","proceeding":"Findings (EMNLP) 2021 11","authors":["Hitarth Narvala","Graham McDonald","Iadh Ounis"],"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)","url_abs":"https://aclanthology.org/2021.findings-emnlp.311","url_pdf":"https://aclanthology.org/2021.findings-emnlp.311.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"entity-embeddings","task_name":"Entity Embeddings"},{"task_slug":"knowledge-graph-embeddings","task_name":"Knowledge Graph Embeddings"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"sensitivity","task_name":"Sensitivity"},{"task_slug":"sensitivity-classification","task_name":"Sensitivity Classification"},{"task_slug":"text-classification","task_name":"Text Classification"}],"methods":[{"method_slug":"reldiff","method_name":"RelDiff"}],"datasets_introduced":[],"methods_introduced":[{"slug":"reldiff","name":"RelDiff","full_name":"RelDiff"}],"results":[{"leaderboard":"/sota/sensitivity-classification-on-govsensitivity","task":"Sensitivity Classification","dataset":"GovSensitivity","model":"RelDiff","rank_in_archive_order":1,"of":1,"metrics":{"F1":"0.426"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}