Abstract

Data imputation addresses the challenge of imputing missing values in database instances, ensuring consistency with the overall semantics of the dataset. Although several heuristics which rely on statistical methods, and ad-hoc rules have been proposed. These do not generalise well and often lack data context. Consequently, they also lack explainability. The existing techniques also mostly focus on the relational data context making them unsuitable for wider application contexts such as in graph data. In this paper, we propose a graph data imputation approach called GIG which relies on graph differential dependencies (GDDs). GIG, learns the GDDs from a given knowledge graph, and uses these rules to train a transformer model which then predicts the value of missing data within the graph. By leveraging GDDs, GIG incoporates semantic knowledge into the data imputation process making it more reliable and explainable. Experimental results on seven real-world datasets highlight GIG’s effectiveness compared to existing state-of-the-art approaches.

Original languageEnglish
Title of host publicationDatabases Theory and Applications - 35th Australasian Database Conference, ADC 2024, Proceedings
EditorsTong Chen, Yang Cao, Quoc Viet Hung Nguyen, Thanh Tam Nguyen
PublisherSpringer Science and Business Media Deutschland GmbH
Pages347-358
Number of pages12
ISBN (Print)9789819612413
DOIs
Publication statusPublished - 2025
Event35th Australasian Database Conference, ADC 2024 - Gold Coast, Australia
Duration: 16 Dec 202418 Dec 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume15449 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference35th Australasian Database Conference, ADC 2024
Country/TerritoryAustralia
CityGold Coast
Period16/12/2418/12/24

Fingerprint

Dive into the research topics of 'GIG: Graph Data Imputation With Graph Differential Dependencies'. Together they form a unique fingerprint.

Cite this