Relational Database Data Lineage Ontology

2026-05-15

AI Summary

This work addresses the limitations of data lineage tracing in relational databases, which often suffers from incomplete dependency information. To overcome this challenge, the authors propose a novel ontology that integrates structural, semantic, and transformation-level features, thereby enhancing the semantic expressiveness of knowledge graphs to enable automatic lineage discovery. The approach combines a path-embedding graph neural network with an inductive link prediction framework. Experimental results demonstrate that the proposed method significantly outperforms existing baselines in terms of AUC and Hits@10 metrics, confirming the effectiveness and superiority of the introduced semantic model for data lineage modeling and inference.

Abstract

Modeling data lineage in relational databases remains a challenging problem, particularly in scenarios involving incomplete or missing dependencies between database objects. In this paper, we propose a novel ontology for relational database data lineage, designed to provide a richer and more expressive semantic representation supporting discovering the lineage links by means of knowledge graphs (KGs). Building upon our previous work on KG-based lineage discovery, the proposed ontology extends the earlier model with additional concepts capturing structural, semantic, and transformation-level characteristics of relational data. These extensions enable more precise encoding of lineage evidence. To evaluate the impact of the proposed ontology, we conduct a comparative study using a KG-based inductive link prediction framework. Specifically, we assess the performance of a graph neural network model based on path embeddings under two settings: using the original baseline ontology and the newly proposed one. Experimental results demonstrate that the application of the enriched semantic model leads to improvements in lineage link prediction performance, as measured by AUC and Hits@10 metrics.

Problem

• data lineage
• relational databases
• ontology
• knowledge graphs
• missing dependencies

Innovation

• data lineage
• ontology
• knowledge graph
• graph neural network
• relational database