Enhancing Data Integrity through Provenance Tracking in Semantic Web Frameworks
Abstract
This paper explores the integration of provenance tracking systems within the context of Semantic Web technologies to enhance data integrity in diverse operational environments. SURROUND Australia Pty Ltd demonstrates innovative applica-tions of the PROV Data Model (PROV-DM) and its Semantic Web variant, PROV-O, to systematically record and manage provenance information across multiple data processing domains. By employing RDF and Knowledge Graphs, SURROUND ad-dresses the critical challenges of shared entity identification and provenance granularity. The paper highlights the company's architecture for capturing comprehensive provenance data, en-abling robust validation, traceability, and knowledge inference. Through the examination of two projects, we illustrate how provenance mechanisms not only improve data reliability but also facilitate seamless integration across heterogeneous systems. Our findings underscore the importance of sophisticated provenance solutions in maintaining data integrity, serving as a reference for industry peers and academics engaged in provenance research and implementation.
Keywords
Cite
@article{arxiv.2501.09029,
title = {Enhancing Data Integrity through Provenance Tracking in Semantic Web Frameworks},
author = {Nilesh Jain},
journal= {arXiv preprint arXiv:2501.09029},
year = {2025}
}
Comments
This 10-page manuscript with 5 figures focuses on leveraging Semantic Web frameworks to enhance data integrity through provenance tracking. Intended for conference submission, it aligns with the cs.AI category, addressing knowledge representation, data modeling, and uncertainty in AI using advanced tools like PROV-DM and PROV-O