Enhancing Data Integrity through Provenance Tracking in Semantic Web Frameworks
It addresses data integrity challenges for industries and academics using Semantic Web technologies, presenting an incremental application of existing models.
This paper tackles the problem of data integrity in Semantic Web frameworks by integrating provenance tracking systems using PROV-DM and PROV-O, demonstrating improved reliability and integration across heterogeneous systems through two projects.
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.