2025/01/12 by Nilesh Jain, Jain, Nilesh
Computer Science · Decision Sciences · #68P15: Covers knowledge representation #68T30 #68T35 #Artificial Intelligence (cs.AI) #Cloud Data Security Solutions #Cryptography and Security (cs.CR) #Data Quality and Management #FOS: Computer and information sciences #Scientific Computing and Data Management #Semantic Web applications #and database theory for provenance tracking and data integrity
paper · pdf · doi:10.48550/arxiv.2501.09029
openalex publication_date 2025/01/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.