Enhancing Data Integrity through Provenance Tracking in Semantic Web Frameworks

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. I. INTRODUCTION Encompass Australia Pty Ltd ("Encompass") is a little however unique innovation organization that has some expertise in giving state of the art simulated intelligence and information the executives items to both government and confidential area markets. Established with the mission to change how associations make due, cycle, and influence information, Encompass has quickly secured itself as a forerunner in the field by offering special and high level arrangements. At the center of Encompass' contributions lies its refined utilization of Semantic Web information, an innovative methodology that separates the organization from its rivals. Encompass solidly accepts that the Semantic Web is the best method for safeguarding significance after some time, empowering frameworks and hierarchical changes without the deficiency of basic setting.