用语义网技术追踪数据来源，提升跨系统数据可靠性。
Enhancing Data Integrity through Provenance Tracking in Semantic Web Frameworks
- 基于PROV-O和知识图谱记录数据全生命周期轨迹
- 实现跨异构系统间实体识别与细粒度溯源
- 适合数据治理、可信计算领域研究者参考
本文探讨在语义网技术背景下集成溯源追踪系统，以增强多样化操作环境中的数据完整性。SURROUND Australia Pty Ltd展示了PROV数据模型（PROV-DM）及其语义网变体PROV-O在多个数据处理领域的创新应用，通过RDF和知识图谱系统性地记录与管理溯源信息。该方法有效解决了共享实体识别与溯源粒度问题。论文阐述了企业级溯源架构，支持数据的可靠验证、可追溯性及知识推理。通过两个项目案例，说明溯源机制不仅提升了数据可靠性，还促进了异构系统间的无缝集成。研究强调了复杂溯源方案在保障数据完整性中的关键作用，为行业与学术界提供参考。
原文摘要 · Abstract (English)
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.
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