SMDM: Enhancing Enterprise-Wide Master Data Management Using Semantic Web Technologies
Summary: Semantic Master Data Management (SMDM) publishes relational master data as a virtual RDF store and injects instantaneous reasoning into semantic queries. Two ontologies (core MDM and external domain) enable cross-domain linking, implicit relationship discovery, and declarative policy and entity extension, demonstrated on IBM MDM with insurance data. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Xiaoyuan Wang (International Business Machines China Research Lab)
- 2. Xingzhi Sun (International Business Machines China Research Lab)
- 3. Feng Cao (International Business Machines China Research Lab)
- 4. Li Ma (International Business Machines China Research Lab)
- 5. Nick Kanellos (IBM)
- 6. Kang Zhang (Shanghai Jiao Tong University)
- 7. Yue Pan (International Business Machines China Research Lab)
- 8. Yong Yu (Shanghai Jiao Tong University)
BibTeX Citation
@article{wang_vldb09,
title = {{SMDM: Enhancing Enterprise-Wide Master Data Management Using Semantic Web Technologies}},
author = {Wang, Xiaoyuan and Sun, Xingzhi and Cao, Feng and Ma, Li and Kanellos, Nick and Zhang, Kang and Pan, Yue and Yu, Yong},
journal = {PVLDB},
series = {{VLDB} '09},
doi = {10.14778/1687553.1687600},
url = {https://doi.org/10.14778/1687553.1687600},
year = {2009}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 65 | Freebase: A Collaboratively Created Graph Database For Structuring Human Knowledge | 2008 | SIGMOD | 0.00038697603 |
| 10,045 | SOR: A Practical System for Ontology Storage, Reasoning and Search | 2007 | VLDB | 5.1703259e-05 |
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