SMDM: enhancing enterprise-wide master data management using semantic web technologies

  • Authors:
  • Xiaoyuan Wang;Xingzhi Sun;Feng Cao;Li Ma;Nick Kanellos;Kang Zhang;Yue Pan;Yong Yu

  • Affiliations:
  • IBM China Research Lab, China;IBM China Research Lab, China;IBM China Research Lab, China;IBM China Research Lab, China;IBM Software Group, Canada;Shanghai Jiao Tong University, China;IBM China Research Lab, China;Shanghai Jiao Tong University, China

  • Venue:
  • Proceedings of the VLDB Endowment
  • Year:
  • 2009

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Abstract

Motivated by evolving business requirements and novel enterprise applications, we propose and implement the Semantic Master Data Management (SMDM), a semantics-level enhancement to the existing MDM solutions. The SMDM system publishes relational-based master data as virtual RDF store, and injects instantaneous reasoning capabilities into semantic queries. Two kinds of ontologies are introduced to the system, the core MDM ontology and the external imported domain ontology. SMDM enables data linking among multi-domains, implicit relationship discovery, and declarative definition and extension of business policies and entities. Based on these functions, modern companies can customize their applications and services on demand within the MDM hub. In the demonstration, we build the system environment based on IBM's MDM solution, and run the use cases on the master data of an insurance company.