A. Banks Pidduck et al. (Eds.): CAISE 2002, LNCS 2348, pp. 230-244, 2002.  Springer-Verlag Berlin Heidelberg 2002 Towards a Data Model for Quality Management Web Services: An Ontology of Measurement for Enterprise Modeling Henry M. Kim 1 and Mark S. Fox 2 1 Schulich School of Business, York University Toronto, Ontario M3J 1P3 hkim@schulich.yorku.ca 2 Department of Mechanical and Industrial Engineering, University of Toronto Toronto, Ontario M5S 3G9 msf@mie.utoronto.ca Abstract. Though the WWW is used for business process automation to lower costs and shorten leadtimes, arguably its use has been limited for another metric of business success: Improving quality. A promising advancement to the WWW is the development of the Semantic Web, which relies upon using machine process-able domain knowledge represented in ontologies. Therefore, one promising area of research and application is the development of ontologies used as data models to provide quality management services on the Semantic Web. In this paper, the TOVE Measurement Ontology is presented as a formal model of a fundamental domain, which needs to be represented to provide these services. Measurement is fundamental for representing quality because before quality is evaluated and managed, it must first be measured. An assessment system for measuring attributes of an entity, activities for measurement, and quality as conformance to requirements are the core concepts represented in the ontology. The formal representation of measurement is emphasized over detailing context of ontology use, since this is an issue not heavily examined by the ontology community and one that needs to be detailed in order develop data models to provide Semantic Web based quality management services. 1 Introduction Using Internet technologies to enable novel business processes and link globally disparate entities has led to benefits such as lowered transaction costs and leadtimes. However, what about quality? Though technologies can be used to automate rote operational business processes, quality management processes are often knowledge intensive. It is difficult then to abstract processes steps, and automate them to