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From knowledge management point of view, business process models and ontologies are two essential knowledge artifacts for organizations that consume similar information sources. In this study, the PROMPTUM method for integrated process modeling and ontology development that adheres to well-established practices is presented. The method is intended to guide practitioners who develop both ontologies and business process models in the same or similar domains.

The method is supported by a recently developed toolset, which supports the modeling of relations between the ontologies and the labels within the process model collections. This study introduces the method and its companion toolset. An explanatory study, that includes two case studies, is designed and conducted to reveal and validate the benefits of using the method. Then, a follow-up semi-structured interview identifies the perceived benefits of the method.

Application of the method revealed several benefits including the improvements observed in the consistency and completeness of the process models and ontologies. The method is bringing the best practices in two domains together and guiding the use of labels within process model collections in ontology development and ontology resources in business process modeling.

The proposed method with its tool support is a pioneer in enabling to manage the labels and terms within the labels in process model collections consistently with ontology resources. Establishing these relations enables the definition and management of process model elements as resources in domain ontologies. Once the PROMPTUM method is utilized, a related resource is managed as a single resource representing the same real-world object in both artifacts. An explanatory study has shown that improvement in consistency and completeness of process models and ontologies is possible with integrated process modeling and ontology development.

Integrating ontologies with process modeling has gained increasing attention in recent years since it enhances data representations and makes it easier to query, store and reuse knowledge at the semantic level. The authors focused on a process and ontology integration approach by extracting the activities, roles and other concepts related to the process models from organizational sources using natural language processing techniques. As part of this study, a process ontology population (PrOnPo) methodology and tool is developed, which uses natural language parsers for extracting and interpreting the sentences and populating an event-driven process chain ontology in a fully automated or semi-automated (user assisted) manner. The purpose of this paper is to present applications of PrOnPo tool in different domains.

A multiple case study is conducted by selecting five different domains with different types of guidelines. Process ontologies are developed using the PrOnPo tool in a semi-automated and fully automated fashion and manually. The resulting ontologies are compared and evaluated in terms of time-effort and recall-precision metrics.

From five different domains, the results give an average of 70 percent recall and 80 percent precision for fully automated usage of the PrOnPo tool, showing that it is applicable and generalizable. In terms of efficiency, the effort spent for process ontology development is decreased from 250 person-minutes to 57 person-minutes (semi-automated).

The PrOnPo tool is the first one to automatically generate integrated process ontologies and process models from guidelines written in natural language.

The purpose of this paper is to review the literature on quality model development, validation and limitations.

The systematic literature review used online journal indexes between January 1995 and April 2010. International studies focusing on multiple functional domains and those in which development methods were selected. Two reviewers assessed all studies and 18 were shortlisted.

Literature reviews, peer reviews, questionnaires and expert panels are the most frequently used model development methods. Expert judges were widely used to validate the models. The most important limitation was that key indicators were missing.

Existing healthcare quality models are not comprehensive and there is no consensus on targets, clinical areas or diseases.