Towards a Collaborative Process Platform: Publishing Processes according to the Linked Data Principles

Research in the area of process modeling and analysis has a long-established tradition. Process modeling is among others used in the medical domain to define an ideal workflow in order to ensure an efficient treatment of patients. These processes are often defined and maintained by multiple persons. Furthermore, multiple persons are interested in these defined processes to compare them with own defined processes for improvements purposes. Current solutions provide tools to model processes locally and export them in standard formats in order to exchange them. Besides, there are some collaboration tools available to model processes collaboratively and see changes dynamically. However, these solutions do not publish the data according to the Linked Data principles. Enriching processes with semantic information is useful in order to perform enhanced analysis. However, different users can only provide particular meta-information on same process steps. To address these problems we 1) developed an intuitive, open-source extension for Semantic MediaWiki that supports the graphical modeling of processes and stores the information in a structured way; 2) enable to enrich the processes with semantics from ontologies and knowledge graphs with references to external data sources 3) provide adapted views on meta-information in order to not overwhelm users with unnecessary information.