The paper reports on an early iteration of a Design Science effort for defining a business process analytics method. The method hybridizes explicitly engineered knowledge and implicit knowledge, as it streamlines the following ingredients: BPMN modeling, semantic linking and the transformation of models into RDF graphs, natural language processing and network analysis applied on the resulting graph repository. The engineered knowledge comes in the form of a BPMN implementation that can transform diagrams into RDF, whereas the implicit knowledge is derived from analytic measures (similarity metrics, network analysis) that further annotate the graphs obtained from models, enabling richer semantic queries and filtering possibilities for Business Process Management use cases. The originating problem context consists of contract management and project management scenarios from which use cases will be exemplified. The proposed method is deployable as an orchestration of tools: the BEE-UP modeling environment, GraphDB for storage and Python libraries (rdflib, nltk, networkx) for processing the graphs and running the annotating analytics.

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