From BPMN Models to Labelled Property Graphs

There\u27s a growing interest in leveraging the structured and formal nature of business process modeling languages in order to make them available not only for human analysis but also to machine-readable knowledge representation. Standard serializations of the past were predominantly XML based, with some of them seemingly discontinued, e.g., XPDL after the dissolution of the Workflow Management Coalition. Recent research has been investigating the interplay between knowledge representation and business process modeling, with the focus typically placed on standards such as RDF and OWL. In this paper we introduce a converter that translates the standards-compliant BPMN XML format to Neo4J labelled property graphs (LPG) thus providing an alternative to both traditional XML-based serialization and to more recent experimental RDF solutions, while ensuring conceptual alignment with the standard serialization of BPMN 2.0. A demonstrator was built to highlight the benefits of having such a parser and the completeness of coverage for BPMN models. The proposal facilitates graph-based processing of business process models in a knowledge intensive context, where procedural knowledge available as BPMN diagrams must be exposed to machines and LPG-driven applications

Pragmatic design of methodology in process-based knowledge integration

This paper proposes a methodology to design a pragmatic ontology.Pragmatism can maximize the interaction between rules and ontology.A formal model provides the template for pattern identification of symbols and rules for manipulation by a logic machine.The ongoing research proposes pragmatics in knowledge engineering.However, current methodology does little to emphasize a truly pragmatic design in an ontology pattern.A critical analysis of the approach against existing methodologies has shown lack of research in this important area of ontology design

Knowledge-Driven Harmonization of Sensor Observations: Exploiting Linked Open Data for IoT Data Streams

The rise of the Internet of Things leads to an unprecedented number of continuous sensor observations that are available as IoT data streams. Harmonization of such observations is a labor-intensive task due to heterogeneity in format, syntax, and semantics. We aim to reduce the effort for such harmonization tasks by employing a knowledge-driven approach. To this end, we pursue the idea of exploiting the large body of formalized public knowledge represented as statements in Linked Open Data

Enterprise Knowledge Structures

Ell B, Simperl E, Wölger S, et al. Enterprise Knowledge Structures. In: Simperl E, Warren P, Davies J, eds. Context and Semantics for Knowledge Management. 1st Edition. Springer; 2011: 29-58