A Methodology to develop process ontology from organizational guidelines written in natural language

Date

2017

Author

Gürbüz, Özge

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Integrating ontologies with process modeling improves data representations and makes it easier to query, store and reuse processes at the semantics level. Therefore, in recent years, this topic has become increasingly popular. The studies in the literature have proposed methods for the integration process either to relate domain ontologies to process models or to transform process models to process ontologies. Another way to establish the integration between ontologies and process models is to develop process ontologies from organizational sources. Since most organizations have guidelines in natural language, it requires significant amount of time and effort to extract the roles, activities, information carriers, business rules, and relationships for process ontology development. In this thesis, a new Process Ontology Population (PrOnPo) methodology and tool is proposed that will automatically develop process ontology by extracting process information from organizational guidelines (regulations, procedures, directives and policies written in natural language). This approach will not only minimize the effort and time required for process ontology development, will also address the natural language ambiguity and provide an input for process modeling, hence improve the semantic quality of the business process models and their consistency with process ontology. As a part of this thesis work, two exploratory studies, a multiple case study and an experiment were performed in order to explore, generalize and validate the proposed approach.

Subject Keywords

Electronic commerce., Consumer profiling., Machine learning., Artificial intelligence.

URI

http://etd.lib.metu.edu.tr/upload/12621039/index.pdf
https://hdl.handle.net/11511/26518

Collections

Graduate School of Informatics, Thesis

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Citation Formats

Ö. Gürbüz, “A Methodology to develop process ontology from organizational guidelines written in natural language,” Ph.D. - Doctoral Program, Middle East Technical University, 2017.