Model Integration

A semantic framework for enabling model integration for biorefining

Authors Linsey Koo Nikolaos Trokanas Franjo Cecelja

https://doi.org/10.1016/j.compchemeng.2017.02.004
Abstract
This paper introduces a new paradigm for establishing a framework that enables interoperability between process models and datasets using ontology engineering.
Semantics are used to model the knowledge in the domain of biorefining including both tacit and explicit knowledge, which supports registration and instantiation of the models and datasets.
Semantic algorithms allow the formation of model integration through input/output matching based on semantic relevance between the models and datasets. In addition, partial matching is employed to facilitate flexibility to broaden the horizon to find opportunities in identifying an appropriate model and/or dataset. The proposed algorithm is implemented as a web service and demonstrated using a case study.

About authors

Franjo Cecelja has email f.cecelja@surrey.ac.uk and homepage https://www.surrey.ac.uk/cpe/people/franjo cecelja/.