Linsey Koo

A semantic framework for enabling model integration for biorefining

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.

Model Integration Using Ontology Input-Output Matching

Abstract This paper introduces ontology controlled model integration framework using input-output matching in the domain of biorefining. The framework builds upon the existing framework and replaces the Common Object Request Broker Architecture (CORBA) object bus with more flexible semantic repository. Semantic Web Services Description Ontologies (OWL-S) are used to describe model inputs, outputs, preconditions, operating environment and its functionality. The OWL-S enables the automation of model integration through (i) discovery, (ii) selection, (iii) composition, and (iv) execution stages. This concept has been verified with a small scale model integration to demonstrate the flexibility of model integration through all four stages of the process.

A Holistic Approach to Model Discovery Using A Domain Ontology

This paper introduces a knowledge representation of process system models and data in the domain of biorefining. The semantic approach for model and data discovery is supported by the explicit description of the models and data representing biorefining technologies and their characterisation with matching properties. The domain ontology enables the process of registering models and data, instantiation of ontology through parsing, as well as facilitation of flexible model and data discovery. The proposed method of model and data discovery has been demonstrated using a process flowsheet related to biorefining process and the concomitant performance of the method has been verified.

A Semantic Web Engine for Biorefining Model Integration.

The number of models available to the biorefining community is continuously increasing, there is a need to provide better ways for their description, categorization, discovery and integration in order to improve reusability of them. Biorefining models on the other hand are developed using heterogeneous methods, data format and various environments that makes their reuse challenging. In this paper, we describe a semantic web engine for the domain of biorefining, which enhances the description of biorefining model by using semantic web technologies in order to facilitate discovery and integration. In particular, we present how domain and web service ontologies are used in semantic mapping for the purpose of model integration, which is achieved by OWL-S (Semantic Markup for Web Services). Whilst the application has been designed and implemented for a specific domain, this novel design can be applicable to similar problems in other domains.

A Semantic Repository for Model Integration in Biorefining

Abstract Solving complex problems in biorefining associated with the process or unit design, process synthesis and analysis, or pure understanding the potential, heavily rely on modelling and simulation, the activity which remains implicit to the engineers who built them and hence limited to their use. The importance of model reusability, therefore, has been realised and the major contribution towards their interoperability and hence integration was provided by computer aided process engineering (CAPE) community (Belaud & Pons 2002). We argue that existence of a flexible, readily available and simple to use model repository is a precondition for model sharing and reusing and hence for model integration towards solving particular problems. This paper proposes a semantic repository which enables model registration, model discovery and concomitantly model integration. The functionality of repository is fully controlled by a biorefining domain ontology implemented using Ontology Web Language (OWL) and published on semantic web. The functionality of the repository has been tested using several biorefining related scenarios.

Integration of CAPE Models and Data for the Domain of Biorefining: InterCAPEmodel Ontology Design

Abstract A design of InterCAPEmodel ontology, which contains a comprehensive description to represent the knowledge of models and data in the biorefining domain, is presented. Primarily, the InterCAPEmodel ontology aims at providing implicit knowledge that reflects process synthesis logic, and explicit knowledge including a complete set of input/output types and the parameters associated with each model and dataset to manage the repository. At present, the InterCAPEmodel ontology supports integration of model and/or data. To fully exploit the potential of providing the description of the model and data to sufficiently support semantic integration, the design of knowledge model is described and the use of ontology that demonstrates its functionality is presented using a case study of a lignocellulosic based biorefining models and data at supply chain level.

Towards a Methodology for Reusable Ontology Engineering: Application to the Process Engineering Domain

Abstract This paper proposes a methodology for ontological engineering with an aim to develop reusable ontologies. The proposed methodology combines experience of developing ontology engineering using a ‘good’ practice with established methodologies and concomitantly implementing reusability in the eSymbiosis ontology.

Ontology engineering approach to support process of model integration

Abstract Process modelling and simulation is a vital tool to plan, evaluate, assess, and develop different alternatives for the design of products and processes. The complexity of problems as well as heterogeneity of modelling methods make process modelling and simulation challenging, time consuming and often tedious process requiring a wide range of expertise. Inconsistencies in model development are the main cause for redundant work. Models remain implicit to the engineers who have built them, which further limits the potential of reusability. The only model integration framework in use, the CAPE-OPEN, addresses the issue of standardisation of interfaces to enable interoperability between simulator software components from different sources. It is the framework built around a middleware, the Common Object Request Broker Architecture (CORBA) that hosts communication between unit operations defined for a specific function and the process modelling environments. The standard specification is defined as a property package which is needed for a thermodynamic or physical property calculation. The interoperability of models, such as model selection, parameter identification, and experimental work is enabled through the connection related to the unit operations and physical properties. It is not necessary to match all parameters in order to facilitate Input-Output (I-O) matching. However, the shortcoming of the CAPE-OPEN is in the need for identifying key variables for each unit operation. In this paper a new approach for model integration is proposed which builds upon the CAPE-OPEN framework and proposes the use of ontology and replaces the object bus with more flexible semantic repository (Koo, Trokanas and Cecelja, 2017). Models are described by Semantic Web Services (SWS) using Ontology Web Service Description (OWL-S) as an enabler of web services through service discovery, selection, composition, and execution stages (Figure 1). The discovery stage allows formation of an integrated model through matching requests from a public repository(ies). The best match that satisfy the requestor’s functionality is selected in the model selection stage. The model composition stage then formulates the chain of integrated models and execution of integrated model takes place during the execution stage. This paper focuses on the matching parameters related to the domain of process system engineering, with emphasis placed on the role of physical properties and unit operation. Each model representing a device (e.g. unit operations, flowsheets, subflowsheets etc.) is semantically described in domain ontology including domain assumptions and descriptions of the functionality of the model. The domain ontology guides the process of registering models and instantiation of ontology through ontology parsing, which makes the model and data explicit and sharing terminology in domain ontology improves consistency. The devices in a process are connected via streams that transmit information through multiple inlets and outlets from one unit to the other. The connection between devices are described in ontology by introducing the concept of ‘ports’ and ‘connections’. The ‘ports’ generally describe inlets and outlets of devices and three different types of streams are distinguished: material, energy, and information, which are further described by objective properties. The ‘connections’ are the object that is responsible for establishing a link between two ports, which contains information regarding methods, types, quantities, and units of streams.