Elsevier

Computers in Industry

Volume 58, Issue 5, June 2007, Pages 428-437
Computers in Industry

A manufacturing system engineering ontology model on the semantic web for inter-enterprise collaboration

https://doi.org/10.1016/j.compind.2006.09.015Get rights and content

Abstract

This paper investigates ontology-based approaches for representing information semantics and in particular the World Wide Web. A general manufacturing system engineering (MSE) knowledge representation scheme, called an MSE ontology model, to facilitate communication and information exchange in inter-enterprise, multi-disciplinary engineering design teams has been developed and encoded in the standard semantic web language. The proposed approach focuses on how to support information autonomy that allows the individual team members to keep their own preferred languages or information models rather than requiring them all to adopt standardized terminology. The MSE ontology model provides efficient access by common mediated meta-models across all engineering design teams through semantic matching. This paper also shows how the primitives of Web Ontology Language (OWL) can be used for expressing simple mappings between the mediated MSE ontology model and individual ontologies.

Introduction

The growing complexity of manufacturing information and the increasing amount of knowledge and information required by a wide variety of users has made it increasingly difficult to share and exchange knowledge between companies. The escalating use of the Internet has also accelerated the amount and complexity of manufacturing digital information. Manufacturing projects that operate within inter-enterprise environments additionally face the problem that different information models are likely to be used by different parts of the manufacturing project teams. Engineers working within a particular company or group will inevitably develop their own vocabulary, or common terms for particular issues, elements or activities and these will need to be adjusted to be more practical and to precisely meet the requirements of different projects or teams. Hence, when people are brought together from different groups or companies, two common types of problem can occur in communications that share and exchange information, firstly, that the same term is being applied to different concepts (semantic problem) and secondly, that different terms may be used to denote the same entity (syntax problem) [1].

A standardized terminology needs to be semantically consistent across organization boundaries, since the communication aspects of information require that communicating parties have the same understanding of the meaning of the exchanged information. This assumption is simple: if everyone adopts the same concepts, vocabulary, and language, any data expressed within this language will be accessible to everyone. For example, technical standards for product information and CAD/CAM documents have been realized by efforts like Product Data Management, Product Lifecycle Management and the Standard for the Exchange of Product Model Data—STEP [2].

However, establishing comprehensive and compatible standardized product data models can be a long and complicated process. According to Turk [3], the problems experienced in the development of standardized, large-scale product data models are due to the difficulties of getting the interested parties to agree on a common representation and also to the incompleteness of the models. It is infinitely more difficult to design a global standard. Kosanke and de Meer [4] also consider that there are too many overlapping groups developing international standards independently using incompatible and inconsistent terminologies. Furthermore, Stouffs and Krishnamurti [5] question whether standardization will improve the design process through effective data exchange, or whether it will hinder the process instead, by imposing a specific language for designers to express their ideas and conceptualisations? They believe that whilst a standard vocabulary will enable all participants to effectively communicate and exchange data within the context of this standard, it will not support flexibility and extensibility from outside their design domain.

In response to this problem, a well-defined manufacturing taxonomy and axioms are required that can be accepted by all participating engineers to make design knowledge effectively accessible across all the project team members without imposing an unnatural standard vocabulary on everyone. This means that sufficient cross-understanding of each other's terminology is essential. An approach for doing this, based on a manufacturing system engineering (MSE) ontology model has been proposed in [1], [6]. It has been designed to provide the explicit semantics of a common meta-model for a semantic and syntax interoperability service to enable cross-understanding of the basic manufacturing concepts, properties of concepts, relationships and constraints in concepts between different MSE applications.

There are many potential application areas for this approach since companies enter into temporary inter-enterprise collaborations for many types of business ventures and consequently many different types of information may need to be exchanged or shared. For example, details of products or components at different stages of design or manufacture, or details of available manufacturing facilities or resources, etc. An example based on one such application area, i.e., resource e-planning, is provided in Section 5 of this paper. It should be recognised though that although this example demonstrates the proposed scheme and ontology model approach in a particular context, the concepts presented here have a much wider set of application areas.

The issue of data structuring syntax for presentation and conceptualisation inevitably arises when considering ontology-based applications. On the syntactical level, standardization is an important research topic to integrate heterogenic information sources. In this paper, the MSE ontology model which is presented has adopted semantic web technology. This includes the Resource Description Framework (RDF), RDF schema [7] and Web Ontology Language (OWL) [8], which is the World Wide Web Consortium (W3C) standard semantic markup language for publishing, sharing and reuse of semantic data on the World Wide Web. In addition, the expressiveness of the OWL primitives in the manufacturing taxonomy and axioms provide the mediate service for enhancing information integration within an inter-enterprise community.

Section snippets

The semantic web for MSE digital information

The current web technology has provided platform independence for users to publish and access data anywhere and any time to support global network collaboration. It is probably the richest information repository in human history, but most of its digital information is unstructured and merely provides a human-readable web. Berners-Lee in his Semantic Web Roadmap document suggested “the Semantic Web approach instead develops languages for expressing information in a machine processable form” [9].

The semantic web syntactic standard: RDF, RDF schema and OWL

The W3C announced final approval of two key semantic web technologies in 10 February 2004, the revised RDF and OWL are semantic web standards.

RDF provides a simple data model and the RDF schema defines a simple ontology language with classes, sub-classes, properties, sub-properties, and domain and range restrictions in RDF for expressing metadata. However, the RDF Schema is not explicit (formal) enough and still does not provide exact semantics when it comes to representing complex constraints.

The MSE ontology model using OWL

This research and the MSE ontology model are motivated by the concepts of Moderators (to support both Product Design and Manufacturing System Engineering) that have been suggested and previously reported in [20], [21], [22], [23], [24]. A Moderator is an intelligent support application that is designed to facilitate and improve collaborative engineering design by enhancing the degree of awareness, cooperation, and coordination among engineering team members. To raise awareness between members

Example: mediated ontology to support information autonomy

When enterprises collaborate with each other, there is a need for mechanisms to support collaborative work for dynamic, geographically and organizationally dispersed project teams. Inter-enterprise operation, knowledge sharing and collaboration within a particular extended project group can typically be done by creating an agreed common understanding ontology model that is accepted by all participating engineers. Using this approach, an individual team member's documents within the group can be

Conclusion

The MSE Ontology Model is based on a comprehensive Semantic Web technology by making use of ontologies and Semantic Web standard language. Different engineering information terminologies are interpreted and connected to the corresponding terminologies through schema matching into the mediated ontology model. The paper addresses many of the inter-enterprise and inter-working issues related to the requirements of information semantic interoperability for knowledge sharing. The proposed MSE

Hsiao-Kang Lin received BA degree in International Trade & Finance from Fu Jen University, Taiwan and MSc degree in Business System Analysis and Design from the City University, London, UK, in 1987 and 1992, respectively. From 1993 to 2001, she worked in global OEMs sales and marketing in the PC sector and also in global manufacturing investment consultancy, working on behalf of both Taiwan companies and official government agencies in England. In 2005, she received a PhD degree from the

References (33)

  • J.A. Harding et al.

    Information-centred enterprise design supported by a factory data model and data warehousing

    Computers In Industry

    (1999)
  • K. Kosanke et al.

    CIMOSA: enterprise engineering and integration

    Computers In Industry

    (1999)
  • H.K. Lin et al.

    Manufacturing system engineering ontology for semantic interoperability across extended project teams

    International Journal of Production Research

    (2004)
  • ISO Standard, Industrial automation system and integration-Product Data Representation and Exchange: Part 1: Overview...
  • Z. Turk

    Limits of information technology in engineering: why computers might not replace the engineers

  • K. Kosanke et al.

    Consistent terminology—a problem in standardization/State of art report of enterprise engineering

  • R. Stouffs et al.

    Standardization: a critical review

  • H.K. Lin, Manufacturing System Engineering Ontology Model for Global Extended Projects Team, PhD Thesis, Wolfson School...
  • S. Powers

    Practical RDF

    (2003)
  • S. Bechhofer, F. van Harmelen, J. Hendler, I. Horrocks, D.L. McGuinness, P.F. Patel-Schneider, L.A. Stein, OWL Web...
  • T. Berners-Lee, Semantic Web Road Map, Available on-line as http://www.w3.org/DesignIssues/Semantic.html,...
  • E. Hyvonen et al.

    Application of ontology techniques to view-based semantic search and browsing

  • E. Mena et al.

    Ontology-Based Query Processing for Global Information Systems

    (2001)
  • D. Bourges-Waldegg et al.

    Combination of RSS newsfeeds and forms for driving web-based workflow

  • Y. Nakano et al.

    A proposal of RSS WebCrawler model of product information

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    Hsiao-Kang Lin received BA degree in International Trade & Finance from Fu Jen University, Taiwan and MSc degree in Business System Analysis and Design from the City University, London, UK, in 1987 and 1992, respectively. From 1993 to 2001, she worked in global OEMs sales and marketing in the PC sector and also in global manufacturing investment consultancy, working on behalf of both Taiwan companies and official government agencies in England. In 2005, she received a PhD degree from the Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, UK. Currently she is an Assistant Professor of the Department of Industrial Engineering & Management, I-Shou University, Taiwan. Her research interests include manufacturing system engineering moderator, ontology modelling, knowledge engineering and semantic web application for global enterprise integration.

    Jenny Harding is a Senior Lecturer in the Wolfson School of Mechanical and Manufacturing Engineering at Loughborough University. She has substantial industrial experience, having worked for over 15 years in the engineering and textile industries before joining Loughborough University in January 1992. Her expertise includes knowledge management and reuse, tools to support knowledge sharing within collaborative teams, knowledge discovery and data mining applications in manufacturing, and identification and structuring of ‘Best Practice’ information and knowledge. Her research projects have been funded in Europe and in the UK by EPSRC and Industry. She has a wide range of academic publications and has supervised several successful PhD projects.

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