Elsevier

Computers in Industry

Volume 65, Issue 1, January 2014, Pages 108-115
Computers in Industry

Towards a Machine of a Process (MOP) ontology to facilitate e-commerce of industrial machinery

https://doi.org/10.1016/j.compind.2013.07.012Get rights and content

Highlights

  • Most of the ontologies developed for manufacturing, appeared as the result of intuitive or non methodological processes.

  • Current ontological methodologies proposed to date emphasize in development from scratch.

  • Reusing was stressed in our case, thus previous ontologies were considered, beside of the current discarding approach.

  • With the proposed methodology we fill the gap between intuitive and methodological development.

  • A large set of data about machines was analyzed by means of text crawling tools.

Abstract

Adapting to user's requirements is a key factor for enterprise success. Despite the existence of several approaches that point in this direction, simplifying integration and interoperability among users, suppliers and the enterprise during product lifecycle, is still an open issue. Ontologies have been used in some manufacturing applications and they promise to be a valid approach to model manufacturing resources of enterprises (e.g. machinery and raw material). Nevertheless, in this domain, most of the ontologies have been developed following methodologies based on development from scratch, thus ontologies previously developed have been discarded. Such ontological methodologies tend to hold the interoperability issues in some level. In this paper, a method that integrates ontology reuse with ontology validation and learning is presented. An upper (top-level) ontology for manufacturing was used as a reference to evaluate and to improve specific domain ontology. The evaluation procedure was based on the systemic methodology for ontology learning (SMOL). As a result of the application of SMOL, an ontology entitled Machine of a Process (MOP) was developed. The terminology included in MOP was validated by means of a text mining procedure called Term Frequency–Inverse Document Frequency (TF–IDF) which was carried out on documents from the domain in this study. Competency questions were performed on preexisting domain ontologies and MOP, proving that this new ontology has a performance better than the domain ontologies used as seed.

Introduction

The development of new products1 is a challenging activity that demands highly flexible and adaptable enterprises. Approaches, such as flexible manufacturing systems (FMS) [1], concurrent engineering (CE) [2] and design for manufacturing (DfM) [3] among others, aim to contribute to this challenge. Nevertheless, these approaches are centered in previously existing resources; that means they only consider available resources in a formerly given facility, and they discard the existence of newer resources which could give a better performance for a given production process. Thus, when new products are developed, the decision makers have fewer possibilities to have updated information about the real worldwide available resources for manufacturing. The situation described above becomes error-prone, given that the evaluation of a new product could conclude that an innovative product cannot be manufactured due to the lack of resources. When innovation is a key factor for success in the modern industry [4], this kind of decisions can lead to loss of business opportunities.

The Internet can be used as an information source of digital models of resources for manufacturing; e.g. industrial machinery, spare parts and raw materials. However, these resources require a different treatment from other resources commonly sold on Internet like clothes or other goods for personal use, for which a technical evaluation is unnecessary. Resources for manufacturing are designed for specific tasks and require skilled engineers and planners to decide about their acquisition and use. Thus, selecting such resources implies team work. Additionally, acquiring resources for manufacturing means disbursing considerable amounts of money, if we compare their costs with the cost of other products currently sold on the Web. Moreover, as we will demonstrate in Section 4, resources for manufacturing are becoming abundant on the Internet for sale. So, engineers and planners may require considerable amounts of time to decide among hundreds of similar resources. In fact, without specialized software tools for analyzing such information, taking an efficient decision becomes technically impossible [5]. An immediate consequence of this is the increase of cost to design and to develop new products.

In this vein, ontologies and the Semantic Web are valid approaches to describe resources on the Web [6]. In the domain of manufacturing, ontologies have been used in several use cases [7], [8], but little work has been conducted to make a semantic representation of certain manufacturing resources such as machinery, raw materials, product designs, among others. Such semantic representation would simplify searching them on the Web, and to integrate their model in a virtual environment or factory for reasoning about production processes constrains, in order to determine if any virtual resource should be integrated in a physical factory to get the target product done. We have selected industrial machinery as a resource to model, because this resource was recently referred in research related to ontology development for manufacturing [9].

Because the information about industrial machinery is in a human readable format (html, txt, pdf, among others), links and semantic connections between content and document are missing, thus the adaptation to a machine readable format is necessary. We use ontologies to bridge the gap between the technical document and its content. In this vein, three ontologies and a corpus of technical documents were considered in this study: (i) the Manufacturing's Semantic Ontology (MASON) [10], defined by its developers as an Upper Level Ontology (ULO) for manufacturing; (ii) the Machine-Tool Model (MTM) [9]; and (iii) the machine ontology (MO) [11]. A corpus of 633 documents was extracted from the Internet and processed by text mining analysis tools to get significant keywords. The aforementioned ontologies were matched to each other in order to obtain similarities among them. Based on these results, an ontology learning (OL) [12] process was carried out with MTM and MO. In a semi-automatically way, relevant concepts and relations were extracted from MTM and MO to form a new ontology. The result was Machine of a Process (MOP), an ontology that represents industrial machinery as resources on the Web, satisfying the user's requirements of knowledge for economic evaluation, engineering design and production control for a given production process. In Sub Section 4.2 we will demonstrate how to evaluate the fulfillment of these requirements by means of performing some competency questions to the aforementioned ontologies [6].

This paper has been structured as follows: we present related work classified in three blocks, product description and Semantic Web, ontologies for enterprises, and ontology learning in Section 2; the general methodology, its tools and methods are described in Section 3; while we discuss our results in Section 4; and some conclusions and future work are outlined in Section 5.

Section snippets

Product description and the Semantic Web

Semantic description of goods is a key factor for e-commerce. This is so, because nowadays, manufacturing of goods can take place almost anywhere at any time, but with different prices and levels of quality. This means that decision makers require computer-based systems to speed up the analysis of product data and take decisions. In this vein, ontology such as GoodRelations [6] illustrates the usability of product description on the Web to simplify e-commerce. Nevertheless, the scenarios in

Methodology

We considered the following assumptions before designing our experiments and selecting the corresponding methods, software tools and materials involved in our methodology.

  • Upper level ontologies facilitate the development of domain ontology [24].

  • Reusing existing ontologies can considerably accelerate the development of a new ontology [25].

  • Ontologies aim at modeling the fundamental concepts and relations in a specific domain of discourse [26]. That is, ontology pretends to model entities by means

Methodology strategy selection

In this section we will describe the results obtained from the application of our methodology mentioned above. We started with an evaluation of the complexity of the domain. This analysis of this domain and an evaluation of software tools are outlined in a detailed technical report [24]. As a consequence of such analysis, a combination of deductive and inductive OL strategy (middle out) was selected. In other words, top-down and bottom-up methodological strategies were considered. In this vein,

Conclusions

Integration of the product life cycle is a key factor for enterprise success. Ontologies and the Semantic Web are currently being used to develop systems for the manufacturing industry. Nevertheless, many of the approaches, related to manufacturing, make ontology from scratch without considering the possibility of reusing ontology, although there is previous work developed in this field.

In this work, we have shown how to bind semantic of a manufacturing domain by an upper level ontology and a

Luis Enrique Ramos García has a teaching position at the National Open University (UNA) of Venezuela. He received his Industrial Engineer degree in 2003 from this institution. In 2008 he received his Master Degree in System Engineering at the University Simón Bolívar (Venezuela). Since 2009 is studying his PhD in Ontological Engineering at the University of Bremen (FB 10) under the supervision of Prof. Dr. John Bateman. His research interests include ontology, the Semantic Web and knowledge

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    Luis Enrique Ramos García has a teaching position at the National Open University (UNA) of Venezuela. He received his Industrial Engineer degree in 2003 from this institution. In 2008 he received his Master Degree in System Engineering at the University Simón Bolívar (Venezuela). Since 2009 is studying his PhD in Ontological Engineering at the University of Bremen (FB 10) under the supervision of Prof. Dr. John Bateman. His research interests include ontology, the Semantic Web and knowledge representation and retrieval in the industrial domain. Last year he began to co-organize the workshop series OSEMA (Ontology and Semantic Web for Manufacturing).

    Richard Gil received his Computer Engineer Degree from the University Simón Bolívar (USB-Venezuela) in 1980. From 1980 to 2005 he worked as a system engineer and software development manager for the same University. His work there included the development of academic systems and other organizational system projects. Between 1982 and 1986 he attended graduate school at USB, receiving degrees in General Management Specialist (in 1986). Likewise, he earned a M.D. of Science in Organization and Management on Communication Technology at Capella University (Minnesota-USA in 2001) and other M.D. of Soft Computing and Intelligent Systems from the University of Granada (UGR-Spain in 2009). From 1986 to 2005 he was a faculty member (part-time) in the Department of Processes and Systems at USB. He teaches graduate and undergraduate classes in Information Systems, System Management, and System Information Development. Currently, he is Ph.D. student of Computer Science and Information Technology at the UGR, working on Ontology Learning methodologies and Machine Intelligence applications for producing qualified Knowledge Support Systems. He is respectively member of the research group in Knowledge Systems and Semantic Technology (GISICOTS-USB) and Intelligent Database and Information Systems (IDBIS-UGR). He is referee of a JCR Journal and he has served as a program committee member for some international conferences.

    Dimitra Anastasiou is a researcher in the departments of Languages and Literary Studies, and Computer Science University of Bremen. The previous two years she was post-doctoral researcher at the Localization Research Center within the Center for Next Generation Localization, where she was chairing the metadata group. She is interested in the Multilingual Semantic Web (MSW) and was involved as reviewer and author the MSW Workshop 2011. She was member of a Technical Committee for the standard XLIFF (XML Localization Interchange File Format) and organized the 1st XLIFF International Symposium. Being interested in interoperability between standards as well as multilingual ontologies, she created a mapping from XLIFF to RDF. In addition, she is a co-organizer of the 2nd OSEMA Workshop 2012.

    Dr. Maria J. Martin-Bautista is a Permanent Professor at the Department of Computer Science and Artificial Intelligence at the University of Granada, Spain, where she received her Ph.D. in Computer Science in 2000. She is a member of the IDBIS (Intelligent Data Bases and Information Systems) research group. Her current research interests include Semantic Web, Knowledge Representation and Ontologies, Text and Web Mining, Data Warehousing and Information Retrieval. She has supervised several Ph.D. Thesis and she has published more than 50 papers in journals and international conferences. She has participated in several international and national projects, as well as has supervised research and technology transfer projects with companies. She has served as a program committee member for several international conferences and she is referee of several JCR International Journals.

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