ORIGINAL ARTICLE

Research on ontology-based integration of productknowledge for collaborative manufacturing

Yang Jiang & Gaoliang Peng & Wenjian Liu

Received: 25 July 2009 /Accepted: 20 November 2009 /Published online: 12 December 2009# Springer-Verlag London Limited 2009

Abstract One of the most important research subjectsgermane to knowledge management in collaborative man-ufacturing is the integration of heterogeneous productknowledge distributed among different collaborative enter-prises. To deal with the problem of optimization ofknowledge integration in collaborative manufacturing, anontology-based framework of knowledge integration ispresented to transform the problem of knowledge integra-tion into a problem of ontology integration. First of all, acollaborative business process for collaborative enterprises isdescribed, and then an ontology-based framework of knowl-edge integration is established. Under the condition ofanalyzing the structure of knowledge ontology, an ontologyschema of knowledge concept is discussed. Aiming at theontology schema, a method of ontology integration basedon ontology similarity is improved. The method is composedof ontology mapping and ontology merging, which is basedon calculated result of ontology similarity between localontology and global ontology. Finally, the implementation ofthe framework of knowledge integration is described, and theapplication results meet the requirement of the knowledgeintegration to ensure that validity of the ontology integrationmethod was proved.

Keywords Collaborative manufacturing . Knowledgeintegration . Ontology . Ontology integration .

Similarity matching

1 Introduction

Manufacturing enterprises are confronted with increasingimpact of competitive pressure resulting from the global-ization of market. These enterprises have to manage andchange with the growing complexity of manufacturinginformation and the increasing amount of knowledge.Manufacturing engineers working within a particularenterprise will inevitably develop their own vocabulary.Hence, two common types of problems would occur incommunications that share and exchange information. First,the same term is being applied to different concepts(semantic problem); second, different terms may be usedto denote the same entity (syntax problem) [1]. Thus, thenext generation of manufacturing calls for heterogeneousknowledge integration between collaborative enterprises[2].

Many different methods of knowledge integration havebeen developed for collaborative manufacturing. Forexample, Alisantoso et al. [3] proposed a novel purpose–behavior-structure (PBS) representation scheme to capturevital design knowledge for collaborative product develop-ment. Lee and Kim [4] proposed integrated framework forproduct development services to effectively integratedifferent knowledge bases and connect different engineer-ing application. Bombardier et al. [5] constructed asymbolic model for representing knowledge and a treestructure for differentiating between different knowledgelevels. Knowledge was then inferred using a fuzzy methodthat integrated different knowledge sources. Bless et al. [6]designed a generalized heuristic scheme to map therequired knowledge attributes set that allowed users to seekcheaper and superior knowledge sources from massiveknowledge bases, and then integrated the distributedknowledge sources. However, all these studies focused

Y. Jiang (*) :G. Peng :W. LiuSchool of Mechanics and Electronics,Harbin Institute of Technology,Harbin, Chinae-mail: jy2008ntu@yahoo.com.cn

Int J Adv Manuf Technol (2010) 49:1209–1221DOI 10.1007/s00170-009-2463-3

primarily on integrating non-semantic knowledge or non-conceptual knowledge, and neglected to integrate allsemantic or conceptual product knowledge in collaborativemanufacturing.

In response to this problem, some approaches based onontology [7–10], which defines an entity, attribute andrelationship among knowledge concepts within a specificdomain using explicit descriptions and specifications thatpresent an interoperable format which both humans andmachines can understand, were investigated for represent-ing semantics of information.

However, an integrated knowledge base does not meanthe merging of existing knowledge in collaborative manu-facturing. In addition, a local domain ontology justrepresents knowledge of a local repository, and it cannotrepresent integrated knowledge from many heterogeneousknowledge sources. Therefore, based on the study ofcollaborative manufacturing characteristics and practicalrequirements, authors present an ontology-based frameworkof knowledge integration to construct global domainontology for optimizing the result of knowledge integrationand allow all cooperative workers to share knowledge incollaborative manufacturing, and thus increase productdevelopment capability, and reduce product developmentcycle time and cost.

The paper is organized as follows: a review of relatedwork of the existing algorithms to represent and constructdomain ontology is presented in Section 2. Section 3introduces the business process for enterprise collaborationand presents an ontology-based framework of knowledgeintegration in collaborative manufacturing. An ontology ofknowledge and schema is designed in Section 4, whichdescribes the structure of the product knowledge. Basedon the ontology of knowledge, a method of ontologyintegration is studied in this section. An implementationbased on the framework is introduced in Section 5.Conclusions and future work of this study are providedin Section 6.

2 Related work

During the past few years, in order to construct thecommon semantic of the domain knowledge, manyontology-based ideas have been proposed to solve theproblem of knowledge integration between distributedenterprises. Ontology can provide standards for knowledgesharing, interoperation and integration among enterprisesby specifying concepts and relations between them. Theapplication of ontology consists of three main problems: (1)representation of ontology; (2) construction of ontology;and (3) ontology mapping. The representation of ontologyaims to provide standard format for knowledge integration

and sharing. The construction of ontology is to specifyconcepts and relations between knowledge. The ontologymapping researches that map between users, keyword/localterms and global terms (concepts) are stored in an ontologyrepository. The ontology updating is used to update theontology repository.

(1) For representation of ontology, some logic languagesand graph-based methods were presented. These logiclanguages, such as DAML+OIL [11] and OWL [12] etc.,are widely used to represent ontology because of theirpowerful reasoning functionality. Graph-based representa-tions [13] and object-oriented representations [14] are twomain methods of representation of ontology.

Graph-based representations can provide a distinct viewof ontology for users. The most famous applications ofgraph-based ontology representations are ConceptualGraphs [15] and WordNet [16]. The Sowa’s conceptualgraph defines two basic elements: box and circle. Theboxes are called concepts, and the circles are calledconceptual relations. Every concept has a type “t” and areferent “r”. It maps circles to predicate with each arc asone argument, and it maps concept nodes to typedvariables, where the type Label inside each concept boxdesignates the type. There are no arcs that link conceptsto concepts or relations to relations. WordNet is alinguistics ontology developed by Princeton University.The WordNet database contains 120,000 lemma orga-nized in 170,000 synonym sets. Each synonym setconsists of a list of synonymous words or collocations,and pointers that describe the relations between one setand other sets. Two kinds of relations are represented bypointers: lexical and semantic. Lexical relations holdbetween word forms; semantic relations hold betweenword meanings.

The advantages of concept graphs are clarity andpowerful reasoning ability. However, this method is notsuitable for representing attributes and features of concepts.Therefore, many researchers utilize the object-orientedrepresentations of ontology to represent attributes andfeatures of concepts. For example, Kyoung-Yun et al. [17]presents a new paradigm of ontology-based assemblydesign. In the paradigm, assembly design ontology servesas a formal, explicit specification of assembly design so thatit makes assembly knowledge both machine-interpretableand to be shared.

(2) For construction of ontology, there have been severalmain methods: the heuristic method, classification-basedmethod, and clustering-based method.

Approaches of the heuristic method capture domainconcepts and their relations by experiences of knowledgeengineers. For instance, Fortuna et al. [18] presents anOntoGen system for semi-automatic topic ontology con-struction. The OntoGen system offers support to the user

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during the construction process by suggesting topics andanalyzing them in real time. Lee et al. [19] presents a novelepisode-based ontology construction mechanism to extractdomain ontology from unstructured text documents. Themechanism consists of four steps: (1) defining the purposeand domain of the ontology; (2) constructing the ontologyby capturing concepts and relations in the domain, encod-ing them, and integrating all concepts and relations; (3)evaluating the ontology; and (4) documentation. Well-known, the domain conceptualization is critical to ontologyconstruction. In practice, capturing domain concepts andtheir relations is a bottleneck for constructing ontology. Themain reason is that knowledge engineers usually holddifferent views for the same concept. In addition, differentconcepts coming from heterogeneous databases usuallyhave the semantic.

With the development of machine learning, it is possibleto capture concepts and their relations automatically.Classification-based method and clustering-based methodare applied to construct ontology efficiently. Theclassification-based method is an incremental way toconstruct ontology. For example, Roche [20] presents twonew concepts which are defined by an existing concept byadding two opposite “differences”. Thus, all conceptsconstitute a binary tree in a top-down way. Theclustering-based method makes it possible that the conceptscan be extracted from structured or semi-structured datasources such as databases or XML documents [21]. Atpresent, term identification can be accomplished by naturallanguage processing (NLP) tools. For instance, Cruz andXiao [22] present an ontology-driven data integrationmodel which is used to parse documents in heterogeneousnetworks to extract a list of syntactically plausibleterminological candidates.

(3) For ontologymapping, there is no general methodologyat present. However, researchers have summarized severalfeasible methods, such as build-time approach, run-timeapproach, and Bayesian network-based (BN) approach.

The build-time approaches utilize ontological relation-ships which were defined in domain ontology. For instance,Ding and Foo [23] summarize the evolvement of ontologymapping, which calculates the similarity between ontolo-gies to match concepts and semantic representation ofexchanged information. Dey et al. [24] found super-concepts, sub-concepts, and synonymous concepts of user’skeyword from WordNet as well as domain ontology. Sun etal. [25] proposes a solution for interconnecting tacit andexplicit knowledge resources, considering the samedomain ontology set for modeling the knowledge itemsannotations, the user competency profile, and the taskcontext.

The run-time approaches infer semantically relevant con-cepts from the mapping between a run-time description/local

ontology and global ontology. For example, Vallet et al.[26] proposes an approach to provide a keyword with itssuper-concepts and sub-concepts for searching, which arecompared with domain ontology. Kim et al. [27] proposesa method for searching product information from differ-ent shopping malls. It interacts with users to specifyrelevant product categories and properties. In their otherwork [28], they try to enhance the expressive power inrepresentation of customer’s search intent and to allowcustomers to search product information basing on theirown context.

Bayesian network-based (BN) approach is an active wayin the ontology mapping at present. For example, Pan et al.[29] tries to use the iterative proportional fitting procedureapproach that helps experts to generate the required CPTin a consistent way. Tang et al. [30] uses Bayesiandecision method to optimize ontology-mapping result.This method conducts a matching process to conceptnames only, not considering the synonym and otherattribute information in ontology. Kim et al. [31] hasproposed a conversational agent that infers the intentionsof the user basing on BN and their semantic information.Jung et al. [32] proposes an ontology-mapping-basedsearch methodology (OntSE). Its objective is to find theterms which have the same semantics with user’s keywords,basing on multidimensional similarity and Bayesiannetwork.

To deal with the problems that optimal knowledgeintegration in collaborative manufacturing, in this study anontology-based framework of knowledge integration ispresented for transforming the problem of knowledgeintegration into domain ontology integration in collabo-rative manufacturing. Based on the optimal algorithm ofontology mapping between the local ontology and globalontology, the approach achieves the integration of do-main ontology and gets high performance of knowledgeintegration.

3 Framework of knowledge integration for collaborativemanufacturing

In this section, a business process is designed to describebusiness processes and exchanged information betweendistributed enterprises. Subsequently, an ontology-basedframework of knowledge integration is presented to supportthe business process in networked collaborative manufac-turing environment.

3.1 Collaborative manufacturing business process

In this subsection, a collaborative manufacturing businessprocess between enterprises is described, as shown in

Int J Adv Manuf Technol (2010) 49:1209–1221 1211

Fig. 1. The collaborative manufacturing business processhas three main phases: product design, process planningdesign, and product manufacturing. Each phase andactivity involves different tacit and explicit productknowledge distributed among different collaborative enter-prises, e.g., the enterprise of product design is in the USA,while enterprises of process planning design are in theEngland and Germany, at the same time, manufacturingenterprises are in China, Vietnam, and Brazil. All of theseenterprises have to exchange various product knowledgeand information:

1. Exchanged information regarding product configura-tions and manufacturability evaluation result betweencollaborative enterprises that product design and pro-cess planning design.

2. Exchanged information regarding process plan betweenprocess planning design and manufacturing enterprise.Details of the process planning include process routeand machine, setup route and fixtures, cutting tools andcutting parameters, and cutting tools and cuttingparameters.

3.2 Ontology-based framework of knowledge integrationfor collaborative business

Aiming at the problem of optimization of integrating theproduct knowledge between distributed collaborative enter-prises, an ontology-based framework of knowledge inte-gration is presented in this subsection. Because a domainontology can comprehensively represent knowledge of onearea, an integrated ontology can represent all knowledge ofvarious repositories. Through searching based on anintegrated ontology, a user will get more comprehensiveand accurate knowledge.

By taking advantage of this idea, the ontology-basedframework of knowledge integration is used to optimallyintegrate product knowledge of various enterprises andeventually satisfy user knowledge demands. All of the core

elements of the integration framework are shown in theFig. 2. The framework includes seven fundamental ele-ments {DE, LO, OI, GO, OS, U, and KP}.

1. DE—domain enterprise. A dominant enterprise estab-lishes a required knowledge and transforms it into aproduct knowledge ontology as a local ontology.

2. LO—local ontology. A collaborative enterprise estab-lishes a sharable local ontology based on local ontologyschema built by the dominant enterprise, this estab-lished local ontology is delivered to the process forknowledge integration. Then, one or more distributedlocal ontologies are then integrated into activity nodesof the product knowledge ontology as a globalontology.

3. GO—global ontology. By using the knowledgeintegration mechanism, these distributed local ontol-ogies are integrated into the product knowledgeontology established by the dominant enterprise,thereby creating product knowledge ontology withaddress connecting physical knowledge, which is aglobal ontology. Thus, collaborating enterprises canshare their own product knowledge with otherknowledge workers in collaborating enterprises toincrease knowledge value.

4. OI—ontology integration. In the process of the inte-gration the integration method is provided by a publicintegration server on the internet. The integrationmethod consists of two steps: ontology mapping andontology merging. The first step is divided intosimilarity computation, sorting, filtering, and linkingof the ontology schema. The concept names ofmerging, fundamental information merging, relationmerging and vertical/horizontal concept merging com-pose the second step: ontology-merging process. All oflocal ontology is integrated and merged into globalontology by the integration server.

5. OS—ontology searching. In the searching process, theuser can query integrated knowledge according to his/her demands. The primary steps in knowledge retrieval

Fig. 1 The collaborative business process

1212 Int J Adv Manuf Technol (2010) 49:1209–1221

are as follows: query edit, concept indexing, andknowledge output. Knowledge output identifies themost suitable knowledge via similarity computationsfor all searched knowledge. All of the knowledge isstored in the knowledge repository of design ormanufacturing enterprise.

6. U—it is the subject in the collaborative manufacturingenvironment to denote the user or user group, webservice, application or agent in a subsystem.

7. KP—knowledge repository stores the physicalknowledge that is established by distributed domainenterprise.

4 An ontology of product knowledge and a methodof ontology integration

To realize the ontology-based framework of knowledgeintegration, the ontology of product knowledge and anontology schema are designed, and then a method ofontology integration based on ontology similarity isdeveloped in this section.

4.1 Ontology of product knowledge

In the subsection, an ontology of product knowledge isestablished to describe concept, attribute, instance and

relationship among knowledge concepts within the collab-orative manufacturing.

The ontology of product knowledge is presented asshown in Fig. 3, which is composed of product design,process planning, and manufacturing knowledge. The sub-concepts of these stages are identified as follows:

& Sub-concepts from product design knowledge arecombined with requirement analysis, conceptual design,preliminary design, and detail design.

& Sub-concepts from product process knowledge aremanufacturability evaluation, process route and processresource selection.

& Sub-concepts from manufacturing knowledge includeequipment layout, production management, workshopschedule, and quality control.

The attribute describes the essential information of aconcept, e.g., ID, name, and content. Some examples ofattributes are shown on the right side of Fig. 3 and,respectively, describe detailed design, process route, andworkshop schedule.

4.2 Ontology schema

Based on previous theories about ontology, an ontologyschema is designed to be object of the method of ontologyintegration in this subsection. According to categories ofthe ontology proposed by Mizoguchi [33] and the identified

Fig. 2 Ontology-based framework of knowledge integration

Int J Adv Manuf Technol (2010) 49:1209–1221 1213

of ontology elements by Gruber’s [34], an ontology schemaof product knowledge is designed as shown in Fig. 4. Thisontology schema is used by individual enterprises to buildtheir own domain ontology with address of the physicalknowledge. The schema is described as follows:

& Concept name: describing an explicit or tacit knowl-edge concept name.

& Slot: some connatural attributes of a certain concept toensure that it is easy to understand and specify.Resources in knowledge ontology are related to eachother by inheritance and attributes.

& Relationship: the relation between concepts thatincludes “part_of”, “sub_class”, and “equivalence”.

& Synonym: describing the same semantic using differentconcept terms.

& Essential information: presenting information related toa concept, including function, input, output, constraint,and resource. “Function” specifies the actions that aconcept can perform, whereas a “constraint” indicatesthe basic theory or policy for executing a certain task.“Resource” is the tool or method for executing a certainactivity or task. “Input” and “output” are the materialsrequired and the results of a certain activity or taskexecution.

& Formal knowledge: recording the linking address in-depth knowledge that contains detailed descriptivedocuments for, or examples of, a certain concept.

[ [

[ [[ [

[ [

[ [

[ [[ [

[ [

[ [[ [

[ [

Fig. 3 The ontology of productknowledge

Fig. 4 Ontology schema

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4.3 The method of ontology integration

In order to achieve ontology integration, a method ofontology integration based on the similarity matching ispresented in this section. The method is composed of twophases: ontology mapping and ontology merging as shownin Fig. 5.

Individual enterprises offer their local ontologies, whichare then combined with dominant enterprises relative globalontology, to generate an integrated global ontology. Thisintegrated global ontology provides more comprehensiveconcepts and knowledge connections, to effectively inte-grate an individual enterprise’s knowledge objects andeventually achieve distributed product knowledge connec-tions and integration.

4.3.1 Ontology mapping

Generally, ontology mapping conducts a matching processto concept names only. Matching results are “matched

name”, “partially matched name”, and “unmatched name”.However, matched names will not necessary be matched inconcepts and unmatched names will not necessarily beunmatched in concepts. Consequently, ontology mapping ofthis study conducts similarity matching for concept names andconsiders the similarities of essential information and rela-tionship to precisely identify the similarity between concepts.

In the ontology-mapping process, the similarity calcula-tion is divided into three parts: concept name, essentialinformation, and relationships.

1. Similarity calculation of concept name

sC reflects the synonym sets between two concept term sets,its calculation value is defined as:

SC CAi ;C

Bj

� �¼

CACi \ CB

Cj

��� ���CACi [ CB

Cj

��� ��� ¼s

iþ j� sð1Þ

where CACi is the concept term set from ontology A; CB

Ci isthe concept term set from ontology B; i is the number of

Fig. 5 The method process ofontology integration

Int J Adv Manuf Technol (2010) 49:1209–1221 1215

CACi words; j is the number of CB

Ci words; s is the number ofsynonym sets between CA

Ci and CBCi.

2. Similarity calculation of essential information

Based on the ontology schema of product knowledge, theessential information similarity is divided into five catego-ries: function, input, output, constraints, and resourcesimilarity. sf is used to express the function informationsimilarity, its calculation value is defined as presented inEq. 2.These calculation methods of similarity are differentin the Eqs. 1 and 2, because the volume of terms which iscontained by the concept is less. It is used to calculate thenumber of synonyms to compare methods between termswhen programming; but the volume of terms which iscontained by the essential information is many, and thecomparing process will be very large to compute, so we usethe Eq. 2 to calculate the similarity of essential information.

Sf CAi ;C

Bj

� �¼

CAEi \ CB

Ej

��� ���CAEi [ CB

Ej

��� ���

¼

Pni¼1

xi �Pmj¼1

yi

Pni¼1

x2i þPmj¼1

y2i �Pni¼1

xi �Pmj¼1

yi

ð2Þ

where CAEi and CB

Ej are term sets of essential information. xiand yj are numbers of descriptive term words from thefunction of concepts. Similarly, descriptive term words inthe order of input, output, constraint, and resource from theconcept are also compared and calculated as si, so, sc, sr.After obtaining all essential information similarities, thesesimilarities are summed to determine an average similarityvalue for essential information SE as Eq. 3.

SE CAi ;C

Bj

� �¼ 1

5Sf þ Si þ So þ Sc þ Sr� � ð3Þ

3. Similarity calculation of relationships

Equation 4 is applied for relationship similarity matching:

SR CAi ;C

Bj

� �¼ 1

3SCp þ SCs þ SCe� �

¼ 1

3

CACpi \ CB

Cpj

��� ���CACpi [ CB

Cpj

��� ��� þCACsi \ CB

Csj

��� ���CACsi [ CB

Csj

��� ��� þCACei \ CB

Cej

��� ���CACei [ CB

Cej

��� ���0B@

1CA

ð4Þwhere SCp, SCs, SCe are the synonym sets for “part_of”,“sub_class” and “equivalence” concepts between twoconcept term sets respectively, CA

Cpi and CBCpj are the term

sets of the “part_of” concept for concept A and concept B,CACsi and CB

Csj are the term sets of the “sub_class” conceptfor concept A and concept B, respectively, CA

Cei and CBCej are

the term sets for “equivalence” relationship. The calculationprocess of the Eq. 4 is ordered by the Eq. 1.

4.3.2 The similarity and weight coefficients

Finally, Eq. 5 provides the similarity between ontology Aand ontology B via Eqs. 1, 3, and 4 and is described as:

S CAi ;C

Bj

� �¼ aSC þ bSE þ gSR ð5Þ

Where a, b, g are the weighted factors for similarity, a, b,g2(0, 1). Weight coefficients of the similarity could becalculated according to following steps:

Step 1. Using the scale of attribute importance presentedby Saaty [35], the third-order decision matrix isconstructed as follows:

CD CI CN

A3 !CD

CI

CN

1 2 31=2 1 21=3 1=2 1

24

35

Step 2. Calculation of the weight factor w as Eq. 6:

wi ¼ w*i =X3i

w*i ; i ¼ 1; 2; 3 ð6Þ

where w*i ¼ffiffiffiffiffiffiffiffiffiffiffiQ3j¼1

aij3

s; i ¼ 1; 2; 3�

Step 3. Calculation of the Maximum eigenvalue lmax asEq. 7:

lmax ¼X3i¼j¼1

wi � Sj ð7Þ

where Sj ¼P3i¼1

aij

Step 4. Because the indicator of each element is theestimated value in the decision matrix, theconsistency test is necessary for the correctnessof weight coefficients. The principle of consis-tency test is: if lmax < l'max, the result ofcalculation is correct; otherwise it needs adjust-ment of the value in the decision matrix, until theabove conditions are met. Wherein l'max is thecritical eigenvalue, the value of third-order criticaleigenvalue is 3.116. Calculated by the third-ordermaximum eigenvalue lmax ¼ 3:001 < 3:116,weight coefficients of the similarity could becalculated as Eq. 6:

a ¼ w1 ¼ 0:542 b ¼ w2 ¼ 0:298 l ¼ w3 ¼ 0:161

These weight coefficients with the above method couldbe the initial value of environmental variables. In the

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practical application, the value of weight coefficient couldbe adjusted appropriately. As mentioned above, the range ofthe similarity is (0, 1).

4.3.3 Ontology merging

To deal with the problem of repeated ontologies, ontology-merging process is described when we merge concept contentand relationships based on ontology-mapping results.

Step 1. Concept content reconstruction: Concept contentreconstruction must first consider whether localontology maps with global ontology. If yes,concept content reconstruction and relationshipreconstruction are conducted basing on mappingresults, as follows:

1. Suppose there is any the same concept in the globalontology:

a. Merging concept content when two or moreconcept names or synonyms are the same.

b. Merging the concept name into the field ofsynonyms and their concept content when two ormore concepts have the same content.

2. Suppose no same concepts exist in the global ontology:create a new concept to global ontology that includesconcept names and contents.

Step 2. Relationship reconstruction:1. Hierarchical relationship merging: increasing father

concept relationships in the global ontology.2. Process relationship merging: increasing brother con-

cept relationships in the global ontology.

5 Case study with the implemented software

Based on the proposed ontology-based integration techni-ques for product knowledge in section 4, we explain theimplementation model of proposed framework and analyzeits performance in this section.

Fig. 6 Implementation modelof prototype system

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5.1 Implementation model

In this subsection, an implementation model of theprototype system which has been developed according tothe framework we presented is described. The implemen-tation model is divided into five parts as shown in Fig. 6:information layer, integration layer, business layer, accesscontrol layer, and implementation layer.

& Information layer provides places to storage of variousdata and knowledge and some functions to access tothis information. The information includes knowledge

base, resource base, transport protocols (e.g. FTP, UDP)and access Java Bean to various data format.

& Integration layer provides a mechanism of informa-tion integration to shield heterogeneous of distributeddata. This layer consists of two parts: ontology-basedand web services-based integration. The ontology-based integration supports the semantic affinity ofdistributed data through ontology establishment, on-tology mapping and merging, ontology reasoning andontology management to achieve lowered redundancyand loose-couple in the collaborative manufacturingplatform. The web services-based integration supports

Fig. 7 A case of collaborativemanufacturing

Fig. 8 An example of import-ing new term

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the encapsulation and the management of businessservice to achieve establishment of business functionthrough the registration, discovery, and bonding ofweb services.

& Business layer provides a uniform mechanism of datamanagement, business management, and process man-agement for distributed collaborative partners. Datamanagement includes repository management, files

The global ontology of collaborative manufacturing after integrating four

local ontologies

The local ontology of product concept design knowledge

The local ontology of product detail design knowledge The local ontology of manufacturing knowledge

The local ontology of Product process knowledge

Fig. 9 Interfaces of ontology integration

Fig. 10 An example of knowl-edge indexing

Int J Adv Manuf Technol (2010) 49:1209–1221 1219

management, and general information management. Busi-ness management consists of ontology indexing, knowl-edge acquisition, and version control. Process managementincludes process modeling, implementation, control, andanalysis. All of the blocks are encapsulated by Java Bean.

& Information security is a necessary factor to consider ina collaborative environment. Access control layer isused to guarantee the confidentiality and the integralityof the collaborative environment through ID authenti-cation, role authentication, permission management,and access evaluation.

& Implementation layer provides a uniform collaborativeenvironment of the software for partners by means ofthe networked technique and the digital modeling,scheduling, and analysis to achieve indexing andsharing of technological information.

This study implements the prototype using Java in thefollowing environment: computer hardware—Intel CeleronCPU 2.40 GHz; and software—MS Windows XP, MS SQLServer 2005, JBuilder 8.0, JDK 5.0, Micromedia Dream-weaver MX 6.0, and Protégé 3.3.1 version.

5.2 Implementation case

To specify the function of prototype system, a collaborativemanufacturing of a grinding spindle was described as anexample in Fig. 7. All manufacturing processes consist ofstructural design, process design, engineering simulation,and manufacturing. Concerned engineering knowledgeincludes product structure, manufacturing resource, processrules, and tasks.

Figure 8 shows that the knowledge expert adds a newterm set of process model to local domain terms, and thenthe knowledge expert can edit the essential information andrelationship of terms by automatic tools to achieve buddinglocal ontology. Figure 9 displays the interface of ontologyintegration, which is consisted of integrated global ontologyconcepts and four local ontologies. All of the ontologyindicates the knowledge contents created in Protégé 3.3.1.The integrated global ontology is originally supplied byhegemonic enterprise in collaborative manufacturing, includ-ing the concepts of product design, product process planning,and manufacturing scheduling knowledge. Figure 10 presentsthe user interfaces of knowledge searching result by indexing“process route”.

6 Conclusion and future work

Aiming at the problems such as difficulties in optimizationof knowledge integration, an ontology-based framework ofknowledge integration is presented. The framework is

discussed under the collaborative business process innetworked collaborative manufacturing environment. Byway of introducing the structure of knowledge ontology, theontology schema is designed. Targeting at the ontologyschema, the method of ontology integration based onsimilarity is studied. To complete the similarity matchingin the mapping process, similarity calculation is dividedinto three categories: concept name similarity, essentialinformation similarity, and relationships similarity.

As an implementation, the integrating ontology andsearching results of the required knowledge indicates thatthe method of ontology integration is effective. Theontology-based framework of knowledge integration pro-vides comprehensive concepts and knowledge connectionsto effectively integrate an individual enterprise’s knowledgeintegration, increasing reuse ratio of product knowledgeand reducing product development cost and cycle time.

At present, this proposed framework and method have beenimplemented in collaborative manufacturing domain, and webelieve that our potential strategy can be applied in otherdomains. If only the constructed domain ontology is accurate,the integrated ontologywill be accurate, too. But the efficiencyof ontology mapping is not high enough in this study.Therefore, we will direct attention to the increasing efficiencyof the computing of methodology in our future work.

Acknowledgments The support of National Natural Science Foun-dation of China (No. 50905047) and the Commission of ScienceTechnology and Industry for National Defense Research Program,China (No. 20060521) in carrying out this research is gratefullyacknowledged. Sincere appreciation is extended to the reviewers ofthis paper for their helpful comments.

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