Knowledge integration and sharing for collaborative molding product design and process development

Computers in Industry 61 (2010) 659–675

Knowledge integration and sharing for collaborative molding product design andprocess development

Yuh-Jen Chen *

Department of Accounting and Information Systems, National Kaohsiung First University of Science and Technology, 2 Juoyue Road, Nantz District, Kaohsiung, Taiwan, ROC

A R T I C L E I N F O

Article history:

Received 7 December 2009

Received in revised form 30 January 2010

Accepted 19 March 2010

Available online 22 April 2010

Keywords:

Molding product design and development

Collaboration

Knowledge integration

Knowledge sharing

Ontology

A B S T R A C T

This study presents a systematic approach to developing a knowledge integration and sharing

mechanism for collaborative molding product design and process development. The proposed approach

includes the steps of (i) collaborative molding product design and process development process

modeling, (ii) an ontology-based knowledge model establishment, (iii) knowledge integration and

sharing system framework design, (iv) ontology-based knowledge integration and sharing methods

development, and (v) ontology-based knowledge integration and sharing mechanism implementation.

The mechanism can support collaborative molding product design and process development by

providing functions of knowledge integration and sharing. Results of this study facilitate the knowledge

integration and sharing of collaborative molding product design and process development to satisfy the

product knowledge demands of participants, and thus increase molding product development

capability, reduce molding product development cycle time and cost, and ultimately increase molding

product marketability.

� 2010 Elsevier B.V. All rights reserved.

Contents lists available at ScienceDirect

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1. Introduction

A growing demand to significantly reduce molding productdevelopment time and upgrade the development efficiency has ledto the extensive application of a collaborative design model inindustry from various phases of molding product design andprocess development [1,4,5,8,11], to upgrade the communicationand coordinating skills of all industries. However, molding productdesign and process development is relatively long process in whichvarious industries require a significant amount of pertinentinformation and knowledge expertise in different phases. Satisfy-ing the knowledge requirements of developers in each phase ofmolding product development involves integrating and sharingknowledge cross various industrial sectors by using informationtechnology.

As knowledge sharing heavily relies on knowledge integration,many knowledge integration and sharing methods [7,12,15,17]have been developed for collaborative product development inrecent years. For instance, Yang et al. [17] developed a knowledgerepresentation and knowledge sharing mechanism for assemblinga product structure under the collaborative product developmentmodel in order to resolve knowledge representation and hetero-geneous content related problems by sharing the knowledge of

* Tel.: +886 7 6011000x4316; fax: +886 7 6011158.

E-mail address: [email protected].

0166-3615/$ – see front matter � 2010 Elsevier B.V. All rights reserved.

doi:10.1016/j.compind.2010.03.013

product assembly, Han et al. [7] designed an ontology-basedrepresentation model and knowledge integration framework forball bearing structure to integrate related knowledge of all ballbearing parts effectively. By adopting ontology techniques torepresent knowledge in product design phase and design aninformation and knowledge management system frameworkbased on ontology-based knowledge representation, Venkatasu-bramanian et al. [15] attempted to facilitate the exchange ofproduct information and knowledge sharing among productdesigners and, in doing so, upgrade product design efficiency.Lee et al. [12] developed a product structure knowledge ontologymodel to commence with multiple product structure knowledgemodeling under a collaborative product development model tofacilitate knowledge integration and sharing.

While focusing mainly on knowledge integration and sharingfor a product structure, above studies did not consider how tointegrate and share product knowledge within the lifecycle ofcollaborative product design and process development. This lack ofconsideration makes it impossible not only to integrate and sharevaluable product knowledge effectively, but also to satisfy theproduct knowledge requirements of related developers in colla-borative molding product design and process development.

Therefore, this study develops a knowledge integration andsharing mechanism for collaborative molding product design andprocess development to effectively integrate and share relatedmolding product knowledge offered by industry from variousphases of the molding product design and process development.

mailto:[email protected]

http://www.sciencedirect.com/science/journal/01663615

http://dx.doi.org/10.1016/j.compind.2010.03.013

Fig. 1. Interactive matrix for molding product design and process development

activities.

Y.-J. Chen / Computers in Industry 61 (2010) 659–675660

The proposed mechanism also attempts to satisfy the knowledgerequirements of related developers. Specifically, this study has thefollowing objectives: (i) to model a collaborative molding productdesign and process development process, (ii) to establish anontology-based knowledge model, (iii) to design a knowledgeintegration and sharing system framework, (iv) to developontology-based knowledge integration and sharing methods,and (v) to implement an ontology-based knowledge integrationand sharing mechanism.

Results of this study significantly contribute to efforts tofacilitate knowledge integration and sharing of collaborativemolding product design and process development in order tosatisfy the knowledge requirements of developers in the colla-borative molding product design and process development, thusincreasing product development capabilities and reducing productdevelopment cycle time and costs.

2. Collaborative molding product design and processdevelopment process modeling

This section describes how to establish the process forconventional molding product design and process developmentby using the process modeling technique (IDEF0). Exactly howactivities from conventional molding product design and processdevelopment interact with each other is then analyzed. Finally, the

Fig. 2. Interactive matrix for molding product design and process development tasks.

Y.-J. Chen / Computers in Industry 61 (2010) 659–675 661

process for collaborative molding product design and processdevelopment is established based on this interactive relation.

2.1. Conventional molding product design and process development

The process of conventional molding product design andprocess development is modeled properly by devising anintegrated definition for function modeling (IDEF0) [9], i.e., oneof the IDEF family of methods, and using it as the basic construct forprocess analysis and modeling.

The conventional molding product design and processdevelopment were modeled based on IDEF0 functional model-ing. The process of conventional molding product developmentand manufacturing process includes six phases: moldingproduct design, molding process design, mold design, moldfabrication process planning, mold fabrication and moldingproduct manufacturing. Furthermore, each phase can beclassified into several activities. For instance, molding productdesign involves four tasks of preliminary design, moldabilityassessment, parting line specification, and detailed design forthe molding part.

Fig. 3. Portion of collaborative molding product

2.2. Collaborative molding product design and process development

Collaboration refers to informal, cooperative relationships thatbuild the shared vision and understanding needed for conceptualiz-ing cross-functional linkages in the context of knowledge intensiveactivities. Collaboration facilitate the acquisition and integration ofresources through external integration and cooperation with othercooperative or supporting enterprises, conducted on a basis ofcommon consensus, trust, cooperation, and sharing by a multi-functional team of experienced knowledge workers. Collaboration isthus essential in knowledge generation and transfer.

To efficiency and competitiveness of molding product designand process development are upgraded by first identifying how themolding product design and process development activities/tasksinteract with each other. Such an identification is made byanalyzing the input, output, constraints and knowledge of, fromand used in each activity/tasks, to pave the way for collaborativemolding product design and process development construction.Fig. 1 shows the interactive relations between activities of moldingproduct design and process development. These molding productdesign and process development related activities include molding

design and process development process.


product design, mold design, molding product process design,mold fabrication process planning, mold fabrication, and moldproduct manufacturing. Each development activity is furtherrefined into levels of tasks. Fig. 2 shows the interactive relationsbetween these tasks.

According to the analyzed interactions in Fig. 2, the ‘‘moldingproduct design’’, denoted as the shaded portion, is selected as arepresentative example for establishing the collaborative moldingproduct design and process development process, as illustrated inFig. 3.

3. Ontology-based knowledge model establishment

This section first identifies the knowledge involved in activitiesfrom each phase of the collaborative molding product design andprocess development. The ontology-based knowledge model basedon the identified knowledge is then implemented to pave the way forknowledge integration and sharing system framework development.

Fig. 4. Portion of knowledge involved in collaborative mold

3.1. Knowledge identification and classification

The concealed knowledge items within activities from everyphase of collaborative molding product design and processdevelopment are identified by using IDEF0 as a knowledgeidentification model. Fig. 4 shows a portion of the results.

Capable of classifying the identified knowledge systematically,this study also classifies knowledge into seven levels of ‘‘know-what’’, ‘‘know-why’’, ‘‘know-how’’, ‘‘know-when’’, ‘‘know-who’’,‘‘know-where’’ and ‘‘know-with’’. Each knowledge level is detailedas follows.

(1) Know-what: the definitions and contents from activities ofcollaborative molding product design and process development.

(2) Know-why: the executive motivation or intention fromdevelopmental activities in each phase of collaborativemolding product design and process development process,such as molding product functional requirements, customer

ing product design and process development process.

Fig. 5. Ontology-based knowledge model.

Fig. 6. Schema of knowledge concept (ontology).


Fig. 8. Web service-based knowledge integration and sharing system framework.

Fig. 7. Web service-based knowledge integration and sharing environment framework.


Fig. 9. Ontology-based knowledge integration and sharing process.

Fig. 10. Ontology integration conceptual model.


requirements, design specification, experience knowledge, andmoldability assessment data.

(3) Know-how: the executive steps for activity from the process ofmolding product design and process development (e.g., designSOP, cavity process plan, feed process plan, and cooling processplan) as well as the required product strategy knowledge (e.g.,preliminary product model, molding product specification,mold feature, design principle, mold workpiece and cavitiesmodel, material property, manufacturing capability, moldinganalysis data, variation analysis parameter, and cost plan).

(4) Know-when: the start and end time for each activity frommolding product design and process development process, thefixed time planning for performing each developmentalactivity (e.g., machine setting time evaluation), and thesequence planning for operating steps.

(5) Know-who: the domain expert (e.g., molding product designengineer and fabrication operator) and resource application(e.g., CAD\CAE and material database) in executing everyactivity from the process of molding product design andprocess development.

(6) Know-where: the knowledge sources in each activity ofmolding product design and process development.

(7) Know-with: related collaborative activities while executing theactivity of molding product design and process developmentprocess.

3.2. Ontology-based Knowledge modeling

Based on the results of Section 3.1, the knowledge is modeledusing the ontology techniques, as depicted in Fig. 5. Thisontology-based knowledge model consists of conceptual knowl-edge and physical knowledge. The former refers to the mainknowledge concepts in the molding product design and processdevelopment (e.g., molding process design knowledge, mold


fabrication process planning knowledge, and collaborationknowledge). Meanwhile, the latter describes physical knowledgecontents, which can be grouped into eight types: know-what,know-why, know-how, know-when, know-who, know-where,know-with, and communication knowledge. Each knowledgetype can be represented through XML/HTML formats in theknowledge repository.

Fig. 11. Similarity matching p

Ontology-based knowledge integration and sharing in thecollaborative molding product design and process developmentare facilitated by designing a knowledge concept (ontology)schema, as shown in Fig. 6. The schema is described as follows:

� Knowledge concept: expressing a tacit or explicit knowledgeconcept name.

rocess for concept name.

Fig. 12. Cosine of u is adopted as Sim(DGA, QLB).


� Concept source: recording the name of an enterprise offeringknowledge. Whenever an enterprise drops out of the integrationand sharing mechanism, the link between a concept node and itsphysical knowledge is removed.� Concept definition: describing a certain concept so that it is

easily understood and specific. A user can identify other relevantconcepts in this concept description.� Synonym: describing the same semantic using different concept

terms.� Physical knowledge: recording the linking address physical

knowledge that contains detailed descriptive documents—‘‘know-what’’, ‘‘know-why’’, ‘‘know-how’’, ‘‘know-when’’,‘‘know-who’’, ‘‘know-when’’, ‘‘know-with’’, and ‘‘communicationknowledge’’.� Relation: describing the relations between concepts using five

relations ‘‘is_part_of’’, ‘‘is_a’’, ‘‘request’’, ‘‘collaborative_with’’,and ‘‘sequence_to’’.

4. Web service-based knowledge integration and sharingframework design

A web service-based knowledge integration and sharingenvironment framework is proposed to support knowledgeintegration and sharing in the collaborative molding productdesign and process development. Based on the proposed environ-ment framework, a web service-based knowledge and sharingsystem framework is then designed.

4.1. Web service-based knowledge integration and sharing

environment framework design

To model the knowledge activities in the environment ofcollaborative molding product design and process development,this section describes the web service-based knowledge integra-tion and sharing environment framework to integrate moldingproduct development knowledge. This knowledge is provided byenterprises from various phases of collaborative molding productdesign and process development, with the intention of sharing thisintegrated molding product development knowledge with rele-vant developers to satisfy their knowledge requirements formolding product design and process development. According toFig. 7, the environment framework includes five portions: webservice-based knowledge registration, enterprise knowledge (localontology), knowledge integration, molding product design andprocess development knowledge (global ontology), and knowledgesharing. They are described as follows.

� Web service-based knowledge registration: Enterprises areoffered a registration channel of knowledge integration via aweb service method to facilitate knowledge integration andsharing.� Enterprise knowledge (local ontology): Collaborative enterprise

can establish its enterprise knowledge to integrate and shareknowledge based on the designed ontology schema.� Knowledge integration: Knowledge from collaborative enter-

prises is integrated into molding product design and processdevelopment knowledge by using the ontology method.� Molding product design and process development knowledge

(global ontology): Integrated knowledge of collaborative mold-ing product design and process development can be shared withrelevant developers from collaborative enterprises.� Knowledge sharing: Relevant developers from collaborative

enterprises at each phase of the collaborative molding productdesign and process development can acquire required relatedknowledge by searching for knowledge in the molding productdesign and process development to achieve their tasks.

4.2. Web service-based knowledge integration and sharing system

framework design

Based on the web service-based knowledge integration andsharing environment framework, two knowledge managementagents are designed in this section to support knowledgemanagement in collaborative molding product design and processdevelopment, as shown in Fig. 8. These two knowledge manage-ment agents are a global knowledge management agent and a localknowledge management agent, respectively. A global knowledgemanagement agent is responsible for managing the knowledgeduring molding product design and process development, includ-ing cooperative enterprises registration, global knowledge repo-sitory management, ontology integration and maintenance, andknowledge sharing. A personal knowledge management agent isresponsible for managing the knowledge of an individualenterprise, and also provides a connection to a global knowledgemanagement agent via the Internet or company Intranet forknowledge integration and sharing.

5. Ontology-based knowledge integration and sharingmethods development

To realize the knowledge integration and sharing systemframework for collaborative molding product design and processdevelopment, the process of ontology-based knowledge integra-tion and sharing for collaborative molding product design andprocess development is designed. Subsequently, methods involvedin the designed process are developed.

5.1. Ontology-based knowledge integration and sharing process

design

Based on the web service-based knowledge integration andsharing system framework in Section 4.2, this section presents theprocess of ontology-based knowledge integration and sharing toeffectively support knowledge integration and sharing in acollaborative mold product design and process developmentenvironment, as shown in Fig. 9.

The process includes two main parts of ‘‘ontology-basedknowledge integration’’ and ‘‘ontology-based knowledge sharing’’,as described below.

(1) Ontology-based knowledge integrationa. Ontology mapping: Concept mapping is proceeded with for

local ontologies established and provided by collaborativeenterprises to identify similar knowledge concepts. Steps inontology mapping are similarity comparison, similar con-cepts aggregation, and linking.

b. Ontology merging: After the linking of similar concepts iscompleted, ontologies are integrated using the ontologymerging function. Steps in ontology merging includeconcept name merging, concept content merging, non-similar concept and relation reconstruction.

(2) Ontology-based knowledge sharing.


Ontology-based knowledge sharing largely uses the globalontology of molding product design and process developmentformed by ontology-based knowledge integration to share knowl-edge among activities from any phase of molding product designand process development. Ontology-based knowledge sharing

Fig. 13. Similarity matching process

includes two searching methods for ontology concept: conceptselection and concept searching. The operational steps for conceptselection are concept expansion and knowledge selection, whilethe operational steps of concept searching are concept matchingand intent identification. Meanwhile, the intent identification can

for concept knowledge content.


identify the concept requested by a user and the locations of itsrelated concepts within the ontology for providing the mostcorrect concept to meet the intent of the user requestingknowledge.

5.2. Ontology-based knowledge integration method development

5.2.1. Conceptual model of ontology integration

Ontology integration includes two phases: ontology mappingand ontology merging. In the ontology integration, ontologymapping is used initially to identify similar knowledge conceptsbetween local ontology and global ontology, and then to become anewly integrated global ontology through ontology merging, asshown in Fig. 10.

5.2.2. Ontology mapping

Ontology mapping determines the similarity of two conceptsare similar based on their concept names, knowledge contents andrelations, as detailed below.

Step 1. Perform similarity matching for concept names. Thissimilarity of concept names between two concepts is calculatedusing the Jaccard coefficient [6,10]. The calculation is as shown inEq. (1).

Name SimðCiGA;C jBLÞ ¼

CinGA \C jnLB�� CinGA [C jnLBj j (1)

where CiGA is the activity concept in global ontology; CjLB is theactivity concept in local ontology; CinGA ¼ fCiGA

1 ;CiGA2 ; :::;CiGA

t g isthe term set of the i concept name in global ontology; C jnLB ¼fC jLB

1 ;C jLB2 ; :::; C jLB

t g is the term set of the j concept name in localontology; SinGA ¼ fSiGA

1 ; SiGA2 ; :::; SiGA

t g is the synonym term set ofthe i concept in global ontology; S jnLB ¼ fS jLB

1 ; S jLB2 ; :::; S jLB

t g is thesynonym term set of the j concept in local ontology.

In similarity matching for concept names, concept names are firstdeconstructed as term sets of unit words. The similarity for term setsof two different concept names is then determined. Fig. 11 shows thealgorithm for similarity matching of concept names.

Step 2. Perform similarity matching for the knowledge contentof the concept. Similarity matching for the knowledge content ofthe concept focuses on knowledge content matching of ‘‘know-what’’ and ‘‘know-why’’ included in a concept. Given that thesetwo knowledge contents are represented in the form of a textparagraph, these two knowledge contents of ‘‘know-what’’ and‘‘know-why’’ from each concept can be treated as one documentcontent. Similarity matching for document content [3,14,19] issubsequently applied to determine whether these two knowledgecontents from two different concepts are similar. This methodinvolves vector representation and similarity measurement for theknowledge content of a concept. They are explained as follows.

Fig. 14. Relation similarity be

5.2.2.1. Vector representation for knowledge content of a concept. -

Before the vector for knowledge content of a concept isrepresented, these text contents of ‘‘know-what’’ and ‘‘know-why’’ from the concept A in global ontology and the concept B inlocal ontology must be represented as DGA and QLB, respectively.Moreover, these two knowledge contents of ‘‘know-what’’ and‘‘know-why’’ from the two concepts are used to execute termsegmentation and sentence segmentation by using the HownetKnowledge Database [16,20] in order to get the results of termsegmentation and sentence segmentation from knowledge con-tents of concepts A and B (i.e., DGA ¼ ½CGA

1d ;CGA2d ; . . . ;CGA

id � andQLB ¼ ½CLB

1q ;CLB2q ; . . . ;CLB

iq �).According to the results of term and sentence segmentation

from the knowledge content of a concept, the term frequency foreach term in the knowledge content is estimated. The calculationscan derive the term frequencies of all terms in DGA. They areT f GA

D ¼ ½T f GAC1d; T f GA

C2d; . . . ; T f GACid �, as well as the term frequencies of

all terms in QLB, and they are T f LBQ ¼ ½T f LB

C1q; T f LBC2q; . . . ; T f LB

Ciq�.Moreover, the frequency value Tfi from each term and also themaximum term frequency value Tfi(max) among terms are obtainedto determine the weight value Wi for each term by using Eq. (2).

Subsequently, all of the terms (DGA ¼ ½CGA1d ;C

GA2d ; . . . ;CGA

id � andQLB ¼ ½CLB

1q ;CLB2q ; . . . ;CLB

iq �) in these two concepts and their weighvalues Wi are represented as vectors in an i-dimensional space byusing Eq. (3).

Wi ¼T f i

T f iðmaxÞ(2)

�!DGA

¼ ½WGA1d ; WGA

2d ; :::;WGAid � (3)

5.2.2.2. Similarity measurement for knowledge content of a con-

cept. According to the above vector representation for theknowledge content of a concept, the vector space model methodin document content similarity measurement (Eq. (4)) is adoptedto estimate the knowledge content similarity of ‘‘know-what’’ and‘‘know-why’’ included in two concepts from different ontologies.Fig. 12 shows the knowledge content vectors �!DGA

and �!QLB

included in the two concepts from different ontologies. Addition-ally, the cos u value is defined from the angle projected by thesetwo vectors as the knowledge content similarity of the twoconcepts. Fig. 13 shows the algorithm for similarity matching ofconcept knowledge content.

SimðDGA;QLBÞ¼ �!DGA� �!QLB

�!DGA

�� !QLB

�� ¼Pn

i¼1 WGAid�WLBiqffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPni¼1 WGAid2

q�

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPni¼1 WLBiq2

q

Step 3. Calculate the relation similarity. Fig. 14 shows theconcept relation between the two ontologies. When a similarity

tween different concepts.

Fig. 15. Similarity matching process for relation between concepts.



exists between parent and child nodes from the concept CiGA inglobal ontology A and parent and child nodes from the concept CjLB

in local ontology, then a similar relation can be found between theconcepts CiGA and CjLB.

Relation SimðCiGA;C jLBÞ ¼ 1=2

CiUxGA \C jUxLB��

CiUxGA [C jxLBj j

þ 1=2CiSxGA \C jSxLB��

CiSxGA [C jSLB��

��(5)

Eq. (5) is the formula for estimating relations similaritybetween concepts. Meanwhile, CiUxGA denotes the parent conceptset of the concept CiGA (i.e., fCiUGA

1 ;CiUGA2 ; :::; CiUGA

n g) in globalontology and C jUxLB represents the parent concept set of theconcept CjLB (i.e., fC jULB

1 ;C jULB2 ; :::;CiULB

n g) in local ontology, whileCiSxGA represents the child concept set of the concept CiGA (i.e.,fCiSGA

1 ;CiSGA2 ; :::;CiSGA

n g) in global ontology and C jSxLB refers to thechild concept set of the concept CjLB (i.e., fC jSLB

1 ;C jSLB2 ; :::;CiSLB

n g) inlocal ontology. Fig. 15 shows the algorithm for calculating therelation similarity.

Fig. 16. Concept knowledge merging process.

5.2.3. Ontology merging

Based on the above ontology mapping results, this sectionattempts to merge the similar concepts that match the similaritystandard in both global ontology and local ontology. Fig. 16 showsthe merging algorithm process. Ontology merging can be classifiedinto two modes: (1) between two similar concepts in the sameactivity and (2) between two similar concepts with differentactivities. The two ontology merging modes are described asfollows.

(1) Concept merging between two similar concepts in the sameactivity: Similar concepts from both global ontology and localontology are first taken. For a situation in which these twoconcepts belong to the concept knowledge from the sameactivity, related knowledge from these two similar concepts ismerged together. Moreover, the similarity of the child conceptsfrom these two similar concepts is determined. If they aresimilar, related knowledge from these similar child concepts ismerged together. Conversely, the child concepts from localontology are added to global ontology.

(2) Concept merging between two similar concepts with differentactivities: Similar concepts from both global ontology and localontology are first taken. For a situation in which these conceptsbelong to the concept knowledge from the different activities,relevant knowledge from these two similar concepts is mergedtogether. Moreover, the similarity of the child concepts fromthese two similar concepts with different activities isdetermined. If the child concepts are similar, related knowl-edge from these similar child concepts is merged together. Incontrast, the ontology merging process is closed.

5.3. Ontology-based knowledge sharing method development

According to the knowledge sharing portion of ontology-basedknowledge integration and sharing process in Fig. 9, thissubsection describes related schemes including concept matchingand intention identification.

Fig. 17. Concept matching process for knowledge sharing.


5.3.1. Concept matching

Fig. 17 shows the concept matching process for knowledgesharing, which includes the following four steps:

Step 1. Inputs the inquiry content of the concept knowledge.Step 2. Perform similarity matching of the concept name by using

the algorithm for similarity matching of the concept nameproposed in Section 5.2.2.

Step 3. Determine whether the similarity of the concept name islarger than 0.5. If yes, then go to Step 4. If no, no similarconcept name exists, and the inquiry process of the conceptknowledge is completed.

Step 4. Obtain similar concept names.

5.3.2. Intention identification

To satisfy the knowledge requirements of users, this subsectiondescribes how to utilize the user intention extraction and conceptretrieval methods [2,13,18] to assist the user in obtaining anintention tree (Fig. 18). Doing so complies with the inquiry

Fig. 19. User intention tree generation process.

Fig. 18. User intention tree generation.

intention of users based on the obtained similar concept namesfrom the concept matching process. Fig. 19 presents the process ofuser intention tree generation, in which the six steps involved aredescribed below.

Step 1. Obtain similar concepts from the results of concept namematching in Section 5.3.1.

Step 2. Determine whether the obtained number of a similarconcept equals one. If it is equivalent to one, then go to Step4. If it is greater than one, then go to Step 3.

Step 3. Take all similar concepts, and identify a similar conceptwhich is located at the highest layer in the user intentiontree. Then go to Step 4.

Step 4. Conduct the concept expansion based on a similar conceptto obtain its parent concept node. This parent concept nodeis exactly the node of least general concept (LGC).

Step 5. Establish the concept path linking for concept expansion.

Fig. 20. Global ontology of the mold design before knowledge integration.

Fig. 22. Local ontology of the mold design owned by enterprise a before knowledge

integration.

Fig. 21. Related knowledge of the global ontology of the mold design before knowledge integration.

Fig. 23. Related knowledge of the local ontology of the mold de


Step 6. Generate one user intention tree that satisfies a user’sinquiry intention based on the established concept pathlinking for concept expansion.

6. System implementation with an illustrative example

Based on the proposed techniques for knowledge integrationand sharing for collaborative molding product design andprocess development, a prototype of knowledge integrationand sharing mechanism for collaborative molding productdesign and process development was implemented at theKnowledge Engineering and Management Research Laboratory(KEMRL) of National Kaohsiung First University of Science and

sign owned by enterprise a before knowledge integration.

Fig. 27. OWL-based knowledge representation of the global ontology after

knowledge integration.

Fig. 24. Ontology mapping results for integrating the local ontology into the global

ontology.

Fig. 26. Related knowledge of the global ontology of the mold design after

knowledge integration.


Technology in Kaohsiung, Taiwan. Computer hardware in theimplementation environment is equipped with an applicationserver, a web server, and a data and knowledge server. Theapplication programs are implemented using PHP language asthe development tool, MySQL 5.0 version for database, andProtege 3.2.1 Beta version for concept knowledge.

Feasibility of the proposed approach is demonstrated bypresenting the activity ‘‘mold design’’ in the collaborative moldingproduct design and process development as an actual example byoperating the implemented mechanism.

Figs. 20–27 present the user interfaces for the ontology-basedknowledge integration and sharing mechanism for collaborativemolding product design and process development. Before the localontology is integrated. Fig. 20 presents the global ontology of themold design established by Protege, while Fig. 21 describes therelated knowledge of the global ontology of the mold designcreated by Protege. Additionally, Figs. 22 and 23 show the localontology and related knowledge of mold design, respectively. Afterthe implemented system is executed, Fig. 24 summarizes theontology mapping results while attempting to integrate the localontology into the global ontology. Figs. 25 and 26 illustrate theglobal ontology and its related knowledge after the mold designknowledge is integrated. Fig. 27 displays the OWL-based repre-

Fig. 25. Global ontology of the mold design after knowledge integration.

sentation of the integrated global ontology. Finally, Figs. 28 and 29summarize the query and knowledge searching results of conceptknowledge and the related knowledge of the retrieved conceptknowledge, respectively.

Fig. 28. Query and searching results of concept knowledge.

Fig. 29. Related knowledge of the retrieved concept knowledge.


7. Conclusions

This study develops an ontology-based knowledge integra-tion and sharing approach for collaborative molding productdesign and process development. In addition to integratingmolding product design and process development knowledgedistributed among various cooperating enterprises, the pro-posed approach provides users with the ability to share moldingproduct design and process development knowledge. Detailedresults includes (i) the collaborative process of molding productdesign and process development, (ii) the ontology-basedknowledge model, (iii) the web service-based knowledgeintegration and sharing framework, (iv) the ontology-basedknowledge integration methods, (v) the ontology-based knowl-edge sharing methods, and (vi) the ontology-based knowledgeintegration and sharing mechanism.

Results of this study significantly contribute to efforts toachieve knowledge integration and sharing for collaborativemolding product design and process development in order tosatisfy user requests of knowledge, increase molding productdesign and process development capabilities, reduce mold productdevelopment cycle time and costs, and ultimately increase productmarketability.

Acknowledgements

The author would like to thank the National Science Council ofthe Republic of China, Taiwan, for financially supporting thisresearch under Contract Nos. NSC96-2218-E-327-005 and NSC97-2221-E-327-038.

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Dr. Yuh-Jen Chen is currently an assistant professor of

Department of Accounting and Information Systems,

National Kaohsiung First University of Science and

Technology, Taiwan, ROC. He received his PhD and MS

degrees in Institute of Manufacturing Information and

Systems of National Cheng Kung University in 2005 and

2001, respectively, and gained his BS degree from the

Department of Applied Mathematics of Chung Yuan

Christian University, Taiwan, ROC, in 1999. His current

research interests include Enterprise Information Sys-

tems, Knowledge Engineering and Management, and e-

Business.

Documents

Knowledge integration and sharing for collaborative molding product design and process development