Ontology-Based Knowledge Management for Joint Venture Term Projects

8/14/2019 Ontology-Based Knowledge Management for Joint Venture Term Projects

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communication. The ontology-based approach can alsohelp enhance collaborative relationships among knowledgeworkers from different domains. Finally, the approachadvocate in this study is likely to increase the likelihoodof success for the JV project.

The remainder of this paper is organized as follows:

Section 2 discusses the development of a particular EOin relation to the high technology industry. Additionally,this section reviews the recent literature on KM and JV.Section 3 presents the overall EO framework that formsthe central focus of this paper. Issues concerning the pro-cessing of the EO, its verification, and the respectivedomains of joint alliance EO are also described. Moreover,Section 4 discusses an IC industry case in detail. This caseis used to demonstrate the generic types of problem that arelikely to occur with respect to KM in JVs. Finally, Section5 provides a conclusion and discussion of future researchdirections.

2. Literature review

2.1. Literature review of related issues for JV projects

JVs are considered an important strategy for industries.JVs are typically defined as an alliance between two ormore parties in researching, developing, producing, selling,or distributing a product or service for profit (Kukalis andJungemann, 1995). JVs involving international competitorshave attracted growing interest among both researchersand participants (Richter and Vettel, 1995). Many scholarsand researchers are working on the issues related to enter-

prises engaging in JVs.The literature on this subject can be sorted into five cat-

egories: strategic issues, relationship management issues,enterprises modeling issues, performance measurementissues, and KM issues (Table 1).

2.2. EO and KM issues for JVs project

Gruber (1993) defined an ontology as a specification of arepresentational vocabulary for a shared domain of dis-

course including definitions of classes, relations, func-tions, and other objects. The use of the term has a longhistory in philosophy, where it refers to the nature of exis-tents and existence. In a broad sense ontologies form thebasis of what is relevant, what can be referred to and dis-cussed, and what can be modeled in a particular domain.

Research into ontologies has become increasingly impor-tant to knowledge-based systems and KM.In the context of modern organizations it has been

argued that any ontology must take account of activities,agents, roles, positions, goals, communication, authority,and commitment (Fox, Barbuceanu, and Gruninger,1996). In this context, EO is a collection of terms and def-initions relevant to enterprises (Uschold, King, Moralee,and Zorgios, 1998). Much of the previous research hasfocused on developing ontologies for particular organi-zations.

Recent studies on EO and KM for JV projects areshown as follows: Kidd in 2000 proposed a novel devel-

opment in knowledge brokering based on a trustedagent located off-shore. In this agent-based model, thedata warehouse concept is adopted as the basis of asearch for prospects and an attempt to gain benefitsfrom a JV. This study first raised issues concerningKM in the context of collaboration among JV. Madni,Lin, and Madni (2001) presented the IDEONTM extensibleEO for enterprise design, management and control pro-cesses. This work first provides a reference model forthe interoperability between new and legacy businessapplications to integrate and adapt business strategiesand ongoing operations to external and internal environ-

mental changes.Jolly (2002) conducted an investigation to identify deci-

sion and knowledge sharing problems in JVs. This studyshowed the importance of KM and trust for inter-firmcollaboration. Moreover, Wong, Maher, and Luk (2002)outlined the development and attraction of the JVapproach to foreign investment in China. Attempting todiscover what strategic management knowledge was trans-ferred from the Western partner. This study examines theissue of international KM for JV projects.

Table 1

Literature review of JVs researchStrategy Relationship management Enterprises modeling Performance measurement Knowledge management

Kukalis and Jungemann(1995), Mills and Chen(1996), Naylor and Lewis(1997), Vanhonacker(1997), Maccoby (1997),Chen and Chen (2002),Rigby and Zook (2002),Yasuda (2005)

Shaughnessy (1995), Littlerand Leverick (1995),Martinsons and Tseng(1995), Beamish and Inkpen(1995), Gifford (1998),Norwood and Mansfield(1999), Fey and Beamish(2000), Hobbs and Andersen(2001), Steier (2001),Meyerson (2001), Ghosn(2002), Buckley et al. (2002),Walker and Johnnes (2003),

Bayona et al. (2006)

Mesak and Mayyasi(1995), Richter and Vettel(1995), Nakamura et al.(1996), Williams et al.(1998), Wang et al.(2004), Storey (2005)

Luo (1996), Park and Kim(1997), Pearce and Hatfield(2002), Beamish andBerdrow (2003), Swierczekand Dhakal (2004), Mohrand Puck (2005)

Abecker et al. (1998), Kidd(2000), Jolly (2002), Wonget al. (2002), Tsang (2002),Walker and Johannes(2003), Li et al. (2003),Gerwin and Ferris (2004),Chadam and Pastuszak(2005), Revilla et al. (2005),Wong (2005)

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Other research regarding the research JV topic, such asthat of Li, Hsieh, and Sun (2003), has provided an ontol-ogy-based KM system for the metal industry to studyknowledge sharing and development from the macro viewin traditional industry. Li, Wu, and Yang (2004) presentedan ontology-oriented approach that uses logical descrip-

tion to formally represent concepts and roles (relations)relating to the partner view of domain knowledge. Thiswork extends this approach to the high technology industryto further study the application of KM.

This study summarizes previous research and reviewsKM from both the macro and micro perspectives by usingthe ontology-based approach to analyze complex knowl-edge sharing and development in the high technologyindustry.

3. Framework of ontology-based KM

3.1. Key concept of the framework

Ontologies are increasingly considered a key technologyfor enabling semantics-driven knowledge processing(Maedche, Motik, Stojanovic, Studer, and Volz, 2003).McKeown (1992) described Semantics-driven approachesas using knowledge about the case frames of verbs todrive interpretation. Enterprise KM entails formally man-aging knowledge resources to facilitate access and reuse ofknowledge, typically through the use of advanced informa-tion technology (OLeary, 1998). The development of EOfor JVs will typically require activities that involve knowl-

edge sharing, knowledge negotiation, knowledge creationand the resolution of knowledge conflicts. This study devel-ops an EO for a JV, a process for EO development and adictionary for JV EO components.

The development of an EO for a JV can be divided intofour phases: Design, Building, Linking, and Running. Dur-ing the design phase the two enterprises that are enteringinto a JV represent two, essentially independent, knowl-edge repositories. The two jointing enterprises have inde-pendent domain experts owns its domain knowledge.After triggering the JV project, both sides began to prepareand design the knowledge sharing and developing pro-cesses. Knowledge sharing and development start fromthe communication and collaboration processes duringthe Build phase. Two independent domain expert groupsbegin to create the new knowledge base via sharing andinferring their own domain knowledge. In the Link Timephase, the EO plays a key role in integrating the joint enter-prises. Domain experts from different enterprises have towork out the EO together to produce the JVs dictionaryfor both enterprises in the following collaborative JV pro-ject. Finally, in the run time phase, the knowledge workersuse the JVs dictionary to complete collaborative missions.The overall processes continues until the termination ofthe JV relationship. Fig. 1 shows the key conceptual frame-

work use in this study.

3.2. Ontology-based KM

This study provides a model of ontology-based KM forJVs. The ontology-based KM process model consists ofseven important steps for reference by managers whenimplementing JV projects, including: Knowledge acquisi-tion, knowledge identification, ontology analysis, ontologyimplementation, ontology verification, knowledge reposi-tion, and knowledge sharing/development. Fig. 2 showsthe model of ontology-based KM for JVs project. Eachof the seven steps is described in detail below.

Knowledge acquisition: Domain experts provide thedomain knowledge for the JV which can involve the overallbusiness processes and specific technological transfer. Theknowledge acquisition process contains the joint domainknowledge from individual enterprises increase the com-plexity of KM. Poor knowledge acquisition procedurecan lead to incomplete EO result.

Knowledge identification: Domain experts identify whatknowledge needs to be shared and what knowledge doesnot need to be shared in this step. This step prevent theleakage of knowledge and avoids arguments regardingknowledge sharing.

Ontology analysis: This step analyzes the collected dataand information. Based on the analysis, the ontology canbe divided into information ontology and domain ontology

(Abecker, Bernardi, Hinkelmann, Kuhn, and Sintek, 1998).

Domain

Knowledge

Domain

Knowledge

Domain

Experts

Enterprises

Ontology

Domain

Experts

Joint VenturesDictionary

Knowledge

Workers

Knowledge

Workers

Execute Execute

Produce

Use Use

Collaborate

Own Own

EnterpriseA

EnterpriseB

Infer Infer

Design Time

Run

Time

Link Time

Build

Time

Fig. 1. Key concept of framework.

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The information ontology is a meta model that describesknowledge objects and contains generic concepts and attri-butes of all information about knowledge objects. Thedomain ontology consists of the concepts, attributes,and instances of the enterprises involved in the JV.Domain ontology is designed to achieve semantic matchingwhen searching for knowledge objects (Li et al., 2003). Theontology analysis can also include verifying that the partypossessing some technology agrees to that technologybeing transferred to another party.

Ontology implementation: The ontology implementa-tion step executes the ontology process and ensures thecompletion of information and domain ontology after theontology analysis.

Ontology verification: Ontology verification includesverification of: (1) Each individual definition and axiom.(2) Collection of definitions and axioms that are statedexplicitly in the definitions of the ontology. (3) Definitionsthat are imported from other ontologies. (4) Axioms that

can be inferred using other definitions (Gomez-Perez,

1996). The verification of ontology for JVs projectsinvolves more complicated processes and is further dis-cussed in Section 3.3.

Knowledge reposition: The new created ontologies from joint enterprises are stored in the public database forreposition. The public database provides a channel for

accessing the new ontologies and supports subsequentknowledge sharing and development.Knowledge sharing/development: The joint venture

dictionary is generated in this step. The joint venturedictionary consists of the new definitions and axioms forthe JVs project. The enterprises entering into the JV shareand develop the knowledge required for the JV through the joint ventures dictionary, which acts as the basis forimproved collaboration.

3.3. Verification of EO

After processing the EO for enterprises entering into aJV, ontology verification can ensure the correctness of the joint venture dictionary and thus enhance collaborationduring JV evolution. Fig. 3 shows the EO verificationprocess. First, the definitions and axioms of the domainknowledge are collected from the knowledge bases. Theother definitions from the jointing ontologies can beimported to the collecting knowledge bases. The verificationscreens the two parts of definitions and axioms. The clearpart is sent to the JV dictionary for knowledge workers.Meanwhile, the other parts are sent to the inference engineto distinguish the inferable axioms using the definitions andaxioms in the knowledge bases. Some axioms may not be

able to be inferred owing to incomplete content preventingthe inference engine from distinguishing them. These axi-oms are stored in the other knowledge base for later manualanalysis.

3.4. Domains of the enterprises entering into the JV EO

The domains of EO in a JV may involve major businessprocesses and related information. This study categorizes

Knowledge

Acquisition

Knowledge

Reposition

Ontology

Analysis

OntologyImplementation

OntologyVerification

Knowledge

Sharing/Development

Knowledge

Identification

Fig. 2. Ontology-based KM process for JVs project.

Collecting

Definitions &

Axioms

Verification

Check

Import Definition

fromjointing

Ontologies

Axioms can be

inferred using

other Definitions

and Axioms

Joint Ventures

Dictionary

Clear

Not

Clear

Yes

Inference

Store

No

Fig. 3. EO verification process.



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the major knowledge domains as the Technology domain,Finance domain, Marketing domain, Productiondomain, and Human resources domain. The Technologydomain serves as an example demonstrating the JV EOdevelopment processes (Fig. 4). During JVs, the Technology1 domain represents the Technology domain ontology in

company A to JV. The Technology 1 domain may consistof the Product 1 domain, Process 1 domain, Equipment 1domain, Information Technology 1 domain, and Quality 1domain, which represent the domains of product, processes,equipments, information technology, and quality, respec-tively, in company A. The Technology 2 domain representsthe technology domain ontology from company B. TheTechnology 2 domain consists of Product 2 domain, Process2 domain, Equipment 2 domain, Information Technology 2domain, and Quality 2 domain, which represent thedomains of product, processes, equipment, informationtechnology, and quality, respectively, from the company B.

The Product 1 domain consists of four sub-product

domains from company A, namely: Sub-Product 1A,Sub-Product 1B, Sub-Product 1C, and Sub-Product 1D(Fig. 5). Meanwhile, the Product 2 domain consists of foursub-product domains from company B: Sub-Product 2A,Sub-Product 2B, Sub-Product 2C, and Sub-Product 2Dfrom company B. During the JV, the domains of EO arecombined, resulting in the creation of new ontologicaldomains, namely technology joint venture domain, productjoint venture domain, Sub-Product joint venture domain,which represent the technology domain of joint ventures,

product domain of joint ventures, and sub-product domainof joint ventures, respectively.

4. Example

4.1. Introduction to the IC foundry industry

Fig. 6 shows how IC devices are mostly produced byIntegrated Device Manufacturer (IDM) and ApplicationSpecific Integrated Circuit (ASIC) in the initial phase ofthe IC business (Tseng, 2002). The IDM and ASIC compa-nies include functions of system/IC design, wafer manufac-turing, assembly and testing. After the emergence of ICdesign companies (Fabless company), IC Foundries startedto play a very important role in the business. Foundriesmanufacture ICs for design companies or other IDM andASIC companies. Foundries have expanded their technicalsupport for Intellectual Property (IP) design companies byintegrating design services and wafer manufacturing. Each

IC foundry company usually has several wafer fabricationfoundries located in different areas, and even differentcountries. Wafer fabrication usually requires 100500processes over several weeks. Besides the time-consumingoperations, complex manufacturing characteristics suchas reentry process, lot splitting, batch operation, and maxi-mum lot waiting time complicate capacity planning forwafer fabrication (Chen, Chen, Lin, and Rau, 2005, p.710).

Features of the IC operation life cycle, independent ofthe JV itself, include Design, Engineering, and LogisticsCollaboration, as shown in Fig. 7 (Tseng, 2002). Capacity

expansion in IC industries is determined during design col-laboration through interactions among the IP design selec-tion, wafer layout design and wafer mask making process.Engineering collaboration begins with manufacturingevaluation and continues until logistic arrangement. Mean-while, logistics collaboration begins with capacity bookingand ordering process continuing through to the logisticmanagement process. These collaborations may happenconcurrently through different organization units andfunctions.

4.2. Trend of IC foundry JVs

To remain competitive in the evolving semiconductorindustry environment, some IDM/ASIC companies, sys-tem companies, and Fabless companies have begun toenter into joint ventures with IC Foundry companies.Table 2 shows a clear increase in JVs involving IC Foundrycompanies in the semiconductor industry over the past fiveyears. The strategic purposes of these JVs include new tech-nology sharing, capacity sharing, and gaining increasedsupport for production.

The above table demonstrates that most strategic JVprojects require technological collaboration and knowledgesharing/development. Specifically, IC Foundry companies

must integrate complex business processes, respond to the

Joint VenturesProject

Technology2

Finance2

Marketing2

Production2

HumanResource

2

Product2

Equipment

2

InformationTechnology

2

Process2

Quality2

Technology1

Finance1

Marketing1

Production1

HumanResource

1

Product1

Equipment1

InformationTechnology

1

Process1

Quality1

Fig. 4. Domains of EO in JVs project.



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challenge of collaborating with respect to different technol-ogies, and engage in complex KM. Poor KM and ineffec-tive communications can cause JV project failure or evenresult in lost business in the competitive semiconductor

industry.

4.3. Case study of the IC foundry industry

4.3.1. Example of processing EO for IC foundry industry

Considering the case of two IC Foundry companiesestablishing a strategic joint venture to share advancedtechnology, the jointing companies first perform technol-ogy transfer and sharing during the JV project. As dis-cussed in the previous section, the ontology-based KMfor the JV project in this study consists of six steps: knowl-edge acquisition, ontology analysis, ontology implementa-tion, ontology verification, knowledge reposition, andknowledge sharing/development. In this case, the advancedtechnological product, process, and equipment in the ICindustry are used as an example to describe the ontologyprocesses for managing knowledge sharing and develop-ment in the JVs project. Fig. 8 shows the example of pro-cessing EO for the jointing IC Foundry companies.

Knowledge acquisition may occur in one of theadvanced technologies of the 0.13 lm process and theproduct type of Logic provided by the product engineersfrom company A. The product engineers of company Bmay also provide experience in running 0.13 lm process

and product type of Logic during collaborative meetings

Joint Ventures

Project

Technology

2

Finance

2

Marketing

2

Production

2

Human

Resource

2

Product

2

Equipment

2

Information

Technology2

Process

2

Quality

2

Technology1

Finance

1

Marketing

1

Production1

HumanResource

1

Product

1

Equipment

1

Information

Technology

1

Process

1

Quality

1

Sub-

Product

2.A

Sub-

Product

2.B

Sub-

Product

2.C

Sub-

Product

2.D

Sub-

Product

1.A

Sub-

Product

1.B

Sub-Product

1.C

Sub-

Product

1.D

TechnologyJV

Product

JV

Sub-Product

JV

Process

JV

Equipment

JV

Information

Technology

JV

Quality

JV

Fig. 5. Domains of jointing EO.

System Design

IC Design

Fab

Assemble

Test

IDM/ASIC

IP

Fabless Co.

Foundry

Contract

Assemble

Test

Design Service

System

Design

System Co.

IC Design

Fig. 6. IC foundry business.



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and discussions. The equipment used in the process consistsof the photo, thin film, etch, and diffusion for both compa-nies. Furthermore, the related equipment engineers help toprovide related equipment information. The ontology anal-ysis process starts from analyzing the collected definitionand related information of the 0.13 lm process, Logicproduct, which may include the design IP, the layout toproduce masks, mask layers, manufacturing issues, qualityrequirement, and shipping procedures. The information ofequipment may include the recipes of each machine, main-tenance schedules, machine vendors, spare parts, machinedimensions, etc. Some information may be reserved for

business certain issues and is also discussed in the process.

The ontology implementation process is executed afterthe analysis of ontology. Certain information ontology(such as process types, components of layout, maskinformation, manufacturing information, etc.) anddomain ontology (IP number, dimension of each masklayer, wafer specification, wafer acceptance testing speci-fications, inspection specifications, etc.) are created forthe JV project. The example of the jointing domainontology process is detailed in Section 4.3.2. To ensurethe correctness of the ontology, the ontology verificationprocess is continuously executed after each of the ontol-ogy implementation process. To further understand the

importance of the ontology verification process, the

Manufacturing

Evaluation

Capacity Booking &

OrderingDesign IP Selection Layout Mask Making Manufacturing Manufacturing

QualityLogistic Management

Design Collaboration

Engineering Collaboration

Logistic Collaboration

IC Operation Life Cycle Points of View

Fig. 7. IC operation life cycle resource from .

Table 2Major IC foundry JV events in past five years

Year Joint ventures (JVs) Rationale2005 Advanced Micro Devices Inc. (AMD) and United

Microelectronics Corp. (UMC)Establish a joint 300-mm wafer foundry venture in Singapore for high-volumeproduction of PC processors and other logic products starting with 65-nmtechnology

2004 Fujitsu and Sumitomo Electric Increase sales of a wide variety of compound semiconductor devices throughdevelopment and manufacturing

2003 Jazz Semiconductor and Hua Hong NEC (HHNEC) Increases current Jazz capacity for digital, analog and RF CMOS processes aswell as SiGe Bi-CMOS

Taiwan Semiconductor Mfg Co. Ltd (TSMC) andOmniVision Technology, Inc.

Provides integrated back-end manufacturing services for image sensors,including color filters

Infineon Technologies AG and United Expitaxy Company(UEC)

Matches Infineons optoelectronic chip technology with UEC, its back-endexpertise

2001 Micron Technology Inc. and Hynix Semiconductor Inc. Micron Technology Inc. uses the Korean chip maker as a foundry for DRAMs

2000 Hitachi, Ltd. and United Microelectronics Corporation(UMC)

Achieves maximum production capacity for UMC 300 mm wafer facility

Amkor Technology and Toshiba CorporationsSemiconductor Company

Collaborates with Iwate Toshibas assembly and test operations, will allow formanufacturing operations at two independent subcontract assembly houses

IBM China Company Limited (IBM China) and ChinaGreat Wall Shenzhen Co., Limited

Provides electronic manufacturing services for Nokia JV manufacturingoperations

Chartered Semiconductor Manufacturing and LucentTechnologies Microelectronics Group

Forged a $700 m R& D agreement for next-generation IC communications

Malaysias Mimos Bhd and Integrated Silicon Solution Inc(ISSI)

Establishes a fabless design and marketing organization for non-volatilememory products

1999 Philips, the Taiwan Semiconductor ManufacturingCompany and the Economic Development Board ofSingapore

Produces chips at sizes of 0.25 lm, 0.18 lm and smaller

http://www.witsa.org/awards02/nominees/witsa/TSMC.pdfhttp://www.witsa.org/awards02/nominees/witsa/TSMC.pdf


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example of ontology verification will be discussed indetail in Section 4.3.3.

The knowledge reposition process starts after the com-pletion of ontology processing. The identified terms arestored in the database for further retrieval. The knowl-edge sharing/development process provides informationand knowledge for the other related knowledge workersto access. The joint venture dictionary is created in thisprocess. Managers and knowledge workers, such as project managers, process engineers, manufacturing supervi-sors and quality engineers, can use the JV dictionary toefficiently retrieve required information and common def-initions for communication and collaboration withpartners.

4.3.2. Example of domains of jointing EO

In the domains of joined EOs (Fig. 9), the process

domains of company A are 0.13 lm, 0.15 lm, 0.18 lm,

and 0.22 lm. Meanwhile, the process domains of companyB are 90 nm, 0.13 lm, 0.25 lm, and 0.35 lm. Moreover,the ontological domains merged in the JV are the newontology for the 0.13 lm process is created after processingthe JV EO. Company A can also receive technologicalsupport for 90 nm from company B to increase competi-tion in advanced technology. The new ontological domainfor the 90 nm processes is fully transferred from companyB during the JV.

The product domains of company A consist of OR com-prise logic, DRAM, and high voltage. The productdomains of company B consist of logic, NVM, SiGe, andcolor filter. The new ontology for the logic product iscreated after processing the EO for the JV. The equipmentdomains of companies A and B consist of photo, thin film,etch, and diffusion. The new ontologies for photo, thinfilm, etch, and diffusion are created after processing the

EO for the JV.

Comparing Definition of

0.13M process

Store

Information

Verification

Yes

NoTerm

Usable ?No

Term

TerminationNo

Assign New

Term

OK

Yes

Documentation &Announcement

A COMPANY0.13MPROCESS

1.PATTERN SELECTION

2.EQUIPMENT REQUIREMENT

3.MASK PREPARATION

B COMPANY0.13 M PROCESS1.PATTERN SELECTION

2.EQUIPMENT REQUIREMENT

3. MASK PREPARATION

Product Engineer from A company

Product Engineer from B companyDiscussion Meeting

Joint Ventures Dictionary

Process Engineer of A CompanyProcess Engineer of B Company

Knowledge

Acquisition

Ontology

Analysis

Ontology

Verification

Ontology

Implementation

Knowledge

Reposition

Knowledge

Sharing/

Development

Knowledge

Identification

Fig. 8. Example of processing EO for jointing IC foundry companies.



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4.3.3. Example of EO verification

Fig. 10 shows an example of verification of EO for0.13 lm process. First, the definitions and axioms of0.13 lm process are collected from company A. Then, thedefinitions of 0.13 lm process from company B areimported to the ontology of company A. In the productdomain of 0.13 lm, in both company A and B, there maybe 1.0 V, 1.2 V and 1.5 V core options, and I/Os of 2.5 V

and 3.3 V. the 1.0 V, 1.2 V and 1.5 V are verified and sent

to the JV dictionary for reference. The domain ontologyof 6T SRAM cell size (2.43 lm2) for various system-on-chip (SOC) applications in the networking, computingand consumer market segments cannot be verified. Afterthe inference procedure, the definition of SOC is found anew term for the JV project. Moreover, the new term isnot involved in the present project. The definition ofSOC is first stored in the other database for reasons of

security OR owing to security concerns.

ProcessB

90 nm

B

0.13 m

B

0.25 m

B

0.35 m

B

TechnologyB

Joint Ventures

Project

Technology

A

Process

A

0.13 m

A

0.15 m

A

0.18 m

A

0.22 mA

Technology

JV

Process

JV

0.13 m

JV

90nmJV

Product

A

Product

B

LogicA

DRAM

A

HighVoltage

A

LogicB

NVM

B

SiGeB

Color Filter

B

Product

JV

Logic

JV

EquipmentJV

EquipmentA

EquipmentB

Photo

A

Thin Film

A

EtchA

DiffusionA

Photo

B

Thin Film

B

Etch

B

DiffusionB

Photo

JV

Thin Film

JV

Etch

JV

Diffusion

JV

Fig. 9. Example of domains of jointing EO.



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In the IC foundry industry, the collaboration of two

jointing companies may involve only partial technologytransfer but not full support for some strategic securityissues. The verification of EO for the JV project has to pro-tect the intellectual property of the companies. On theother hand, EO verification also helps the collaborationfor both companies to run the project smoothly. Therefore,poor verification process can cause serious problems andimpact JV project success.

5. Conclusion

This study provides a reference framework for managers

during knowledge sharing and development in a JV pro-ject. This work adopts an ontological approach to analyzeKM processing in JVs. The IC Foundry industry is used asan example to study the practicality of the proposedapproach. The characteristics of complex business pro-cesses and high technological issues in the IC Foundryindustry increase the challenge of this research. This studyaimed to provide managers with assistance to increase theirchanges of successfully dealing with complicated JVprojects.

The inaccuracy and discrepancy of the original informa-tion provided by the domain experts represents an impor-tant limitation of this study. The information of specifictechnology domains can provide a reference for improvinginformation on this area. This study focuses on the KM ofthe technology domain. Other domains, like finance, mar-keting, production, and human resources, would also makeinteresting topics for future research. Meanwhile, futurestudies can examine other forms of real world strategic alli-ances, such as mergers or acquisitions.

After we identified the process of knowledge sharing anddevelopment for the collaborative companies, knowledgeaccess and cultural differences represent another two keyissues of concern. Some previous studies concern accessto knowledge from some different perspectives, including:

access control (Bertino, 1998; Ray, 2004), modeling knowl-

edge access process (Gaines and Shaw, 1997; Musen,Fagan, Combs, and Shortliffe, 1999), and strategy ofknowledge access (Beamish and Berdrow, 2003; Richterand Vettel, 1995). This study was not concerned with cul-tural differences. However, national culture is a pertinentinfluence on KM. Child and Rodrigues (2002) delineated

that national cultural differences may foster feelings of dis-tance and conflict in a JV, and may hinder KM between thetwo groups of people. Consequently, it is reasonable toexpect that JV partners will rely increasingly on manage-ment involvement for knowledge acquisition with increas-ing cultural difference between them.

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Collecting

Definitions &Axiomsof 0.13 m

process

VerificationCheck (1.0V,1.2V,

1.5 V,SOC)

Import Definition from

jointing Ontologies of0.13 m process

Axioms can be

inferred usingother Definitions

and Axioms

Joint Ventures

Dictionary

- 1.0 V

-1.2 V-1.5 V

Clear

NotClear

Yes

Inference(SOC)

Store-system-on-chip

(SOC)

No

Fig. 10. Example of verification of EO.

http://bss2.bham.ac.uk/http://bss2.bham.ac.uk/


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Documents

Ontology-Based Knowledge Management for Joint Venture Term Projects