Reusing design knowledge based on design casesand knowledge map

Cheng Yang • Zheng Liu • Haobai Wang • Jiaoqi Shen

Published online: 2 March 2013� Springer Science+Business Media Dordrecht 2013

Abstract Design knowledge was reused for innovative design work to support designers

with product design knowledge and help designers who lack rich experiences to improve

their design capacity and efficiency. First, based on the ontological model of product

design knowledge constructed by taxonomy, implicit and explicit knowledge was extracted

from some design cases. The design knowledge was expressed using a constructional

organization. With the knowledge map, design knowledge was illustrated to help novice

designers reconstruct specific design cases, thus, encouraging innovative design. Four

groups of designers were invited to participate in an experiment for the design knowledge

map system. The experiment results verified the effectiveness of the method.

Keywords Design case � Ontology model � Knowledge map � Implicit knowledge �Reuse of knowledge

Introduction

When leading or participating in a design project for an innovative product, most

designers, especially novices, deeply hope to obtain effective support, such as sharing

multi-major design knowledge, making design decisions, organizing, and managing a

design team. In the complicated process of developing a new product, product designers

have to deal with an immense amount of information, part of which is obscure and

scattered. Ideas on marketing product information and design case are examples of

important intangible design properties, which can be maximized with effective manage-

ment and organization. Therefore, reusing important information effectively can help

designers improve the quality of their design.

C. Yang (&) � H. Wang � J. ShenZhejiang University City College, No 51 Huzhou Street, Hangzhou, Zhejiang, Chinae-mail: yangchengyc@126.com

Z. LiuChina Academy of Art, No 218 Nanshan Road, Hangzhou, Zhejiang, China

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Int J Technol Des Educ (2013) 23:1063–1077DOI 10.1007/s10798-013-9239-7

The study of creative design focuses not only on the process by which design knowl-

edge is recycled, but also on how to help designers reuse this knowledge (Doultsinou et al.

2009; Zdrahal et al. 2007). The important aspects of this research include the design

knowledge model, the method used to present implicit knowledge, and design knowledge

management.

A design knowledge database is based on the design knowledge model constructed to

classify and retrieve design knowledge and allow design knowledge to be represented,

stored, and used conveniently. Common knowledge models include the concept design

process model for design cognition (Kruger and Nigel 2006), general design mission model

(Chen 2005), structural plan model (Owen 2001), common concept design frame model

(Sebastian et al. 2001), and product design process model (Liu et al. 2008). These models

were used by important personalities in design to classify design knowledge for future use

and management. These studies are effective and helpful in reusing design knowledge

work. However, these models do not consider the relationships among the various design

concepts in a design knowledge model. Thus, the users of these knowledge systems have to

be experienced designers who understand these design concepts well beforehand.

Implicit design knowledge mainly includes the designers’ inspiration, experiences, and

skills, which are very difficult to present clearly with language but are very important for

designers. Implicit knowledge (Reed et al. 2010) is subjective and vague, which makes it

difficult to be coded and managed. Scholl et al. held that studies on implicit knowledge

transfer mainly focuses on the transfers of characters, design process model, and of situ-

ation, etc. (Zhao and Peng 2004). Tomi believed that the knowledge base of designers

could be enhanced and collated with the spiral ascendant process (Tomi 2004). Although

these studies focused on discovering the characteristics of implicit knowledge, they seldom

explored the concomitant relationships between implicit knowledge and explicit knowl-

edge, nor did they consider the systematic use of implicit knowledge.

In the field of design knowledge management, the common areas of study are the processes

of gaining knowledge, transferring knowledge, constructing the knowledge model, mapping

knowledge, integrating knowledge, and reusing knowledge (Liao 2003). These aspects are

the basis for sharing design knowledge. With the research on knowledge management sys-

tems, data mining, and artificial intelligent expert system (Tan and Feng 2007), a design

information platform was constructed to take advantage of the design knowledge resources.

Researchers on design knowledge engineering, who explored the various aspects of design

knowledge, which consists of object-oriented knowledge, process-oriented knowledge, and

realization-oriented knowledge, believe that design knowledge can be described and nor-

malized (Jong et al. 2011). However, the problems arising in the product design process are

varied, and design theories are complicated. Therefore, discovering, obtaining, and orga-

nizing scattered knowledge is important in design knowledge management.

Research supporting product innovative design knowledge aims to organize and manage

existing design knowledge, as well as to help designers obtain relevant information. For

designers who already have a rich experience in design, such help is enough to promote

innovative thinking. However, novice designers, who lack substantial practice, cannot

integrate the disorganized design information without concrete design situations.

Knowledge requirements of designers

The problem of product innovation design is poorly defined typically lacking a clear

definition of the initial state, ultimate goal, solving strategies, operating procedures, and

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testing standards. Product innovative design thinking is illustrated as a black box to deal

with a large amount of relevant design knowledge (Howard et al. 2008), as shown in

Fig. 1. Without enough design experience, designers would face many difficulties, which

are as follows:

1. In most design knowledge management systems, design knowledge generally exists in

information fragments. Designers who lack design experience may have difficulty

combining these information fragments into an effective and good design idea.

2. Some preconditions or implicit needs of design knowledge are not generally specified

in advance. The information obtained from a design knowledge management system

could be misunderstood by novice designers.

3. Without rich experiences, designers will not be able to understand what kind of design

knowledge needs to be understood in advance.

4. In obtaining design knowledge, much invalid and repeated information exists;

designers need to judge the validity of the design information based on their

experience.

5. Novice designers need a great deal of design practice, reading, memory, and critical

thinking to identify a good idea to enhance their design cognition. The process of

conceiving a good design idea is slow and lengthy.

Therefore, a knowledge management system that will provide designers with knowl-

edge of design cases, such as the design object, target, use environment, and users’

behavior with regard to the product, is needed so that designers can gain explicit design

direction from the start, thereby improving the design efficiency and success rate.

Designers need inspiration from design methods, information, and previous decisions.

Most designers not only need to obtain design knowledge, but also need to know what kind

of knowledge is useful for a particular design idea.

Excellent designers achieve their design knowledge through enormous amounts of

practice with design cases. They memorize and understand these design information

according to specific design situations, which can integrate scattered information in the

designers’ minds. However, many ordinary designers, especially novices, cannot meet their

own needs for ideas (McLaren and Stables 2008) just by looking for scattered design

information, because they lack practical design experiences and understanding. Therefore,

an advanced design knowledge system for designers should not only provide explicit

design knowledge, but also implicit knowledge, including design situation, design

Fig. 1 Design thinking process

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experience, understanding, etc., which can help designers understand design information

fragments.

Gaining knowledge based on design cases

Product design knowledge is complicated and unorganized, and information on rules and

implicit knowledge is limited. Thus, the result of the design is nondeterministic. Design

cases, which contain the initial conditions and objectives of a design problem, are taken as

a solution strategy for a particular design problem. Therefore, recycling these cases can be

very useful for design ideas. In a poorly defined structure, specific cases can provide

efficient ways to save and transmit implicit knowledge. Cases that have rich information

can help a designer understand the current problems, so that they would not have to start

from scratch. The design case will effectively enhance design efficiency.

Design knowledge based on cases

Four classes of industrial design undergraduate students and seven postgraduate students

conducted a design experiment. Thirty junior or senior students composed each class. The

subject of design experiments is consumer electronics, such as TV sets, refrigerators,

electric fans, and electric lighting. After the experiments, we interviewed these students

and asked them to describe their chosen design process. The novice designer’s design

process presents a common characteristic, that is, they are more inclined to use case-driven

design thinking, as affirmed by existing research results (Cynthia et al. 2005). Although the

works of novice designers are different, most of them experience the following process:

1. Clarify the design requirements. Read the design brief to clarify the design

requirements and prepare for the next step.

2. Search cases. As novices’ design goals are not clear, they will search partly related or

unrelated design cases.

3. Select relevant cases. In this stage, novices will choose design cases that are relevant

to the design problems or have similar functions.

4. Analyze the selected cases. Analyze the selected cases from various aspects, such as

design issues and target.

5. Match cases. Map the known design problem into the selected case to obtain the

solution methods, processes, strategies, and/or technology of the problem at hand.

6. Modify/Adjust. Modify and adjust the sub-solutions to meet the requirements of the

current design problems.

7. Synthesis. Integrate the contents or conclusion obtained from the selected cases and

the adjusted sub-solutions into a new design alternative.

The accumulation of knowledge for novice designers is a transformation process

beginning from descriptive knowledge to process knowledge, that is, from knowing what

the problem is to knowing how to solve it. Novices lack the implicit knowledge and

available valid design resources that professional designers have, which renders them

unable to submit a large number of design solutions using analog methods, unlike a

professional designer.

User needs can be obtained from the scenario design, which involves a simulation and

description process of the product system’s usage based on users and its environment,

‘‘plotting’’ the task, simulating problems and solutions to obtain user needs, and verifying

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designs. Using scenario design can help identify the user behavior that is rarely considered

in reality. In addition, to determine the validity of the design information obtained, we also

need to do an ethnographic research to observe the process of user behaviors and determine

the effective demand by analyzing the motivation and scenarios showing user experience.

In truth, most innovative products are not fabricated from scratch. Instead, these are

contrived and achieved by borrowing ideas from existing products or the user’s life

experience and transplanting them in new products to achieve an innovative design.

Therefore, providing novice designers with design case knowledge can help them reuse

design knowledge, improve their ability to acquire relevant information, enable them to

learn from the past, and build their own design knowledge base.

Knowledge acquisition of product innovative design

A knowledge management system, which we want to create, should be able to obtain,

accumulate, classify, and share systematically the knowledge, information, and specific

design methods for the development process of future product concepts. The system would

organize and re-organize the designer’s explicit and implicit knowledge to achieve

knowledge sharing and design innovation.

We primarily used observation, questionnaires, interviews, discourse analysis, task

analysis, systems analysis, and data mining methods to access and filter design knowledge

from external knowledge sources (e.g. firms, design companies, free designers, and the

Internet), including the description texts, tables, sketches, schematics, diagrams, rela-

tionship diagrams, behavioral maps, video, models, and user feedback on products. The

knowledge management system can organize, classify, and reconstruct the collected

information for future retrieval and reuse. Importing knowledge can be done in two ways:

manual mode (users add the information) and system mode (‘‘capture’’ the case knowledge

of certain product designs from the Internet automatically). Users input the key words to

search design cases according to the design requirements, after which, the system executes

a fuzzy dynamic combination search to retrieve relevant design cases. Afterwards, the

users match the design problems with the search results, analyze and use the selected cases

to produce new design solutions, and provide feedback on the cases. Hence, users will

optimize the system library based on user feedback, and the new design solutions will

become part of the new external knowledge sources. The system flowchart is shown

in Fig. 2.

Ontology model of product design knowledge based on design cases

The establishment of the product design knowledge model is the basis for design

knowledge usage and expression. As shown in Fig. 3, product innovation design is a

typically poorly defined problem (Feng et al. 2007) that lacks a clear definition of the initial

state, ultimate goal, solving strategy, operational procedures, and testing standards. The

definition of product design knowledge is loose thus, building knowledge models to

analyze the information and knowledge included in design practices is necessary.

Ontology is a conceptual expression referring to the intrinsic conceptual structure in the

field. Ontology classes are connected by attributes, which clearly express a semantic

relationship among different entities. Ontology provides the basic concepts of the field as

well as a macroscopic understanding of the relation among these concepts. Therefore,

ontology contains rich semantic relations and strong inference functions, making it an

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excellent carrier for design knowledge information and solution for the problem of data

heterogeneity at the semantic level.

The ontology model of product design knowledge based on design cases is the

framework with different granularities of abstraction. The frame system is classified into

several levels of abstraction: class, second class, task, and specific behavior, which sum-

marize the different types of design knowledge gradually from the highly abstract to the

very specific levels. The versatile and adaptable system structure can accord specific

application situations to choose the suitable abstraction granularity, as well as decompose,

polymerize, and map knowledge in specific design cases.

Accuracy and a detailed degree of the product design knowledge model directly

determines the accuracy and practicality of follow-up studies. Therefore, an ontological

framework needs to follow three main principles:

Fig. 3 Fuzzy state of product innovative design

Fig. 2 Flowchart of product design

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1. Universal principle. The model should be abstract, able to reflect the development

process of the different types of products and design projects.

2. Dynamic principle. The model should be altered to fit the specific cases for new

product development.

3. Cross principle. The model should be able to support the cooperative work of different

individuals from different fields and reflect the field knowledge involved in the team‘s

development activities.

With the classification of case knowledge, the different levels of abstraction, the

inductive method, and the knowledge framework model was established. By defining the

underlying activities involved in the design process, activities with similar targets and

participants were classified into one group. The advantage of using the example investi-

gation method in establishing the design knowledge framework is that it allows designers

to use other designers’ experience, analyze design processes better, and utilize research

results to support practical design work.

According to design considerations, we divided the upper ontology of product inno-

vative design knowledge into six categories, namely, user, behavior, appearance, function,

process technology, and culture, as shown in Fig. 4. During the retrieval process, the

system will compare these six categories to improve retrieval accuracy.

Overall expression of product design knowledge structure and visual designknowledge network

Given that novice designers lack experience, they are unable to integrate scattered design

information and can seldom formulate effective breakthroughs, often producing ambiguous

ideas. The key to the novice designer’s innovative design process is to help them construct

the specific design situation in their mind despite having insubstantial design experience

Fig. 4 Ontology model of product innovative design

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and to help practicing designers collate the large number of design knowledge fragments

into a comprehensive collection.

Designers have to put the fragment into the overall context of design knowledge and

understand how this piece of knowledge is connected with the general framework of design

knowledge to understand a single knowledge fragment. In the context of design knowl-

edge, designers will be able to find the implicit design knowledge hidden in practices and

cases and merge a variety of fragment formation properties into a systematic concept.

Thus, design knowledge indicates which contents of the design knowledge management

system should be combined in accordance with the corresponding time sequence or the

internal logical relationship to express the overall structure, thereby forming a visual

design knowledge network.

By applying the overall expression of the design knowledge structure and the visuali-

zation of the knowledge network map, designers will be able to master the knowledge of

product design, to rebuild design situations easily in their minds and to broaden their

understanding of innovative design. By searching and operating the visual design

knowledge network map, designers can quickly access global and local knowledge sys-

tems, obtain a multidisciplinary understanding of design, enhance knowledge absorption,

and increase efficiency in design innovation.

Structural product design knowledge

We managed the product design knowledge structure based on ontology, a finite set of

conceptual entities, which can help solve the diverse issues of the terminology of similar

concepts in the knowledge network. This framework can represent and retrieve knowledge

effectively, eliminate semantic ambiguity between ideas from different areas, support

information discovery, match and group ideas in richer ways, and increase the depth of

knowledge utilization. The structural management process of product design knowledge is

presented in Fig. 5.

First, case structures are designed. Design cases are analyzed and deconstructed into

design elements, which are used to build the ontological base and the design knowledge

base for searching.

Second, case retrieval is designed. Similar cases are obtained using keyword search and

fuzzy search according to the design requirements.

Third, knowledge reuse is designed. Design cases that contain explicit and implicit

design information are reused to design innovative products and generate new solutions.

Fig. 5 Knowledge management model of innovative product design

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Design results are fed back to the ontological and knowledge bases, thus optimizing the

system.

Visual design knowledge network

The overall design knowledge environment can be constructed based on the ontology

network of product design knowledge. Using the visual map to express design knowledge

resources, the visual knowledge network for product design innovation is established.

The knowledge element, which is defined as unit ontology = (I, O), refers to the

knowledge unit in the product design knowledge ontology frame. ‘‘I’’ is the finite set of

concepts, which are conditions of getting into knowledge elements, whereas ‘‘O’’ is the

finite set of concepts, which are the output targets of a knowledge node. The ‘‘I’’ and ‘‘O’’

of different knowledge elements contain different knowledge domains. The knowledge

element consists of a frame structure and utilizes slot, slot values, side, and side value to

store knowledge categories and the corresponding attribute values in a hierarchy. As shown

in Fig. 6, the knowledge element has a tree structure distribution that is expandable,

intuitive, and maintainable and can eliminate the confusion in terminology and concepts.

Based on the ontology model of product design knowledge, case knowledge is

deconstructed into multiple levels of the granular abstraction of knowledge elements.

Different knowledge elements, which are combined into a tree structure, express product

design knowledge structure, as shown in Fig. 7. The knowledge content of the elements is

classified into multiple layers to illustrate the depth of knowledge based on the basic

concept, structure, and theory for the purpose of knowledge reuse.

Multiple design knowledge levels are selected to construct a visual knowledge network

map. Logical instructions such as sequential, hierarchy, juxtaposition, causal, and evolu-

tion are added between knowledge elements and knowledge slots. The product design

knowledge system was constructed using knowledge element association and a knowledge

link.

The visualization function modules of the design knowledge network consist of three

sections:

1. The layout control. The traditional tree structure, radius tree structure, hyperbolic tree

structure, and other structures need corresponding layout control mechanisms to

realize the visualization of an automatic layout for the design knowledge elements.

2. The data control. Considering the focus of users, the system loads data corresponding

to the knowledge network map.

3. The interactive control. Certain interactive functions, such as zoom, abstract,

thumbnail navigation, and other tools for finding and understanding the design

process and results are provided.

Fig. 6 Structure of knowledge element

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Active control mechanism for users

The traditional design knowledge management system mainly supports user query and

supplementation, but provides less support for users’ modifications of the knowledge

source. The present research thus intends to strengthen autonomous user control.

Users can add new knowledge obtained from the design process to the knowledge

system. The system will expand the knowledge base spontaneously to constantly improve

itself through the get-store-use cycle.

Moreover, users can also modify the structure of the design knowledge network, which

can be adjusted for specific product categories to help the knowledge system adapt to a

variety of complex situations. For example, in the traditional wooden furniture product

design, we do not need to have knowledge elements concerning metal, glass, and chrome,

as shown in Fig. 6. Through various knowledge element operations, users can change the

content and structure of the knowledge element, reflecting the characteristics of the

knowledge structure in a specific case. In the present study, a variety of other knowledge

element operations includes merger, division, trim, insert, and mutation.

Finally, users can add personalized annotations. In knowledge slots on the knowledge

elements, users can add their own experience and mark human resources for relevant

expert advice.

Experiment verification

We collected a large amount of design information on furniture and developed a knowl-

edge management system prototype for furniture design based on the Web 2.0 technology

to enable users to access and upload design knowledge via the Internet Explorer browser.

Fig. 7 Local enlargement sketch map of the overall structure of design knowledge

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Design knowledge map system

Based on the design knowledge ontology model, furniture design case knowledge is

deconstructed into multiple levels of abstraction granules of knowledge information, which

was inputted into the knowledge map system via an information input interface, as shown

in Fig. 8.

The knowledge on furniture design cases was distracted on the design knowledge

ontology model and was inputted into the system with different levels of knowledge

elements. The knowledge map was generated with a drawing engine. The map can be

scaled to display different abstract granularity distributions of knowledge, as shown

in Fig. 9.

Users can comment and adjust knowledge nodes and modify the association between

factors in the knowledge map system according to a specific usage situation, as well as

extend a particular knowledge space, as shown in Fig. 10.

Users can search through the results to find multiple matching queries of furniture

knowledge in the map system. Using the knowledge elements, the user can access detailed

design information, which includes text, images, video, and other data, as shown in

Fig. 11.

Experiment analysis

We invited 48 product designers composed of postgraduate design students and young

designers in enterprise, to test the design knowledge map system by quickly designing a

variety of furniture drafts in an hour. The design requirements were that the furniture

should be designed for general families, and should include only a table and a chair. The

designers were divided into groups A, B, C, and D according to design experience. Twelve

designers constituted every group. The designers from group A were 26.5 years old on

Fig. 8 Information input interface of the design knowledge map system for furniture

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average and had engaged in design work for an average of 3.4 years. The designers in

group B were 27.2 years old on average and had engaged in design work for a mean time

of 3.6 years. The designers in group C were 25.4 years old on average and had engaged in

design work for an average of 2.8 years. The designers in group D were 26.1 years old on

average and had engaged in design work for an average of 3.2 years. The data from the

design experiment are shown in Table 1.

Fig. 9 Partial illustration of the design knowledge map system for furniture

Fig. 10 Adjustment interface of the knowledge system

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In the design experiment, only groups A and C were asked to use the design knowledge

map system. Group A produced 58 effective solutions (see Fig. 12). Each designer pro-

duced 4.83 design solutions, with a maximum of 8 solutions and a minimum of 3. In

addition, 35 of the 58 solutions were morphological changes of design cases in the base.

The remaining 23 solutions were unrelated to the design cases. Group B produced 41

effective solutions in total. Each designer produced 3.42 design solutions, with a maximum

of 6 solutions and a minimum of 2. Group C produced 61 effective solutions in total. Each

designer produced 5.08 solutions, with a maximum of 7 and a minimum of 4. In addition,

40 of the 61 solutions were morphological changes of the design cases in the base; the

remaining 21 solutions were unrelated to the design cases. Group D produced 37 effective

solutions in total. Each designer produced 3.42 design solutions, with a maximum of 5

solutions and a minimum of 2. The findings demonstrate that, quantitatively, the design

knowledge map system is useful for designers in designing innovative products.

Within the 35 case-based solutions in group A, 17 design solutions are similar in form to

the design cases from the knowledge map system, which means that these solutions have a

high correlation with the cases found in the system, with 8 having a high degree of

innovation, as evaluated by other designers. The remaining 18 solutions were less similar

Fig. 11 Content of a knowledge element in the knowledge system

Table 1 Data from the design experiment

Group Meanage

Mean timeengaged indesign

Generalnumber ofsolutions

Meannumber ofsolutions

Number ofcase-basedsolutions

Maxnumber ofsolutions

Minnumber ofsolutions

A 26.5 3.4 58 4.83 35 8 3

B 27.2 3.6 41 3.42 6 2

C 25.4 2.8 61 5.08 40 7 4

D 26.1 3.2 37 3.08 5 2

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in form to the cases in the knowledge map system which means that the solutions have a

low correlation with the cases, 13 of which have a high degree of innovation. Within the 40

case-based solutions in group C, 21 design solutions have a high correlation with the cases

of the system, 6 of which have a high degree of innovation as evaluated by other designers.

The remaining 19 solutions have a low correlation with the cases, with 15 having a high

degree of innovation.

We found that designers who created design solutions similar to the cases in the

knowledge system are younger. We speculate that the quality of the selected cases may

affect the quality of the design solutions, and this effect will decrease along with the

increase in experience. In addition, the cases provided by the system do not seem to limit

the participants’ imagination and creativity, and they are still able to propose innovative

solutions that are significantly different from the existing ones.

All designers from groups A and C proved that the knowledge map system is effective

for design work and can help designers find case knowledge easily. However, 5 designers

said the system also needs to be improved for several reasons. First, the case base of the

design knowledge map system is incomplete, having only successful design cases, not

including failed ones. For novice designers, these failed cases are equally important.

Through comparison, they will help novices make design decisions, learn from past

experiences, and understand current issues better. The more information the case base

possesses, the more effective assistance the novices will obtain. Second, the terms of

design classification have many synonyms or polysemy, which can affect the novices’

knowledge retrieval. Therefore, the case base of the system should be comprehensive, the

ontological description accurate, and the base of retrieval words complete to support

the fuzzy search. We need to invite interested readers to contact us about field-testing the

system with their students to develop wider inputs into further improvements. It is very

important to check out the system utility with an even wider set of potential users.

Fig. 12 Some examples of design solutions by the designers

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Conclusion

The establishment and application of a knowledge map for innovative product design

requires a correct description of the ontology of knowledge. In the follow-up research, we

need to complete the case base and retrieval thesaurus and build an intuitive and accurate

ontological base. At the same time, we should optimize the retrieval mechanism and

develop a knowledge index method for obtaining the implicit knowledge of professional

designers to make the knowledge acquisition more intelligent, efficient, and accurate.

Acknowledgments This research was supported by the National Natural Science Fund of China (No.51005203) and the Humanities and Social Science Planning Fund of the Ministry of Education of China(No. 11YJAZH110).

References

Chen, H. (2005). Product design. Wuhan: Huazhong University of Science and Technology press.Cynthia, J. A., Monica, E., Cardella, J. T., & Robin, A. (2005). Comparing freshman and senior engineering

design processes: An in-depth follow-up study. Design Studies, 26(4), 325–357.Doultsinou, A., Roy, R., & Baxter, D. (2009). Developing a service knowledge reuse framework for

engineering design. Journal of Engineering Design, 20(4), 389–411.Feng, Z., Li, W., & Li, X. (2007). The ontology engineering and its application. Beijing: Tsinghua Uni-

versity press.Howard, T. J., Culley, S. J., & Dekoninck, E. (2008). Describing the creative design process by the

integration of engineering design and cognitive psychology literature. Design Studies, 29(2), 160–180.Jong, W., Wu, C., & Li, M. (2011). Web-based navigating system for conceptual mould design with

knowledge management. International Journal of Production Research, 49(2), 553–567.Kruger, C., & Nigel, C. (2006). Solution driven versus problem driven design: Strategies and outcomes.

Design Studies, 27(5), 527–548.Liao, S. H. (2003). Knowledge management technologies and applications-literature review from

1995–2002. Expert System with Applications, 25(1), 155–164.Liu, X., Tan, R., & Yao, L. (2008). Product innovation conceptual design application research on integrated

process model. Chinese Journal of Mechanical Engineering, 44(9), 154–162.McLaren, S. V., & Stables, K. (2008). Exploring key discriminators of progression: Relationships between

attitude, meta-cognition and performance of novice designers at a time of transition. Design Studies,29(2), 181–201.

Owen, C. (2001). Structured planning in design: Information age tools for product development. DesignIssues, 17(1), 27–43.

Reed, N., McLeod, P., & Dienes, Z. (2010). Implicit knowledge and motor skill: What people who knowhow to catch don’t know. Consciousness and Cognition, 19(1), 63–76.

Sebastian, M., John, S., & Simon, A. (2001). Development and verification of a generic framework forconceptual design. Design Studies, 22(2), 169–191.

Tan, J., & Feng, Y. (2007). Design knowledge modeling, evolution and application [M]. Beijing: NationalDefense Industry Press.

Tomi, H. (2004). Reconfiguring knowledge management-combining intellectual capital, intangible assetsand knowledge creation. Journal of Knowledge Management, 8(2), 36–52.

Zdrahal, Z., Mulholland, P., & Valasek, M. (2007). Worlds and transformations: Supporting the sharing andreuse of engineering design knowledge. International Journal of Human-Computer Studies, 65(12),959–982.

Zhao, H., & Peng, R. (2004). Knowledge management—principles and best practice (2nd ed.). Beijing:Tsinghua University press.

Reusing design knowledge 1077

123