Tagging for intelligent processing of design information
Hideaki Takeda1, Yutaka Fujimoto2, Masaharu Yoshioka3, Yoshiki Shimomura2
Kengo Morimoto2, Wataru Oniki2
1 National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan

takeda@nii.ac.jp
2 Research into Artifacts, Center for Engineering (RACE), The University of Tokyo,

Komaba 4-6-1, Meguro-ku, Tokyo 153-8904, Japan{yutaka_f, simomura}@race.u-tokyo.ac.jp
3 Graduate School of Information Science and Technology, Hokkaido University
Kita 14 Nishi 9, Kita-ku, Sapporo, Hokkaido, 060-0814, Japan
yoshioka@ist.hokudai.ac.jp

Abstract. This paper describes how to add tags to extract knowledge frominformation for intelligent design support. Our project called Universal
Abduction Studio (UAS) aims to build a new design support system that
supports conceptual design by dynamically integrating various superficiallyirrelevant knowledge in different design domains. This paper focuses onknowledge description form which can be used to capture knowledge from textbased information and then be used for inference for creative design. We
propose so-called design knowledge document in which both human-readable
texts and machine-readable knowledge such as propositions and rules.

Introduction
Design support systems have been well developed for geometric and detail design
stages. In contrast, those in the conceptual design stage are still far from success. In
our opinion, the main difficulty comes from incomplete and insufficientunderstanding about design knowledge and its operations that play a crucial role inconceptual design. Recently, thanks to development of the Internet technologies, more
and more knowledge is accumulated and available electronically. It then becomes aninteresting research question how to apply such an enormous amount of diverse
knowledge to conceptual design.
Our project called Universal Abduction Studio (UAS)[1] aims to solve thisproblem in a unique way. The principle of UAS is abduction that leads design
processes by integrating knowledge from various domains. In order to apply
abduction, we need knowledge in logical or at least semi-logical form. This is themain topic of this paper.
In this paper, we first introduce UAS project briefly in Section 2. Then we discussknowledge representation suitable for our purpose in Section 3. After we investigation
on knowledge on a book in Section 4, we propose our knowledge representation
format in XML in Section 5. We conclude the paper in Section 6.

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Hideaki Takeda1, Yutaka Fujimoto2, Masaharu Yoshioka3, Yoshiki Shimomura2
Kengo Morimoto2, Wataru Oniki2

2.

Universal Abduction Studio (UAS)In this section, we explain ideas behind UAS project and basic concepts in it briefly.
More detail descriptions are found in [2].
2.1 Abduction in Design
The key issue to build a CAD system capable of supporting the early stages of design,in particular, its creativity aspects, is how to represent design processes. It is no doubtthat a creative design process is one of the most intellectual thought processes and isdifficult to model. This is not only because generating a creative product or idea itself
is hardly inimitable by computers, but also because the knowledge used for creative
design is generated, modified, and updated during the process. Once a designer
achieves a new creative design after struggles, s/he is able to perform similar designs
easily, which implies that her/his knowledge was expanded. We believe that the
expansion of knowledge is a mandatory nature of creative design. Creative design
therefore has two aspects, i.e., creating a new product and expanding knowledge, andthe co-relation between these two is common to various creative activities.
How can we, then, model creative design processes with these two aspects? We havediscussed formalization of design processes from the logical viewpoint [3][4][5]. In
short, our theory models design as iteration of deduction and abduction (see also
Coyne[6], Roozenburg and Eekels[7], while they did not offer any computing
mechanisms). In our theory, the core part of design, i.e., creating a new idea or thingcan be attributed to “abduction” while ensuring design to deduction. Abduction is thus
the crucial part in design.
What is abduction and what can abduction offer as reasoning? Abduction proposed by
C.S. Peirce is a logical process to find axiom from theorem [8]. The naïve
interpretation is that abduction is an opposite process of deduction. Although thisnaïve interpretation is somewhat popular within computer science [9], abduction
should be interpreted from wider viewpoints and therefore include more various typesof reasoning. Schurz [10] collected various types of abductive reasoning andcategorized them. In his work, abduction is firstly classified into three, i.e., factual
abduction (first-order existential abduction), law abduction, and second-order
existential abduction. However, since law abduction seems a sub-category of secondorder existential abduction, we regard that distinction of factual and law abduction is
the primary classification of abduction. The former concerns discovery of facts andthe latter concerns discovery of new laws. “Abduction as inversed deduction” ismerely one category of factual abduction.
As we mentioned above, the point of this paper is the dynamics of knowledge whenformalizing design processes. Factual abduction infers new facts from given facts(observable facts) with fixed theory (rules). However, as long as we use reasoning
with a fixed theory, the ability to create new facts is limited. In addition, although
factual abduction can satisfy the primary requirement of abduction (“finding axioms
from a theory”), this interpretation does not qualify another important feature of
abduction mentioned by Peirce. He explained that abduction can find a “surprising”

Tagging for intelligent processing of design information

3

Fig. 1. Fundamental concept of the Universal Abduction Studiofact. “Inversed deduction” is insufficient to realize such a process and abduction isnecessarily accompanied by expansion or revision of knowledge [11]. In the domainsuch as design in which rich knowledge is available, a feasible expansion ofknowledge is obtained by integrating existing knowledge [12]. Integration ofknowledge here does not mean a simple addition of knowledge, but rather suchoperations as translation and modification. There seem to exist a number of possible
ways to integrate knowledge. Abduction as a method to integrate knowledge cansatisfy the two aspects of creative design, i.e., creating a new product and expandingknowledge. Therefore, we believe that abduction can be one model of creative design
processes.

2.2 The architecture of Universal Abduction Studio Systems
A Universal Abduction Studio (UAS) system is a computer environment to support
integration of theories (that contain knowledge) from various knowledge domains forcreative design. UAS is not a design automation system but a cooperation system thatcan solve design problems by helping dynamic interaction between a designer and thesystem. UAS provides a toolbox consisting of a variety of domain knowledge as well
as a variety of abductive reasoning mechanisms for knowledge integration. When thedesigner cannot solve a design problem with knowledge of one domain, the designerchooses a knowledge operation to make correspondences between that domain
knowledge and another domain knowledge that the UAS system proposes. Then, thedesigner estimates and judges whether or not the proposed knowledge should be used.
Finally, the designer generates design solutions based on the tentative design
knowledge chosen by her / him. The basic feature of the system as an inference
system is abduction that can integrate knowledge to proceed design processes.

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Hideaki Takeda1, Yutaka Fujimoto2, Masaharu Yoshioka3, Yoshiki Shimomura2
Kengo Morimoto2, Wataru Oniki2

Figure 1 shows the fundamental concept of UAS. In Figure 1, the designer operates
design information and knowledge on the workspace. The knowledge integration
module consists of multiple abductive reasoning mechanisms, and the designerchooses one or some of them depending on each design problem. The knowledge baseconsists of multiple domain knowledge bases and the designer first chooses one tosolve a design problem. When the designer cannot solve the design problem, thesystem reasons about another domain knowledge base that can possibly be integrated
with the first domain knowledge. The abductive reasoning system then performs
knowledge integration. This fundamental concept requires unified knowledge
description among various domain knowledge bases.

3. Knowledge Representation in Design
In this section, we overview knowledge representation in design, and show our basic
approach for it.
Knowledge in design is mainly classified into two categories, i.e., knowledge on
objects and knowledge on design processes or design procedures [13]. The former isknowledge how objects are represented and operated, and the latter is knowledge how
designers proceed and complete design.
Many studies focus on object modeling. Typical examples of object modeling are
2D/3D geometric modeling and kinematic modeling. Each object modeling method
provides a way of representation based on its aspect. Since any design requires two or
more aspects to complete, we should manage multiple object modeling methods sothat ontology should be introduced.
Ontology in information systems is introduced in knowledge sharing context. The
popular definition of ontology is “an explicit specification of conceptualization”[14].
It provides basic concepts when one wants to represent the target world in somespecific context. Each modeling method assumes some basic concepts that are
introduced by the theory that the modeling is based on. These concepts can becomponents of ontology. Some of these concepts are sharable with other modelingmethods, and the others are not. Providing an ontology that consists of such sharable
concepts helps managing multiple modeling methods. More concrete discussions on
ontologies for engineering design are found in [15].
On the other hand, knowledge on design processes has not been investigated well.
In object representation, we can assume some background theory that the object
representation is based on. Then what kind of theory can we assume as backgroundtheory of design process modeling? We have proposed a logical framework for design
processes and shown abduction can be the principle for design process [3]. In thisframework, abduction corresponds to the process when a new design candidate is
created, while deduction corresponds to the process when it is analyzed and validated.
Abduction for design should not closed in a single domain or modeling but should
include knowledge from various domains and modeling methods.
Suppose that we are designing “knife which is always sharp”, while the following
information is provided in the ontology shown in Figure 2. Concepts from different

Tagging for intelligent processing of design information

5

domains and modeling methods are connected in this ontology. Glass and knife may
be included in an engineering domain knowledge, while chocolate in a cooking
domain knowledge. A possible scenario to design it is as follows. First we make anassumption like “if knife is cut, it is sharp” using the knowledge “if glass is cut, it issharp” and similarity between glass and knife which is come from relations in the
ontology. In like manner, we make an assumption like “if knife is grooved, it is easy
to cut” from the knowledge “if chocolate is grooved, it is easy to cut” and similarity
between knife and chocolate. The solution is then “grooved knife”. In this example,
two fragments of knowledge “if glass is cut, glass is sharp” and “if chocolate isgrooved, it is easy to cut” are jointly used in abduction by relating concepts indifferent domains and modeling methods with the ontology. It should be noted thateach modeling method is based on the specific theory so that ontology covered
completely can not expected. While ontology provides universally valid relationshipamong different modeling methods and domains, abduction is expected to support
teleological relationship among them as assumptions [12].
Ontology
plate-shape

glass

knife

solid

chocolateFig. 2. A sample of ontology for a scenario

4. A Case Study for Knowledge Representation for UAS
As we discussed in the previous section, we expect knowledge for UAS systems as
logical or at least semi-logical form because abduction we suppose requires suchforms. On the hand, most of knowledge in real design activities is represented in text
and figure. In order to know how and what knowledge can be captured from suchinformation sources, we picked up a book on know-how of mechanical design [16]and tried to extract knowledge. Then we analyzed the extracted knowledge.
We extracted pieces of texts that describe information on design processes ascandidates of knowledge from the book. The number of pieces extracted is 350. Then
we transformed these pieces of texts into if-then rules.
This transformation is not simple. We set a rough criterion to separate if-part andthen-part. If-part represents some observation, and then-part represents some action.
Even under this criterion, multiple interpretations are observed. For example, “cutglass becomes sharp” can be either interpreted as “if there is glass, cutting it makes it
sharp” or “if glass is cut, the glass becomes sharp”. The latter may seem more naturalinterpretation but it depends on situation. For example, when looking for informationof glass as candidate of material, the former rule may be useful.

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Hideaki Takeda1, Yutaka Fujimoto2, Masaharu Yoshioka3, Yoshiki Shimomura2
Kengo Morimoto2, Wataru Oniki2

The other issue is categorization. We investigated the collected rules closely andclassified into three. The first category is a collection of rules that have objects in ifpart. The rules are furthermore categorized into six sub-categories depending on thenpart. Then-part includes either “should”, “should not”, “is (are)”, is (are) not”, “there
are merits that”, and “there are demerits that”. The second category is those of which
if-then have operations to objects. This category is also divided into six sub-categories
depending on then-part. Then-part includes either “… is a good design”, “… is a baddesign”, and “… needs care”. The third category is those of which include state orsituation of object. It is also categorized into two sub-categories. One includes“should” in then-part and the other includes “should not”.
We can find some remarks on tagging for design knowledge through this casestudy. The first one is that texts have naturally multiple interpretations. Objects areeasily identifiable but fragments of knowledge like rules are not. We need thedifferent levels of flexibility for annotation. The second is variety of knowledge. Even
though we restricted our investigation to rule-style knowledge, meanings of rules arevarious. The variety probably comes from situations or contexts when we want to use
such knowledge. We listed fourteen categories but they are taken from a single bookand we should investigate such categories more systematically.
In the next section, we discuss format of tagging for knowledge. We mainly
concern the first point in this paper and let the second left for future research.

5. Design Knowledge Document and its Format
Knowledge for design, in particular, knowledge for design processes is often included
in documents written in natural languages. Forming knowledge bases by extracting
such knowledge from documents is a possible approach but it requires cost for<knowledge><head><!-- Properties of this documents are described. --></head><document><!-- Texts with annotations are described. --></document><model><propositions><!-- Facts corresponding annotations are declared. --></propositions><rules><!-- Relations among facts are declared. --></rules></model></knowledge>

Fig. 3. The abstract structure of design knowledge document

Tagging for intelligent processing of design information7

acquisition and maintenance of knowledge. The latter is especially serious because
this approach hardly enables to track changes of documents.
The approach in this paper is knowledge as annotation to texts [17]. We call design
knowledge document that contains texts and annotations to them. The former is justfor human and the latter is mainly for computers but still understandable for human.
The benefits of this approach are twofold. One is readability of knowledge. One caneasily understand meaning of knowledge since texts are used as comments forknowledge. This leads to productivity and ease of maintenance of knowledge. We canproduce knowledge from existing documents and update knowledge when thecorresponding documents are changed. The other is possibility of automatic extractionof knowledge. Because design knowledge documents can be seen as instances ofmapping between texts and knowledge, they can be sources to learn this mapping
function. We focus on the first point in this paper and let the second point left as a
future task.
Figure 3 shows the abstract structure of design knowledge document. The overall
structure is formatted as XML. It consists of three major parts, i.e., “head” part,“document” part and “model” part. “Head” part describes general properties of the
document. “Document” part describes natural language texts with the specificannotation. Without the annotation, they are just texts in documents. “Model” partdescribes knowledge related to these texts.
We provide “<word>” tag for text annotation. This tag relates the specific part oftexts to concepts in knowledge. The form is<word base=”fundamental-form” concept=”concept-name”
id=”ID”> string</word>
String is related to concept concept-name, word fundamental-form, and ID. Conceptname is associated with concept of this name. Concept is either declared in model partof the same document or some ontology. Fundamental-form is provided just for
natural language processing and search. ID is used in model part to refer the specificoccurrence of the concept. For example,<word concept=knife id=knife1>this knife</word>
declares that there is an occurrence of concept knife named knife1.
Model part consists of two parts, i.e., proposition part and rule part. In proposition
part, facts that are believed true in the document are listed. For example,<proposition id="p1"><predicate concept="cut"/><arg idref="knife1"/></proposition><proposition> tag declares a proposition and should include a single <predicate>tag and one or more <arg> tags. Attribute concept for <predicate> and <arg> tags isused to specify the corresponding concept, while attribute idref is used to occurrenceof the concept in the document. The example declares

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Hideaki Takeda1, Yutaka Fujimoto2, Masaharu Yoshioka3, Yoshiki Shimomura2
Kengo Morimoto2, Wataru Oniki2cut(knife1)
where knife1 is the occurrence of knife in the document..
Rule part is used to declare rule-style knowledge. The example is as follows;<rule><if><atom propositionid="p1"/></if><then><proposition><predicate concept=”sharp”/><arg refid=”knife1”></proposition></then></rule>This description is a simple if-then rule. <atom> tag is used to specify aproposition declared in proposition part with propositionid attribute. This example isthen declaration of the following rule.
If cut(knife1) then sharp(knife1)
Figure 4 shows an example of description with this syntax.
By providing design knowledge documents, human can understand and use these
documents as usual on one hand, the system can extract their knowledge part and use
them in its inference. An example of inference is shown in [1].

6. Summary
In this paper, we discussed knowledge representation in design with a case study andproposed so-called design knowledge document that has an XML-based format
including both human-readable texts and computer-understandable knowledge.
There are many issues to be done. The current format for design knowledge
document is tentative to need more discussion. Especially Semantic Web approach[18] rather than XML is more suitable for our purpose. We are going to re-write ourformat to RDF/RDFS/OWL and rule MLs like SWRL1.

References[1] Takeda, H., Sakai, H., Nomaguchi, Y., Yoshioka, M., Shimomura, Y., Tomiyama, T.:
Universal abduction studio – proposal of a design support envrionment for creative thinking
in design –. In Folkman, A., Gralen, K., Norell, M., Sellgren, U., eds.: The FourteenthInternational Conference on Engineering Design (ICED 03), Stockholm (2003)[2] Takeda, H., Yoshioka, M., Tomiyama, T.: A general framework for modeling of synthesis –integration of theories of synthesis –. In: 13th International Conference on Engineering
1 http://www.daml.org/2003/11/swrl/

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Design – ICED 01, Design Research – Theories, Methodologies, and Product Modelling,
Glasgow (2001) 307–314[3] Takeda, H., Tomiyama, T., Yoshikawa, H.: A Logical and Computerable framework forreasoning in design, in D. Taylor and L. Stauffer eds., Design Theory and Methodology -DTM '92 --, pp. 167–174, The American Society of Mechanical Engineers (ASME) (1992).
[4] Takeda, H., Veerkamp, P., Tomiyama, T., Yoshikawa, H.: Modeling design processes, AI
Magazine, Vol. 11, No. 4, pp. 37–48 (1990).
[5] Hayashi, K., Takeda, H., Tomiyama, T., Yoshikawa, H.: Analysis and logical formalizationof design processes (the third report) -- modeling with circumscription and abduction --, The
proceedings of the annual conference of the Japanese Society for Precision Engineering,pp.7-8 (1989) (In Japanese).
[6] Coyne, R.: Logic Models of Design. Pitman Publishing, London (1988)[7] Roozenburg, N., Eekels, J.: Product Design: Fundamentals and Methods. John Wiley &Sons, Chichester, MA. (1995)[8] Peirce, C.: Collected Papers of Charles Sanders Peirce. Volume 5. Harvard University
Press, Cambridge, MA (1935)[9] Flach, P., Kakas, A., eds.: Abductive and Inductive Reasoning: Essays on their Relation andIntegration. Applied Logic Series. Kluwer Academic Press (2000)[10] Schurz, G.: Models of abductive reasoning. In Schurz, G., Werning, M., eds.: TPD
Preprints Annual. Number 1 in Philosophical Prepublication Series of the Chair ofTheoretical Philosophy. The University of Dusseldorf (2002)[11] Aliseda, A.: Abduction as epistemic change: A peircean model in artificial intelligence. In
Flach, P., Kakas, A., eds.: Abductive and Inductive Reasoning: Essays on their Relation andIntegration. Applied Logic Series. Kluwer Academic Press (2000)[12] Takeda, H.: Abduction for design. In Gero, J., Sudweeks, F., eds.: Proceedings of the IFIP
WG5.2 International Workshop on Formal Design Method for CAD, Tallinn, Elsevier
Science Publishers B.V. (1993)[13] Tomiyama, T.: From general design theory to knowledge-intensive engineering. Artificial
Intelligence for Engineering Design, Analysis and Manufacturing (AIEDAM) 8 (1994)
319–333[14] Gruber, T.R.: Toward principles for the design of ontologies used for knowledge sharing.
Technical Report KSL 93-4, Knowledge Systems Laboratory, Stanford University (1993)[15] Yoshioka, M., Umeda, Y., TakedaA, H., Shimomura, Y., Nomaguchi, Y., Tomiyama, T.:
Physical concept ontology for the knowledge intensive engineering framework. (Advanced
Engineering Informatics) (Accepted for publication).
[16] Watanabe, H.: Hits for mechanical design, A second series. Nikkan Kogyo Shinbun (1998)(In Japanese).
[17] Yoshioka, M. and Shamoto, Y.: Knowledge Management System for Problem Solving -Integration of Document Information and Formalized Knowledge --. Proceedings of the2003 ASME Design Engineering Technical Conference \& Computers and Information in
Engineering Conference, The American Society of Mechanical Engineers (ASME), New
York, DETC2003/CIE-48217 (CD-ROM), (2003)[18] Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Scientific American (2001)

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Hideaki Takeda1, Yutaka Fujimoto2, Masaharu Yoshioka3, Yoshiki Shimomura2
Kengo Morimoto2, Wataru Oniki2<?xml version="1.0" encoding="UTF-8" ?><!DOCTYPE knowledge (View Source for full
doctype...)>- <knowledge
xmlns:dc="http://purl.org/dc/elements/1.1/"
xmlns:fc="http://www.race.u-tokyo.ac.jp/fc/"
xmlns:rl="http://www.race.u-tokyo.ac.jp/rl/">- <head><knowledgeid>003</knowledgeid><title>Knowledge on machining setup</title><creator /><date>2004-08-13</date><version>1.0</version>- <origin><dc:title>Hits for mechanical design,
A second series</dc:title><dc:creator>Hidenori Watanabe</dc:creator><dc:publisher>Nikkan Kogo Shinbunsha</dc:publisher><dc:date>1990-08-29</dc:date></origin></head>- <document><word base="fewer"
concept="predicate:fewer">Fewer</word><word base="number" concept="upper:number">numbers</word> of<word base="setup" concept="upper:setup"
id="setup">
setup</word> and<word base="change" concept="predicate:change"
id="change">changes</word> of<word base="position"
concept="upper:machiing-position"</word>
id="position">position</word> and<word base="attitude" concept="upper:attitude"
id="attitude">attitude</word> in<word base="machining" concept="upper:machining"
id="machining">machining</word><word base="cause" concept="predicate:cause"
id="cause">cause</word><word base="decrease" concept="predicate:decrease"
id="decrease1">decrease</word> of<word base="man-hour"concept="upper:time,engineering:man-hour"
id="man-hour">man-hour</word><word base="cost-reduction" concept="upper:cost,engineering:cost-reduction" id="cost-reduction">cost reduction</word></document>- <model>- <propositions>- <proposition id="p1"><predicate
concept="upper:machining" /><arg idref="machining" /></proposition>- <proposition id="p2"><predicate
concept="upper:setup" /><arg idref="setup" /></proposition>- <proposition id="p3"><predicate
concept="predicate:own" /><arg idref="machining" /><arg idref="setup" /></proposition>...(several lines are omitted)</propositions>- <rl:rules>- <rl:rule>- <rl:if>- <rl:and><rl:atom propositionid="p1" /><rl:atom propositionid="p2" /><rl:atom propositionid="p3" />- <rl:not><rl:atom propositionid="p4" /></rl:not><rl:atom propositionid="p5" />- <rl:not><rl:atom propositionid="p6" /></rl:not><rl:atom propositionid="p7" />- <rl:not><rl:atom propositionid="p8" /></rl:not></rl:and></rl:if>- <rl:then>- <rl:and><rl:atom propositionid="p9" /><rl:atom propositionid="p10" /><rl:atom propositionid="p11" /><rl:atom propositionid="p12" /><rl:atom propositionid="p13" /><rl:atom propositionid="p14" /></rl:and></rl:then></rl:rule>...(several lines are omitted)</rules></model></knowledge>

Fig.4 An example of design knowledge document (translated fromdescriptions in Japanese)