Final Paper Submission for PCIS 2002

A Design of Ontologies-Based Information Interoperability for

Garment Quick Response Network

Shi Yu1 Jiaxun Chen2 Fangfang Xin3

1Laboratory of Network & Database, Computer Science Department 2Digitized Textile & Garment Engineering and Technology

Research Center of National Ministry of Education 3Fashion & Art College

Donghua University, Post Code 200051 Shanghai, China

Designated Author for Contact

Shi Yu (Mr.) Donghua Univeristy (Former China Textile University)

Computer Science Department No.1882 West Yan’an Road

Shanghai, China Post Code: 200051

Campus Mail Box: 340#, Donghua University Phone: 86 - 21- 62378397 Fax: 86 - 21- 62379643

E-mail: slash_yu@hotmail.com

A Design of Ontologies-Based Information Interoperability for Garment Quick Response Network

Shi Yu1 Jiaxun Chen2 Fangfang Xin3

1Laboratory of Network & Database, Computer Science Department

2Digitized Textile & Garment Engineering and Technology Research Center of National Ministry of Education

3Fashion & Art College Donghua University, Post Code 200051

Shanghai, China

Abstract Garment Quick Response Network highly demand efficient and automatic Information Interoperability to bridge heterogeneous applications at various domains into an integrative system. However, the natural characteristics of garment industries impede the communication to be established on traditional techniques such as EDI, XML, etc. Noticing that common knowledge is a solid foundation for information to interoperate but requires more delicate mechanism to bring it into realization, this paper presents a design to introduce Ontology into garment industry information systems and figures out a general framework of an Ontology-based intelligent Information Cooperating Systems. The focus of this paper is not the implementation detail of Ontological Language, Description Logic or Reasoning Method, instead, it concentrate on the general mechanism, formalization logic and Mediator Model of Information Interoperability in an engineering view. Though Ontology has been widely applied in knowledge engineering systems of GIS Platforms, Medical Information Systems and Workflow Management Systems, it is still novel for it to be discussed in conducting Information Interoperability with Garment Industries background.

Keywords Ontology, Garment Quick Response Network, Information Interoperability, Knowledge Representation, Intelligent Information Systems.

1. Introduction

The development of Garment Quick Response System (GQRS) has been significantly facilitated by Internet and has beyond its original expectations when first introduced in late 1970s. Equipped with digital and networked information technologies, an emergence of Garment Quick Response Network (GQRN) which integrates Pre-market Testing, Design, Manufacturing & Sales processes across distributed organizations and enterprises into a fast-response supply chain. Affirmatively, it will bring more efficiency into garment industries. However, this emergence is impeded by the bottleneck of information interoperability among distributed and heterogeneous information systems.

It is widely accepted that the speed of Information flow is a key factor in accelerating supply chain, hence many related works have concentrated on speeding the supply chain cycle and their approaches could be briefly concluded as the following four categories:

Resource Modeling.

In development of Multi-Agent Systems (MAS) for decentralized communication of systems, Agents demands of a well-structured workspace to query for knowledge or information. Hence, various Modeling approach have focused on Resource Contention [Goh et al. 1999], Supply Chain Formation [Yen Chen et al. 1999][William E. Walsh et al. 1999], Workflow Mediation [Gail et al. 1999][Merz et al. 1996]and Enterprise Modeling [Mike et al. 1996][Cui et al. 2000][Guido L Geerts. et al. 1999]. Expressive Messaging.

Unlike modeling approaches, some researchers have engaged in enriching the expressive ability of exchanged message among functional business tools. For example, XLBC [Weigand et al. 1999] establishes an extensible language for business communication. AFRICA [Muehlen et al. 2000] improves virtual supply chains communication by workflow interoperability based on XML-messages. Tripathi [A.R.Tripathi et al. 2000] implements distributed workflow systems from XML specifications. Workflow Decomposition and Semantic Operational.

This kind of research tries to analyze functional processes such as groupware, workflow, etc. in a conceptualized way and their central objective is to provide a cooperative transaction model [K.Aberer et al. 1996][Casati et al. 1995] such as workflow patterns [Sadiq et al. 1999], adaptive processing units [Koksal et al. 1998], independent components, etc. which supports cooperative functions among HADs’. Interpreted Knowledge Based Mediation

A synthesis of above three ideas attempts to solve the problem in a more intelligent way, that is, to create Interpreted Languages (XML, DTD, Ontology) as Mediation for different partners to exchange semantic information, which covers a broad range of aspects (for example, resource content, exchanging data, workflow routing, etc.) from information systems. Knowledge-based approaches are often featured with a coordinate mechanism, which provided a parallel knowledge system independent but consistent with operational system. More related experiences could be referenced in [Heiner et al. 2000] [Yan et al. 1997], TSIMMIS [Chawathe et al. 1994], DIOM [Liu et al. 1996], OBSERVER [Mena et al. 2000], Carnot [Collet et al. 1991] and InfoSleuth [Bayardo et al. 1997].

With respect to these related works, this paper focuses on a typical supply chain environment – GQRN and attempts to make improvement in the following points:

Domain-Oriented Declarative Description of product As the key component of information, product description in GQRN has a broad variety of categories and quantities based on different standards. It brings to difficulties for product information expressed in these proprietary standards to perform electrical data exchange. To address this problem, we take out a plan to implement representative knowledge into product description on the basis of the common understanding of design, manufacturing and marketing organizations of Garment Industry. Support Abstract, Fuzzy Semantic, Multimedia and legacy Information

Skirt Pre-markettesting

Typical style

capacity

Customer

Finish

Process flow

Design stage

Object oriented

Effect draw

Measurementchart

Productpositioning

Process design

Manufacturestage

Materialpreparatory

Samplegarment

Preparatoryfinish

Preproduction

Storage

mart

E-Busi

Renewal cycle

Tax rat

Brand level

Profits

Single Price

Multimedia

Advt.

Design &Manufacturing

Package

Cost control

Lot productDealer Productive

Sales

Material

Fashioninformation

Fashion andfabric direction

Figure 1. Mixed Conceptualization of an Object in GQRN

GQRN integrates cross-discipline knowledge of Art, Material, Mechanical, Information and Marketing into a complicate environment and, moreover, garment is an object that strongly influenced by social, economic and culture movement. Information exchanged in GQRN may cover various data models such as fuzzy description of Style and Fashion Tide. Furthermore, existing industry standards and the use of CAD/CAM software also produces a large number of legacy data models into the exchanging environment. Correspondingly, information interoperability of GQRN requires an open, flexible and intelligent mechanism to deal with diversity of Information models. Machine interpretable format of Information Exchanging among HADS Garment is a time-sensitive industry that has strict requirement in Supply Chain Efficiency. Since Information systems in GQRN are distributed and heterogeneous both in software technology and application domain, it requires machine-understandable format of information for interoperating that could be immediately apply into domain-specific applications without human intervention.

This paper introduces an Ontology-Based Information Interoperability Mechanism into GQRN. Although Ontology has been successfully implemented in research of medical record Information systems [7] & Urban GIS systems [5] and is a hot spot in academic research of Database, AI and Distribute Processing [18], this paper makes a dissimilar approach to apply this edged technology into a rich profitable market of garment industries.

The organization of this paper is as follows. A brief description of related work and introduction to our approach has already been presented in Section 1. In Section 2 we analyze the motivation for using ontologies and describe various ontologies used in GQRN. In Section 4 we discuss a logical formalization of Ontologies-based Information Interoperability and exemplified with a simple case in practical supply chain process. In Section 5 we give out a

Layered Interpreted Knowledge Model on this basis of discussions in Section 2 and Section 3. In Section 6 we illustrate a system-level process of Information Exchanged among multi-ontologies. Finally, conclusions and future works expectation are discussed in Section 7.

2. Overview of GQRN 2.1 A Complicate System with Mixed Conceptualizations

Figure 1 presents mixed conceptualizations of a Product object in GQRN. These conceptualizations are respectfully related to certain applications and representing content of domain-interested knowledge. They could be disjoint, which means the object in described from different aspects, and also overloaded, which indicates that a same attribute may be described as different concepts in various domains. For instance, when a pre-marketing group are concerning about a new “skirt” product, they are probably interested in concepts as “material”, “fabric”, “Customer Group” and so on. These concepts are marketing-view of a product. When the pre-marketing group transfer the survey results to the next unit of GQRN – Designer, the designer will no longer consider the product as a pre-marketing-way, instead, will focus on concepts such as “Effect Draw”, “Measurement Chart”, etc., which composed as a designer-view of a skirt. Similarly, when the product is considered by different application domains of GQRN, it brings dissimilar conceptualizations and when product information is exchanged, problems will occur. Although descriptions expressed in different conceptualizations are based on same objective product, natural affiliations have lost. Take a sequential supply chain for instance:

Sequenced (A,B,C) =Partner A → Partner B → Partner C Assume information are transferred from A to B then finally to C, for Partner A, it must

know the conceptualizations both of A and B because it is responsible for A to grant that B has understood the information, if not, A must explain it to B. Similarly, B must know conceptualizations of B and C. Then let’s see what happens when information interoperating in (Figure 2): Conceptualizations of A & B, B & C are directly affiliated and we can use mappings to solve the problem of interoperability, but when the relations of A, B, C transfer from a sequenced model to a networked model: Sequenced (A,B,C) → Networked (A,B,C). It requires adding additional affiliation between A & C which was not directly linked before. And if the same thing happened in a six- partner case, we must deal with 15 affiliations in order to

Partner A Partner B Partner C

?

affiliated affiliated

not directly affiliated

A B

C

DE

F

Conceptualizations in a Three- Partner Case Conceptualizations in a Six-Partner Case

A B

DE

FCK

Conceptualizations in a Knowledge Mediation Case

Object

Onto logyOntology

OntologyOntology

Ontology Ontology

Ontologies based Knowledge of Object

Figure 2. From Data Format Change to Knowledge Mediation: Complexity comparison

make them interoperable. So under this condition, mediated knowledge is a good solution to solve the interoperability problem for we could reduce to only 6 affiliations for six partners and for any partner could establish affiliation through mediated knowledge with at most single route in semantic translation. 2.2 Basic Rational Of Ontologies

Follow this principle, we could conclude: eptualization of an object.

2) direction. 3) We

4) etween

.3 Multi Ontologies Features

We can derive at least four ontologies from example in Figure 1: Pre-Market Testing Ont

Information Type Samples

1) Ontology is a domain-interested concConcept is a projection of realistic object in a certain attributiveWhile an object is projected in different directions, it brings to different concepts.consider knowledge contents in different application domains as different attributive directions and thus lead to Multi Ontologies based on an Explicit Object. Moreover, we consider a pair of Ontologies as a well-defined Affiliation btwo conceptualizations.

2

ology, Design & Manufacturing Ontology and Sales Ontology. The items listed in conceptualizations could be treated as concepts and attributes of Ontology. Even within single domain, the constitution of ontology is not pure. Garment industries cover diversity of information types and they are concluded as six main categories (Table 1).

General & Abstract Declarative formation, Typical Style Fashion InMultimedia Data Pictures, Videos in Try-On Systems Fuzzy & Uncertain Materiality of Fabric In existing Industry Standards or are produced Legacy System Formats

CAD/CAM softwInformation

Time-sensitive Data Renewal Cycle Relational Data Price, Tax rate

Table 1 Diversity of Information Types in GQRN Ontologies

While building ontologies for different conceptualizations, the hierarchical structure, des

n “Case-Based Reasoning” to speculate the

m is responsible to derive a concrete

cription logic, association rule and decision method are strongly dominated by the knowledge content within application domains.

Pre-marketing systems depend mostly omost profitable type of product according to historical records and experience. Correspondingly, pre-marketing ontologies must support general & uncertain decision through abstract knowledge and experience. Design. In contrast, for designer-aided systemodel on the basis of marketing research. This part of ontologies, therefore, should be able to transfer general ideas into concrete design elements such color, material fabric, measurement, pattern plan, effect draw, etc. According to general and abstract requirements, Design Ontology should store a large repository of design information

to support Design Knowledge Mining. Manufacturing. Knowledge in Manu facturing period includes vast quantities of

ass of context interchange of

. A Logical Formalization of Information Interoperation

.1 Logical Formalization

roposition Information of a product is to be exchanged among different application

efinition 1 Consider structure where R is a set of relevant relations and let

Definition 2 For all the domain-interested knowledge within domain N can be expressed as

where is called the knowledge eigenvector of domain N.

efinition 3 We expect an interpreted knowledge model to express interoperated

Existing Standards and Legacy System Formats. CAD/CAM software, Integrated Patterning Machine and Automated Control System are widely equipped and information exchanged in this environment is mostly well-structured and machine-understandable, therefore, we could directly reference those information models in the definition of Manufacturing Ontologies. Sales. Sales Ontology should capable to deal with a mproduct catalogues, orders, customer queries. In one hand, it is quickly changeable with the pace of fashion and should adapt with extensible information structure and dynamic transactions. On the other hand, it not only serves for communicators in supply chain but also for customers. Hence natural language query process ability should also be considered while building Sales Ontologies.

3 3 P Object

) . domains: (,),(),( NdomainBdomainAdomain Κ

D >< Rdomain,C = >< RWdomain ,, be a conceptualization where W represents a given description of

O logical commitment of domain N could be expressed as )}(|),{( NdomainWCW ∈∀ .

the bject . The ontoON =

NK

NNN OEWK ×= ),( NE

D MKinformation which defined as

£©£¨ NBAM EEEMK ,,, Κ×= , where M is called the Weight Operator which satisfies

heorem Following the principles defined above, we could safely deduct that the

nnbann OEEEMWKK ××=∀ )],,,(,[| Κ .

Tinteroperated parameter of information is a twotuple >< MW , , obviously, it is independent to conceptualizations of domain applications. 3.2 Explanation with Examples

: inspiration? (milky) a: colour?

nd (delicacy) and (mature)

(ice-purple) and (powder-green) and ( light-blue) shallow-powder) and (

: Fuzzy Description? vacillate between transparency with

opacity : fabric? (Fine count Wool & silk knitted

fabric) and (LYOCELL cotton & silk synthetic fab

: Feeling? ric)

(excellent quality) and (choiceness techniques)and

ate neatness appearance) (Delic: style? (asymmetry collar) and (fitted

midriff skirt)

We present a practical but simple exampleillustrates two clips of information to be interoptransfer from Pre-Marketing domain to Design Manufacturing. Assuming these two informaautomatically and defining them as �A stis for Design domain. Ontologies of these domainsoutput with a certain input of descriptions and attri

BA KK &

=AE (inspiration, colour, fabric, style) =W [(Milky & Mature),(Powder-Green),(Fine

then . AAA OWEK ××′=

For the sake of simpleness, we use single-dimensiois a vector space. The process of a mining knowledreckoned as locating the vector elements within theelements to form a semantic structure according toget

=BE (Name, Fabric, Measurement, Effect Draw=W [Skirt, Lyocell Cotton, (86,70,90,100,23,36

then BBB OWEK ××′=

Follow these principle, we attempt to expresdescription W. identify the important semantthe concrete value. For a specific application domcompound input ( , ) to ontology . Howcontains varies concepts, so we model

NE

NE W NO

£©£¨ NM EMK ×= BEAE , ,,Κ which collects all tWeight Operator which determines which eigenvalunder conditions. In this example, if we define the

MK =<Name, inspiration, colour, fabric, style, mea

:Name ? kirt S:Fabric ? ll Cotton Lyoce:Measurement ? : Brest Measure? : value? 86 : unit? cm : Waist Measure? 70 : value? : unit? cm : Hip Measure? ? 90 : value : unit? cm : Hem Width? ? 100 : value : unit? cm : Collar Depth? : value? 23 cm : Collar Width?

: unit? : value? 36 cm : Effect Draw?

: unit? skirt.gif

Figure 3. A practical Example of Information Exchanged in Application Domains ofGQRN

to interpret the formalization. Figure 3 erated in GQRN. Graph A is Information Domain and graph B is from Design to tion could be interpreted by machine ands for the Pre-Marketing domain while B ( O ) must support a knowledge mining butes. In this example, suppose

BA O,

Count Wool),(Fitted Midriff Skirt)]

n vectors to represent & W and Ontology ge in a domain-oriented ontology could be domain space, and retrieve all the relevant certain similarities. Similarly, we can also

AE

) ,cm),(http://www.vdesign.com/skirt.gif)]

s information as eigenvector and a ic concepts of vector space while W indicate ain, knowledge could be mined through a ever, is still domain-related and also expect a mediator knowledge

NE

NE

he eigenvalues of multi-ontologies. M is a ues are to be inputted to domain ontologies mediator knowledge model as surement, stylized sketch>

then M in Pre-Marketing Domain and Design Domain is =M [0,1,1,1,1,0,0], � AMA OMKK ××′=

=M [1,0,0,1,0,1,1], � BMB OMKK ××′=

Consequently, for information interoperated in these domains could be encoded as domain independent formats as: Encoded Information to Pre-Marketing Domain: {<(Milky & Mature),(Powder-Green),(Fine Count Wool),(Fitted Midriff Skirt)>,[0,1,1,1,1,0,0]} Encoded Information to Design Domain: {<Skirt,Lyocell Cotton, (86,70,90,100,23,36,cm),(http://www.vdesign.com/skirt.gif)>,[1,0,0,1,0,1,1]} Though perspicuous in the given example above, Ontologies-Based Information Interoperability is perplexing in practical implementation. Firstly, not all the descriptions and eigenvalues could be optimized as single-dimension vectors, the semantic hierarchy sometimes is very complicated and nonlinear. Secondly, it is hard for all the domains to agree on a taxonomization and terminology of an object into represented concepts. For instance, “effect draw” is also named “Stylized Sketch” in some cases. Hence the mediator knowledge model must be capable to handle with synonym semantic relation; otherwise the model will turn to be a mass collection of verbose and imprecise terminologies.

4. Structure of Interpreted Knowledge Model

An intelligent system such as GQRN requires a rich model of knowledge about a subject domain in order to be useful. In our approach, Interpreted Knowledge Model (IKM) provides an explicit framework for information representation. While exchanged among different application domains, the well-structured model could make knowledge easily retrieved by and reasoned by communicators. Noticing in previous sections we have discussed that GQRN is a complicate knowledge environment with mixed conceptualizations and rich knowledge resources, the interpreted model should also support diversity of data models and formats. To allow the efficient compilation of a large amount of knowledge, a language is also needed with semantic constraints and syntactic tools to help in the efficient capture of the structure of the knowledge. In this section, we are going to introduce a multi-layered architecture for Interpreted Knowledge Model. 4.1 Common Vocabulary Layer with Rough Boundaries

Though common knowledge representation formalism goes a long way towards enabling

sharing on the basis of agreed vocabularies, it is rather unpractical for all the communicators within GQRN to agree on same terminologies, say the “Effect draw - Stylized Sketch” case. We believe that vocabulary repositories should support intelligent and uncertain semantic deduction based on theories such as Fuzzy Logic, Rough Sets, Approximate Reasoning, etc, which is named a Common Vocabulary Layer with Rough Boundaries. On this foundation, basic relationships in this layer are determined by three approximative operators: when ( ), where ( ) and what extent ( ). For example, the Similarity relationship between Concept “Effect draw” and “Stylized Sketch” could be expressed as:

Xaprwn Xaprwe

aWXaprwx

bW1 X]aprX,aprX,apr),W,[(WSimilartiy0 wxwewnba ≤≤ .

Therefore, we could integrate terminologies from different domains on algebra level and

determine their relationships flexibly and conditionally. Rough-boundary vocabularies do not mandatory demand of different communicators

agreeing on explicit terminologies, moreover, it is also an important mediator for various terminologies to be translated to the specific description language of Interpreted Knowledge Model because the inner structural definitions of Knowledge is required certain and explicit.

4.2 Service Abstract Layer of Application Domains

The role of Service Abstract Layer (SAL) in IKM could be assimilated to Hardware Abstract Layer (HAL) in Operating Systems Software. SAL registers the identifications of target application domains as Domain Recognition Code and store domain characteristic descriptions as buffer fragment of IKM. Each fragment describes service oriented information about the target application domain for ontology matching process while interoperating. Most commonly used functions and queries could be also collected in this layer for the sake of convenient and fast matching of ontology-based knowledge mining. Another particular function of SAL is to determine the Weight Operator M of IKM. M indicates the domain-relevant combination of particular eigenvalues of . This combination is input parameters to onotolgies for domain-relevant knowledge. As we exemplified in section 3 that M varied its value in different target domain, moreover, in a same domain under different conditions. Hence SAL is responsible to generate M from stored models or based on statistical results of vocabulary similarities.

MK

Common Vocabulary Layerwith Rough Boundary

Service Abstract Layer ofApplication Domains

Domain A Domain B Domain N…………..

Main Knowledge Layer

Data and Formats Layer

M

Figure 4. Layered Structure of Interpreted Knowledge Model

4.3 Main Knowledge Layer

Main knowledge Layer (MKL) combines all the relevant eigenvalues in multi-ontologies within GQRN. It provides a unified model for all the interoperating information to be represented across different domains. The complexity of MKL is dominated by the knowledge’s semantic granularity and it is defined by specific description language. MKL adopts sectioned structure and extensible hierarchy (Figure 4) to support as board range of knowledge as possible. Many previous researches have concerned on creating ontological description languages such as KIF, Ontolingua, SHOE, OIL and so on. However, we do not designate a concrete language in our approach because they have diversified expressive

powers, knowledge structures and reasoning abilities so we believe provide being appropriate, any of them could be adopted by Intelligent Information System of GQRN. 4.4 Layer of Data Types and Formats

To address the problem of vast quantities of existing information standards and legacy data formats, the lowest layered of IKM is defined as Data Layers. Data Layer stores data structures, rules, multimedia files & annotations of existing standards and definitions of legacy system format that defined in MKL Layer. This layer could be federated or, alternatively, established and administrated by each ontological domain. In this way, new concepts and changes at data level could be restricted in this layer that no modification is required in higher layers. 5. Illustration of Implementation

O ntological Definitionsof Marketing

Encoded twotuple (W,M)

Ontological Definitionsof Design & ManufacturiOntological Definitions

of Design & Manufacturing Object

MarketingSystems

Design & ManufacturingSystems

$ $$

Sales Systems

Compiling

InterpretedKnowledge

RuleEngine

RuleEngine

RuleEngine

compiled

Encoded twotuple (W,M)

conceptualization

W,M

KMKnowledge

MiningDomain-Interested

Knowledge

Figure 5. Ontology-Based Information Interoperability in GQRN

To make information inter-operate, each domain should create a rule engine coordinate

with domain ontology (Figure 5). While encoded information is exchanging among communicators, rule engines are responsible to perform knowledge mining. It uses the Weight Operator M to compare with and then calculates the local domain eigenvalues of the incoming information. Finally, the rule engine mines local ontology with process

and retrieve the domain-interested knowledge . In a compare view, this process resembles the mechanism of EDI: Knowledge here plays the role as flat file and data mapping function is replaced by an ontological knowledge mining and machine-deductive processing.

),( MWN

NK

MK NE

),( NN EWMining

ng

6. Conclusions and Further work This paper has described an approach of composing multiple Domain Ontologies to support

information interoperation. The author believed it is a good start to apply ontology in manipulating semantic-based information exchange in mixed conceptualizations environment of different application domains in GQRN. However, this paper does not focus on the concrete methodologies in building ontologies, but on how to establish reasonable mechanisms to enable information interoperability. GQRN is a typical environment with mixed conceptualizations that traditional data interchange methods could hardly solve the problem. Therefore, an ontological domain concept modeling has been introduced and the feasibility of exchanging information based on interpreted knowledge is also discussed. Considering a Domain Ontology as a conceptualization space and knowledge within it as a combination of descriptions and eigenvalues, the interoperability of knowledge depends on the interpreted model and a domain-determined Weight Operator. This paper briefly explained the logical formalization of this process and suggested an Interpreted Knowledge Model featured with layered structure.

This paper has tried to address the problem of information interoperability in an intelligent way. However, as a delicate software engineering process and complicated knowledge modeling work in both, further efforts are required in the following aspects:

Constraints and Syntactic Tools support Machine Deductive in Interpreted Knowledge Model.

To develop a prototype system for further researches and analyses in Decision rule, Rough Clustering, Deductive Function, Reasoning and Reliability Validation of Ontologies-based Information Interoperability.

7. Acknowledgements

The authors are grateful to the Education of Ministry P.R.C. for their financial support of this work.

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