Using IDEF0-Petri Net for Ontology-Based Task Knowledge Analysis

8/3/2019 Using IDEF0-Petri Net for Ontology-Based Task Knowledge Analysis

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Using IDEF0/Petri net for Ontology-Based Task Knowledge

AnalysisThe Case of Emergency Response for Debris-Flow

Wen-Yu Liu, Kwoting Fang,

Department of Information ManagementNational Yunlin University of Science & Technology, R.O.C

[email protected]

Abstract

In terms of the progress of the industry, the

velocity and dynamic nature of the globalenvironment, has caused serious damage anddiminished our earths resources. A lot of effort

has been spent on global sustainable developmentaround the world, however, from a practical

standpoint, the Incident or Hazard Command

System (IHCS), especially emergency response forDebris-flow, is scare in Taiwan.

The purpose of this study was twofold. First, it

adopted the theory of task ontology to build up a

three-level mediating representation for a task

analysis, and the task of emergency response for Debris-flow served as an example. Second, from

the modeling standpoint, a methodology, calledTTIPP, was provided to systemically analyze the

process of the task and subtask in terms of the inputs,

outputs, mechanisms, and controls, using IDEF0

and Petri net.

Keywords: Task Ontology, IDEF0/Petri net,Debris-Flow

1. IntroductionIn the past decade, there were many

massive-scale earthquakes that shocked the world,

such as the earthquake that shook Northridge,

United States in 1994 and the great earthquake of

Hanshin-Awaji, Japan in 1995. In 1999, the 9-21

Earthquake hit Chi-chi, Taiwan and another one hit

Izmit and Istanbul , Turkey. In 2001, a quakeshook BHUJ, India. Then in 2003, a series of

earthquakes shook the world, hitting Altay, Russia;

Boumerdes, Algeria; Hokkaido, Japan; Bam, Iran;

and at the end of December 2004, Southern Asia.These natural calamities caused tragic loss of life,severe property damage, and a resultant decline in

living standards, retarding regional and national

prosperity.

In terms of the progress of the industry, the

velocity and dynamic nature of the global

environment has caused serious damage and

exhausted our earths resources. A lot of effort has been spent on global sustainable development

around the world; however, from a practical

standpoint, the Incident or Hazard Command

System (IHCS), especially emergency response for

Debris-flow, is scare in Taiwan. Taiwan lies on thetyphoon and earthquake belts, which makes it

vulnerable to natural calamities. Thus, in order toreduce enormous losses, the call for the Taiwanese

government to share, copy, and reuse prior or

existing knowledge in a complex IHCS, as a means

of sustainable development over time in a

communi ty, has become loud and clear .Until comparatively recently, natural calamities

brought to light the importance of Incident or

Hazard Command System (IHCS) operations.

However, Taiwan still suffers from natural

calamities due to poor, incomplete, and inconsistent

system performance. There are four IHCS stages:Mit igat ion, Preparedness, Response, and

Post-disaster Reconstruction and each stage has acomplex and dynamic problem-solving method

Given the strike progress of web service technology

associated with the continuing rapid growth in

knowledge management, it is imperative that weinvestigate the problem-solving knowledge and

requirements of each stage in IHCS operations and

try to design a task ontology that is accessible

through the Web in order to reduce the impact of

d i s a s t e r s o n h u m a n l i f e a n d p ro p e r t y .The research presented in this paper is part of a

larger project, supported by the National ScienceCouncil in Taiwan, aimed at developing a

comprehensive and dynamic IHCS framework that

is capable of enhancing the probability of projectsuccess. It is our hope that the findings of this

study can lead the country in establishing

self-sustaining practices grounded in systematic andeffective knowledge capture, reuse, sharing and

learning.

2.Related Work

Proceedings of the 39th Hawaii International Conference on System Sciences - 2006

10-7695-2507-5/06/$20.00 (C) 2006 IEEE


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2.1 Knowledge Management

In the past decade, perhaps the most dramaticevolution in business has been the dawn of the new

economy [9]. A hallmark of the new economy is

the ability of an organization to increasingly

recognize that in the post-industrial era, an

organizations success is determined mainly by itseconomic value based on its collection ofintellectual assets as well as assets of information,

production distribution, and affiliation.

In order to achieve competitive sustainability,

many organizations are launching extensiveknowledge management efforts and relying heavily

on knowledge creation. Unfortunately, due to alack of absorptive capacity, many knowledge

management projects are, in reality, information

projects [23]. Gold et al. indicated that the concept

of knowledge management is cast in doubt when

these projects yield some consolidation of data but

little innovation in the ability to use prior

knowledge and create new knowledge. In essence,in terms of combination and exchange, the quest to

move beyond information management and into therealm of knowledge management is a complex

undertaking which involves the development of

knowledge structure that allows organizations to

recognize, create, transform, and distributeknowledge effectively.

Musen [19] pointed out that one of the major

shortcomings of the current technology for

knowledge-based building is lack of reusability and

sharability of knowledge. This makes it difficult to build knowledge bases, since one always has to

build from scratch what he/she believe and must

ignore the actual and potential resources embeddedwithin, available through, and derived from the

justified true believe. Clearly, facilitating

knowledge usable and useful thus should contribute

to making it easier to build knowledge bases and fitthem to the use-context. In order to achieve this,

Mizoguchi et al. [18] indicated that expertise can be

decomposed into a task-dependent but

domain-independent portion and a task-independent

but domain-dependent portion. The former iscalled task knowledge, formalized knowledge for

problem-solving domain independently.

2.2Task OntologyAt the end of the twentieth century and the

beginning of the twenty-first, ontologies have

emerged as an important and increasing research

focus in a variety of academic setting. Thisphenomena stems from both their conceptual use of

organizing information and their practical use incommunicating about system characteristics [8].

In general, an ontology can be viewed as an

information model that explicitly describes the

various entities and abstractions that exist in a

universe of discourse, along with their properties

[11]. Moreover, an ontology is a partial

specification of a conceptual vocabulary to be usedfor formulating knowledge-level theories about a

domain of discourse. From a system standpoint,ontologies provide an overarching framework and

vocabulary to describe system components andrelationships for communicating among architecture

and domain areas [7]. Therefore, the more the

essence of things is captured, the more possible it isfor an ontology to be shared [10].

There are a number of categorization of

ontologies currently in place. Van Heijst et al. [24]

classified ontologies according to the amount and

type of structure of the conceptualization and thesubject of the conceptualization, while Guarino [12]

distinguished the type of ontologies by their level ofdependence on a particular task or point of view.

Later, Lassila and McGuinness [14] grouped

ontolgies from the perspective of the information

the ontology need to express and the richness of its

internal structure.Ontologies and problem-solving methods (PSMs)

have been created to share and reuse knowledge and

reasoning behavior across domains and tasks [10].

In general, ontologies are concerned with static

domain knowledge, a given specific domain, whilePSMs deal with modeling reasoning processes anddescribed the vocabulary related to a generic task oractivity. Benjamins and Gomez-Perez [1] defined

PSMs as a way of achieving the goal of a task.

PSMs have inputs and outputs and many decompose

a task into subtasks, and subtasks into methods. In

addition, a PSM specifies the data flow between itssubtasks.

Given the definition from Guarino [12], task

ontology is an ontology which formally specifies the

terminology associated with a problem type, a

high-level generic task which has characteristicgeneric classes of knowledge-based application.Chandrasekaren [5] also defined task ontology as a base of generic vocabulary that organizes the

task knowledge for a generic task. From the

problem-solving viewpoint, Newell [21] illustrated

that task ontology can be used to model the

problem-solving behavior of a task either at theknowledge level or symbolic level. Thus, the

advantage of task ontology is that it specifies not

only a skeleton of the problem-solving process but

also the context in which domain concepts are used.In 1995, Mizoguchi, Tijerino, and Ikeda [17]developed a task analysis interview system,

MULTIS, which interacts with domain experts toidentify the detailed task structure based on two-

level mediating representations. Ikeda, Seta,

Kakusho, and Mizoguchi [13] indicated that task

ontology can be interpreted in two ways: (1)

task-subtask decomposition together with task


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categorization; and (2) an ontology for specifying

problem-solving processes. They developed

CLEPE, based on task ontology, to make problem-

solving knowledge explicit and to exemplify itsavailability. Rajpathak and Motta [22] formalized

task ontology by using the OCML, which provides both support for producing sophisticated

specifications and mechanisms for operationaldefinitions to provide a concrete reusable resource

that supports knowledge acquisition.

With the volumes of information that continue toincrease, the task of turning and integrating this

resource dispersed across the Web into a coherent

corpus of interrelated information has become a

major problem. The emergence of the Semantic

Web has shown great promise for the nextgeneration of more capable information technology

solutions and marked another stage in the evolutionof ontologies and PSMs [10]. Berners-Lee [2],

inventor of the Web and coiner of the term

Semantic Web, commented that the Semantic Web

is envisioned as an extension of the current Web in

which information is given well-defined meaning, better enabling computers and people to work in

corporations, effectively interweaving human

understanding of symbols with machine

processability. The way to fulfillment of the

corporation can be paved by using sharedknowledge-components, and ontologies and PSMshave emerged as a core technology for developing

the Semantic Web. The Semantic Web with

ontologies and PSMs can solve some problems

much more simple than before and can make it

possible to provide certain capabilities they have

otherwise been very difficult to support [6].

3.Research MethodologyIn this section we present the TTIPP (Task

analysis, Task ontology, IDEF0, Petri net, PNML)

framework, as shown in Figure 1, to organize and

model the task knowledge acquire during the

knowledge-acquisition activity, using external

resources and implement XML-based languages inwhich the task ontology will be formalized and

implemented. The TTIPP framework is aimed at

not only reducing the brittle nature of traditionalknowledge-based system, but also enhancing the

knowledge reusability and sharability over different

applications. Furthermore, based on Rajathak et

al.s [22] suggestions, three important issues---appropriate level of generality, domain independent

knowledge representation, and domain expert

perspicuity--- are considered while developing the

task ontology. Furthermore, according to the

analogy of natural language, TTIPP is composed ofthree layers and five phases. The top layer, the

lexical level model, mainly deals with the

syntactic aspect of the problem-solving description

in terms of the task analysis phase and task ontology

phase. The middle layer, called the conceptual

level model, captures the conceptual level meaning

of the description. The IDEF0 model phase andPetri net model are shown in this layer. The

bottom layer is called the symbol level model.The PNML phase corresponds to the runnable

program and specifies the computational semanticsof problem solving.

Armed with the above-mentioned points, this

study can provide a core epistemology for theknowledge engineer while developing the task

ontology for a generic task. Now, we present the

research framework model for illustrating the

important phases in the development of the task

ontology.

Phase-I: The Task AnalysisDuring the first phase of development, the nature

task needs to be analyzed thoroughly at a

fine-grained level with diverse information needs.

The structure, semi-structure, or even unstructured

knowledge can be acquired and elicited fromvarious sources, such as the available literature on

the task, the test cases specific to the problem area,

the actual interview of the domain experts, and

ones previous experience in the field, etc. Ikeda ei

al. [13] pointed out that task analysis is madeaccording to two major steps: (1) roughidentification, and (2) detailed task analysis.Based on the various sources of knowledge, rough

identification of task structure is a classification

problem, while detailed task analysis means to

interact with domain experts and articulate how they

perform their task. Once the various knowledgesources, in terms of a variety of forms, such as

document, fact, records, are analyzed in detail, the

important concepts from all of the different classes

of application can be a heightened awareness in

such a way that this knowledge provides enough

theoretical foundation for expressing the nature ofthe problem. According to this view, the initialfocus of the task analysis is to concentrate on the

most important concepts around which the task

ontology needs to be built

Phase-II: Conceptualization of the Task Ontology

A detailed level of the concept is indispensablefor task knowledge description. Thus, this stage is

generic in the sense that it gives a fundamental

understanding of the relations among different

concepts. Also, in accordance with the elicitedconcepts given in the previous phase, this stage

provides the ontological engineer an idea about the

important axioms that needs to be developed inorder to decide on the competence of the task

ontology. From the standpoint of granularity and

generality, Ikeda et al. [13] suggested that lexical

level task ontology should consist of four concepts:

(1) generic nouns representing objects reflecting


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their roles that appear in the problem-solving

process; (2) generic verb representing unit activities

that appear in the problem-solving process; (3)

generic adjective modifying the objects; and (4)

other words specific to the task. Figure 2 presents

a hierarchy of the lexical level task ontology.

Figure 1 Research framework

Figure 2 Lexical level of task ontology

Phase-III: IDEF0 Model

During this phase, task ontology in the research

framework can be operationalized by using theformal modeling language tool. It transforms the

concepts described at the natural language level into

the formal knowledge modeling level in terms of

structured graphical forms. A multi-level model

with different classes and relations can be created in

order to formalize the complex problem into being

simple and more detailed and to understand eachindividual level based on its input parameters.

Thus, the input parameters, altered by the activity or

function, identified at each level can be modeled in

such a way that the expected output to the problem

Task Analysis

Task Ontology

IDEF0

Model

Petri net

Model

Lexical level model Conceptual level modelSymbol level model

PNML


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can be achieved. IDEF0 is a widely used,

activity-oriented modeling approach [9]. Its

diagram, based on a simple syntax as showed in

Figure 3, contains an ordered set of boxes thatrepresent activities performed by the task. The

inputs are those items transformed by the activityand the outputs are the results of the activity that

come from the left and the right side, respectively.The mechanism, drawn as supporting arrows,

entering from the bottom of the box, represents

resources such as machines, computers andoperators, etc. The control (on the top), displayed

as signal arrows, represents control information

such as parameters and rules of the control systems.

The boxes call ICOM

(Input-Control-Output-Mechanism) arehierarchically decomposed and continue until there

is sufficient detail on the basic activities to serve thetasks [23].

Phase-IV: Petri net Model

Broadly speaking, the IDEF0 model has a

number of disadvantages in terms of its time-basedfunction, including cumbersomeness, ambiguity in

activity specification, and perhaps most significantly,

its static nature [6]. The Petri nets (PN) has

emerged over the past ten years as a powerful tool

especially suitable for systems that exhibit

concurrent, conflict, and synchronization [15]. APetri net necessarily consists of three entries: (1) the

place, drawn as a circle; (2) the transition, drawn asa bar; and (3) the arcs, connecting places and

transitions, as shown in Figure. 4(a) [7]. Generally,the PN is defined as follows [4]:

PN = (P, T, A, W, M0)

where,

P = {P1, P2, , Pm} is the finite set of places;T = {T1, T2,, Tn} is the finite set of transitions

withP andPT= ;

A{Px T}{TxP} is the set of arcs betweenthe places and transitions;

W:A {1, 2, 3,...}is the weight function on

the arcs;M0:P {0, 1, 2, 3, ...} is the initial marking.

Figure 3 Component of the IDEF0 model

Figure 4 Component of Petri net model

Cassandras and Lafortune [4] pointed out that a

transition is enabled if each input place P of T

contains at least the number of tokens equal to the

weight of the directed arc connecting P to T.

FUNCTION

NAME

Control

Input Out ut

Mechanism

Idle Working

T1

T2

P1 P2

(b)

Idle Working

T1

T2

P1 P2

(a)


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Where an enabled transition T1 fires as shown in

Figure 4(b), it removes the token from its input

place and deposits it in its output place. Known as

condition/event nets or place/transition nets, PNmodels are suitable to represent the structure of

systems in an extensive use of hierarchical levelsthat exhibit concurrency, conflict, and

synchronization [15].

Phase-V: Petri Net Markup LanguageThe Petri Net Markup Language (PNML) is an

XML-based interchange format for Petri nets. The

PNML is designed to be a Petri net interchange

format that is independent of specific tools and

platforms. Moreover, the interchange format needs

to support different dialects of Petri nets and must be extensible. Thus, PNML should necessarily

include the following essential characteristics [3]:(1) Flexibility means that PNML should be able

to represent any kind of Petri net with its specific

extensions and features.

(2) Ambiguity is removed from the format byensuring that the original Petri net and its particular

type can be uniquely determined from its PNML

representation.

(3) Compatibility means that as much

information as possible can be exchanged between

different types of Petri nets.Even with a mature development on Petri net

technology, it is difficult to know what is possible in

the future. Certainly, PNML should shed light on

the definition of Petri net types to support different

versions of Petri nets and, in particular, future

versions of Petri nets. Given the above mentioned

information, PNML is adopted as a starting point fora standard interchange format for Petri nets.

4. Application to an Incident or Hazard

Command System (IHCS)

Sustainable development is a complex problem,

especially for the Incident or Hazard CommandSystem which requires collaboration and

participation from the national level as well as thelocal communities. The area of Homchu at Natou

county, located at Central Taiwan, the site of

significant disasters resulting from the earthquake

on September 21st

, 1999 and from Debris-flow in

the past two decades, was chosen as a research site

of local communities. Focusing on safety and thesustainability of lives and livelihoods, the residents

of Homchu were involved in owning both the

problem and solution for the natural calamities.

Six stakeholders (different team leaders) involved inthe Incident or Hazard Command System (IHCS)

operations were invited to share their experienceand reconfirm the content of their interview.

Based on the suggestion of Ikeda et al. [13], task

knowledge of Incident or Hazard Command System

(IHCS) operations from a variety of sources such as

the available literature and the interview of the

domain experts was analyzed for the lexical level

task ontology. Normally, there are four IHCSstages: Mitigation (A1), Preparedness (A2),

Response (A3), and Post-disaster Reconstruction(A4). Due to the complex and dynamic nature of

problem solving methods for IHCS, emergencyresponse (A3) of Debris flow was chosen to serve as

an example for presenting the TTIPP framework.

At the modeling stage, the IDEF0 model wasbuilt for describing the function of the emergency

response. From a functional point of view, the

present emergency response has six activities as

shown in Figure 5. The six activities included

Establishing an advance command post

(A31) Monitoring any possible disaster

locations (A32) Evacuating residents

(A33) Urgent repair on construction

(A34)Emergency rescue for casualties(A35)

and Managing goods and materials (A36).

The mechanisms of Civil defense command team(MO1) support all of activities in the emergencyresponse, while each activity is supported by a

variety of mechanisms, respectively.

For the purpose of making it simple tounderstand, the hierarchical decomposition of each

activity into sub-activities was performed to reveal

more detail at each level. Monitoring any

possible disaster locations(A32) served as an

example and was decomposed into five

sub-activities, as shown in Figure 6: observe wild

stream (A321) patrol community

(A322) announce first level situation

(A323)announce second level situation (A324)and establish guard (A325). The different

mechanisms support the different sub-activities as

shown in Figure 6. Moreover, the observe wild

stream(A321) and patrol community (A322)

are controlled under the weather information (c1)

and community emergency plan (C2), while

establish guard (A325) is controlled under the

evacuation action completed (c3). After obtaining

the functional IDEF0 model, we can then develop

the Petri net model for behavior analysis at nextstage.

The PN model is constructed according to the

five activities in the IDEF0 model built at previous

stage, as shown in Figure 7. The PN model

consists of 14 places and 10 transitions, of which

the corresponding notations for monitoring any

possible disaster locations(A32) are described in

Table 1. After constructing the PN model, we

performed the model validation of the dynamic

behavior using the reachability tree. This revealedthat the PN consisted of the liveness, boundedness,

reversibility, and reachability.


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Figure 5. The IDFE0 model of the Emergency response(A3)

Figure 6. The IDFE0 model of the Monitoring any possible disaster locations(A32)


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Figure 7. The PN model of the Monitoring any possible disaster locations (A32)

Table 1. The Notation of place and transition for Monitoring any possible disaster location (A32)

Place Name IDEF0 Transition Name IDEF0

P1 P321 A321 Observe wild

stream

T1 T321-1 A321 Start observe wild

stream

P2 P322 A322 Patrol

community

T2 T321-2 A321 End observe wild

stream

P3 P323 A323 Announcesecond level situation

T3 T322-1 A322 Start patrolcommunity

P4 P324 A324 Announce first

level situation

T4 T322-2 A322 End patrol

community

P5 P325 P325 Establish guard T5 T323-1 A323 Start announce

second level situation

P6 P32-C1 C1 Weather

information

T6 T323-2 A323 End announce

second level situation

P7 P32-C2 C2 Community

disaster protection plan

T7 T324-1 A324 Start announce

first level situation

P8 P32-O1-C O1-C Hourly rainfallreaching 200mm (yellow

alert)

T8 T324-2 A324 End announce firstlevel situation

P9 P32-O5-C O5-C Compulsory

evacuation

T9 T325-1 A325 Start establish

guard

P11 P32-C3 C3 Evacuation action

completed

T10 T325-2 A325 End establish

guard

P11 P32-MO1 MO1 Civil defense

organizationmonitor

team

P12 P32-MO3 MO3 Civil defense

organizationcommand

team and evacuation

team

P13 P32-MO4 MO4 Governmentorganization fire

brigade

P14 P32-ME2 ME2 Communication

facilitiesintercom,

cellular phone


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At symbol level model, PNML was adopted as

a starting point for a standard interchange format for

Petri nets. The XML-based interchange format for

the above-mentioned Petri net model is shown

below.

5.Conclusions and Future WorksTo bridge the understanding gap between the

computer and human beings, we presented the

TTIPP framework and its related methodologies.

TTIPP consisted of three layers, including lexical,conceptual, and symbolic, level model, and five

phases: task analysis, task ontology, IDEF0 model,


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Petri net model, and PNML, for task analysis

problem and mediating representation based on task

ontology. The IDEF0 model was used to capture

the requirements corresponding to the systemspecification at the stage of functional analysis.

Subsequently, at the stage behavior analysis, thePetri net model was constructed according to the

IDEF0 model. Finally, at the implementation stage,the obtained model can be realized by using Petri

net marked coding languages.

Ideally, task ontology, produced by TTIPP, doesnot subscribe to any specific problem-solving

approach. It provides a sound ontological

foundation for the different problem solving

approaches and can be used to support a great

variety of task modeling independently of the targetshell or computational method. Therefore, it does

not only enables better access to information andpromote shared understanding for humans, but also

facilitates comprehension of information and more

extensive processing for computers. Sustainable

development is a complex problem, especially the

Incident or Hazard Command System whichrequires collaboration and participation among

government agencies, academic institutes, private

industries, non-government organizations and local

communities. Thus, in the future, we plan to use

this task ontology as a major building block fordeveloping the generic problem-solvers forunderstanding the space of the Incident or Hazard

Command System as well as advancing the field in

general.

Acknowledgement

This work was supported in part by the following National Science Council (NSC) grants: NSC

93-2625-Z-224-002.

References

[1]Benjamins, V. R. & Gomez-Perez, A. (n.d.).Knowledge-System technology: ontologies andProblem-Solving Methods. Retrieved December 13, 2004,from http://hcs.science.uva.nl/usr/richard/pdf/kais.pdf

[2]Berner-Lee, T. (1999). Weaving the web: The originaldesign and ultimate destiny of the world wide web by itsinventor. HarperCollins Publishers, NY.[3]Billington, J. Christensen, S. van Hee, K. Kinder, E.Kummer, O. & Petrucci, L. (2004). The petrinet markuplanguage: Concepts, technology, and tools. URLhtto://www.informtik.hu-berlin.de/top/PNX/.[4]Cassandras, C. G. & Lafortune, S. (1999). Introductionto discrete event systems, Boston, MA: Kluwer.[5]Chandrasekaran, B (1986). Generic tasks forknowledge-based reasoning the right level of abstractionfor knowledge acquisition. IEEE Expert, 1(3), 23-30.[6]Colguhoun, G. J. Baines, R. W. & Crossley, R. (1996).A composite behavior modeling approach formanufacturing enterprise. International Journal of

Computer Integrated Manufacturing, 9(6), 463-475.[7]David, R, & Alla, H. (1994). Petri nets for modeling ofdynamics systemsA survey. Automatica, 30(2),175-202.[8]Dold, A. H. Malhotra, A. & Segars, A. H. (2001).Knowledge management: An organizational capabilities

perspective. Journal of Management Information System,18(1), 185-214.

[9]Feldmann C. G. (1998). The practical guide tobusiness process reengineering using IDEF0. New York:Dorset House Publishing.[10]Gomez-Perez, A., Fernandez-Lopez, M. & Corcho,O. (2004). Ontology engineering. London: Springer.

[11]Gruber, T. R. A. (1993). A translation approach to portable ontology specification. Knowledge Acquisition,5(2), 199-221.[12]Guarino, N. (1998). Formal ontology andinformation system. Retrieved December 6, 2004, fromhttp://www.loa-cnr.it/Papers/FOIS98.pdf[13]Ikeda, M., Seta, K., Kakusho, O. & Mizoguchi R.(1998).Task ontology: ontology for building conceptual

problem solving models. Proceedings of ECAI98Workshop on Applications of ontologies and

problem-solving model, pp.126-133.

[14]Lassila, O. & McGuinness, D (2001). The role offrame-based representation on the semantic web(KSL-01-02). Knowledge System Laboratory, StanfordUniversity, Stanford, CA.[15]Lee, J. S. & Hsu, P. L. (2003). An IDEF0/Petri netapproach to the system integration in semiconductormanufacturing systems. IEEE International ConferenceSystems, Man and Cybernetics, Vol.5, pp.4910-4915[16]Mizoguchi, R., Tijerino, Y. & Ikeda, M. (1992).Task ontology and its use in a task analysis interviewsystem Tow-level mediating representation in MULTIS,

Proceedings of the 2nd Japanese JKAW92, pp.185-198.[17]Mizoguchi, R., Tijerino, Y. & Ikeda, M. (1995).Task analysis interview based on task ontology. Expert

Systems With Applications, Vol.9, No.1, pp.15-25.[18]Mizoguchi, R., Vanwelkenhuysen, J. & Ikeda, M.(1995). Task ontology of reuse of problem solvingknowledge. Towards Very Large Knowledge Bases:

Knowledge Building & Knowledge Sharing, pp.46-59.[19]Musen, M. (1991). Dimension of knowledge sharingand reuse. Knowledge Systems Laboratory (reportKSL-91-65). Stanford, CA: Stanford University.

[20] National Institute of Standards and Technology(1993). Integration definition for function modeling(IDEF0). FIPS 183. Retrieved November 30, 2004, fromhttp://www.itl.nist.gov/fipspubs/idef02.doc[21] Newell, A. (1982). The knowledge-level, ArtificalIntelligence, 18, 87-127.[22]Rajpathak, D. G. (2001). The task ontologycomponent of the scheduling library: Pilot study.

Knowledge Media Institute, The Open University.

[23]Santarek, K, & Buseif, I. M. (1996). FMS designusing non-conventional model techniques and standard

software tools. 5th natl. Conf. on Robotics, Institute ofTechnical Cybernetics, Wroclaw technical University,Poland.[24]van Heijst, G., Schreiber, A. T. & Wielinga, B.J.(1997). Using explicit ontologies in KBS development.

International Journal of Human-Computer Studies, Vol.46, Issue: 2-3, pp. 183-292.


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Using IDEF0-Petri Net for Ontology-Based Task Knowledge Analysis