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