The Research of IETM Knowledge Acquisition

Based on Semantic Wiki Xue Jianwu

School of Management

Northwestern Poly technical University Xi' an, China

xuejw@nwpu.edu.cn

Abstract-The acquisition about implicit knowledge of

experts ,technicists' experience ,in addition to the structural

knowledge inquired by intelligent IETM are the bottleneck in all

IETM projects, this problem is paid close attention to by

academic circles. The development and application of Semantic

Wiki provides a new solution for it. The paper presents an IETM

knowledge acquisition model at the core of Semantic Wiki, and

discusses the process of the knowledge acquisition, laying the

foundation for the IETM projects.

Keywords: Semantic Wiki Intelligent IETM RDF

Knowledge Acquisition

I. INTRODUCTION

IETM (Interactive Electronic Technical Manu) is an important component of the CALS (Continuous Acquisition and Life-cycle Support) strategy which was put forward by the U.S. Defense Department during the 1980's. It has developed from the initial page-Indexed Pictorial Materials to the frame­navigation, structured, integrated information system, consist of tables, letters, graphics, audios, videos and other data modes. Now, it moves in the direction of intelligence.

Intelligent IETM can realize the semantic retrieval of the information, helping the users to get high-quality information. Currently, the development of high performance computer and communication networks lays the foundation of hardware for the development and application of intelligent IETM. Furthermore, in the AI area, with the development of ontology project, ontology-based means of knowledge sharing, knowledge reasoning provides solid theoretical foundation for the Intelligent IETM.

But the basic of Intelligent IETM development is its knowledge base. It is a bottleneck for the knowledge base to get the implicit knowledge of experts and technicists' experience in addition to the structural knowledge inquired by intelligent IETM. Semantic Wiki is the new hotspot of information management in recent years, it provides a new way for the earlier mentioned problem.

A. Wiki

II. WlKl AND SEMANTIC WIKI

Wiki is one of the important applications of Web 2.0, it has become a symbol of a new Internet era. It is a

978-1-4244-6055-7/10/$26.00 ©2010 IEEE

356

Chen N a Gou Miao

School of Management Northwestern Polytechnical University

Xi'an, China

Karena99941 @126.com

collaborative writing tool invented by a Smalltalk programmer Ward Cunningham. Each member in the organization could explore and expand the common themes with their views and comments freely. At present, the biggest Wiki system among the world is Wikipedia; It is a free online encyclopedia .In 2005, a survey from the Nature says, Wikipedia is nearly as accurate as established sources such as Encyclopedia Britannica. It has the features of free, open, self-growth, as well as Participation of all. Scientists involvement would lead to a multiplier effect, Experts can help write specifics in a nuanced way [2]. These ensure that we can use Wiki to acquire comprehensive and accurate knowledge.

Wiki has been widely applied, Mediawiki, Hdwik,

MoinMoin, CooCooWakka, PmWiki, Twiki and etc. Among

them, Mediawiki is the most famous program which runs in Apache + PHP + Mysql environment and supports for

multiple languages. It has been Wikipedia's system software

since Feb 25, 2002. Now it is reliable and widely used, and its development is supported by the Wikipedia Foundation.

B. Semantic Wiki

Semantic Wiki uses the semantic web technologies like

RDF, OWL to expand the Wiki systems. Its core idea is: The

links between Wiki pages can automatically be processed by

machines(agents, services), enhanced navigation, semantic

search, reasoning, etc.Until now, scholars at home and abroad have already developed some Semantic Wiki systems, such as: Platypus Wiki,Semantic Wiki, SemperWiki,Ike Wiki, WikiSAR ,

etc. Semantic Mediawiki is widely used. It is the system that

Mediawiki system adds semantic plug-in. It has semantic

function, and can output structured knowledge.

III. KNOWLEDGE ACQUISITION MODEL

A. TraditionalIETM Knowledge Acquisition Model

Traditional IETM knowledge acquisition method

(Figure 1 )is: to scan and input a mass of paper materials;

format converse the CAD/CAM information files, and then

import the datum into the IETM database. Also some

knowledge is acquired by having interviews with the experts.

There are many defects of the traditional knowledge acquisition method:

•

•

The acquisition about implicit knowledge of experts and technicists' experience is so hard that the comprehensiveness and accuracy is effected

Massive scanning and entry cause many mistakes

unintentionally, much information can not be feedback

•

•

and revised timely .

It is hard to acquire the structured knowledge needed by intelligent building.

The process is high-cost and time consuming .

IETM database

Figure 1 .Traditional IETM Knowledge Acquisition Model

B. The IETM Knowledge Acquisition Model Based on Semantic Wiki

The model as Figure 2 is B/S mode, having improved

more than the traditional method. With the interaction of

domain experts, knowledge engineers and users, the contents

For milt Q)nversation

IETM database

on the Semantic Wiki platform are added, modified and

evaluated. All the Participants are being the members for

IETM knowledge base construction, easy to exchange ideas,

also it is facilitate for the implicit knowledge and explicit

knowledge conversion, structured knowledge output, reusing and modification.

User 2

Domain expert I

Domain expert n

Knowledge engineer 1

Knowledge engineer n

Figure 2. The IETM knowledge acquisition model based on Semantic Wiki

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With the power of groups ,The Semantic Wiki platform

can timely monitoring, updating and correcting the errors.

Through internal and external links, timely add new technologies and new products information. Knowledge­

sharing platform with Wiki features will be convenient for

knowledge acquisition. And groups of users can query and get

needed knowledge. All these strengthen the system

interaction, and significantly improve the completeness and

accuracy of the knowledge.

IV. THE PROCESS OF KNOWLEDGE ACQUISITON

The knowledge acquisition of IETM is the foundation of

intelligent IETM projects. The acquisition about implicit

knowledge of experts and technicists' experience in addition to

the strnctural knowledge inquired by intelligent IETM are the

bottleneck in all IETM projects.

A. Acquisition of Implicit Knowledge Knowledge is divided into two categories: implicit

knowledge and explicit knowledge. The explicit knowledge is

a formal coding system language, can make use of vector­

borne. But the implicit knowledge is implanted in the

individual mind, experience and hard to code. With the Wiki collaborative platform, users in the group can make explicit

knowledge more complete and accurate, while experience­

based knowledge acquisition is a superiority of semantic Wiki

platforms. It is a good example of Ikujiro Nonaka's SECI

Knowledge creation model: knowledge creation is a spiraling

process of interactions between explicit and implicit

knowledge .As Figure 3, the interactions between the explicit

and implicit knowledge lead to the creation of new knowledge.

The implicit knowledge of experts and technicists' experience

can be acquired though collaborative writing on the Semantic

Wiki platform. Meantime the knowledge needed users such

as: weapons and equipment technicists or maintenance staff

can obtain the domain knowledge easily.

impllcit Explicit

knowledge knowledge

implicit Socialization

Ir Externalization

knowledge

I � explicit

� V knowledge

Internalization Combination

SemanticWiki Platfonn

Figure 3 .Knowledge conversation

B. Acquisition of Structural Knowledge Knowledge extraction is an effective way to acquire

structured knowledge .With the semantic features of Semantic

Wiki and the environment experts and participants interact, it

can extract the general relationship, attributes, values, and

other basic information from common Wikipedia entries to

constitute ontological knowledge which can be transformed

into RDF triples. The paper applied Semantic Mediawiki.

When inputting the texts, we marked the relationship between

the entities and the attributes using the grammar of Semantic Mediawiki. Attribute [[attribute name: =attribute value II and

relationship [[relationship name::target page]], added

semantic information. In the process, knowledge engineers and experts collaborative involved. Thus the navigation of

Wiki system enhanced, the content of page more strnctured

expressed. Simultaneously, semantic query, and the ability to

aggregate or output of knowledge improved. The following

Figure 4 is an example of engine failure, extract ontological

knowledge from it.

• Karena My lalK MY prererences Wly walcnliSI MY conmoullons LOg OUI

Page Discussion Read Edit View history �LI _________ I � I Search I

Engine Failure

Solution [edit]

thermostat generally installed in the engine top. If the cooling water cannot reach a certain temperature, it can not transmit water to the tank.

As the thermostat may therefore be the reason, so the temperature reached the base temperature but can not deliver water to the tank, or

sending too little water, leading to engine overheating. You can replace the thermostat

Facts about Engine Failure RDFf8ed �

Causes of overheating The temperature reached the base temperature but can not deliver water to the tank + c;., and Sending too

little water +

Location Engine top + ....

Point offailure Thermostat +

Solution You can replace the thermostat + ....

Taboo If the cooling water cannot reach a certain temperature, it can not transmit water to the tank +

Figure 4. Output of structural knowledge

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C. The Wholel Process

After the semantic annotation for the input information,

there is a clear structure. And the semantic relation can be mined; knowledge unit can be extracted .The ontological

knowledge can be output in the form of RDF, easy for the

domain experts and knowledge engineers to get the needed

knowledge for the IETM domain ontology construction. Then

optimize the output of the RDF document [11], share datum for

the ontology building. Domain ontology construction itself is a

complex and difficult systems engineering. It needs experts in

the field, users and ontology developers, knowledge engineers

work together to complete, for ontology development has a low efficiency and a long cycle. Semantic Wiki formal

represent the domain knowledge, making information display

in a standard, machine can handle way .Then we can integrate

the fragments RDF documents in the protege to facilitate the ontology construction of intelligent IETM. Simple grammar

makes the process simpler.

Figure 5. The process of knowledge acquisition

V. CONCLUSION

IETM knowledge acquisition is the basic and guarantee

for all the IETM projects. The paper applied Semantic Wiki to obtain implicit knowledge of experts and technicists' experience in addition to the structural knowledge inquired by

intelligent IETM. Based on the knowledge acquisition model,

analyze the process that expert and non expert with ordinary

users together, using Semantic Wiki platform to tap the

potential of knowledge. It provides the weapons and equipment operators and maintenance personnel convenient

access to knowledge. At the same time, structured semantic

knowledge output can be easily used by the IETM domain

ontology builder to get relevant ontological knowledge.

ACKNOWLEDGMENT

The author of this article would like to thank this platform for giving me the chance and also like to thank the tutors in NWPU for giving a lot of suggestion.

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