E-Journal of Advanced Maintenance Vol.2 (2010) 120-134
Japan Society of Maintenology

Building Interoperable System Enabling Failure Information Knowledge
Sharing between Designing/Manufacturing and Maintenance Departments
Focusing on Information Architecture Differences
Akira NAKAJIMA1,*, Takako ONISHI1 and Tetsuo SAWARAGI2
1
2

OMRON Corporation, 9 -1 Kizugawadai, Kizugawa -city, Kyoto 619 -0283, Japan
Kyoto University, Yoshida -Honmachi, Sakyo -ku, Kyoto 606 -8501, Japan

ABSTRACT
Recent years have seen a shortening of product lifecycles and a trend toward product optimization for individual
customers, but at the same time, development and maintenance departments have become increasingly global inscope making it difficult to improve product quality in the market. Against this background, the need has been
felt for a system that can make efficient use of failure information knowledge accumulated for different products
and by different departments to improve maintenance response and prevent reoccurring failures. Althoughadvances in information processing technology have driven the evolution of practical information -storage andinformation -retrieval systems, the search performance of an interoperable system handling failure information
knowledge across different products and departments suffers from many problems in terms of effectiveness andefficiency if using the standard keyword search method. This paper proposes a new information retrieval system
focusing on information architecture differences between departments and presents the results of evaluating an
actual failure information system using this system.
KEYWORDS

ARTICLE INFORMATION

information architecture, FMEA, information foraging, failure information,knowledge sharing

Article history:
Received 22 October 2010
Accepted 22 November 2010

1. Introduction
Advances in information processing technology have enabled the storage and application ofmuch information, and many information retrieval systems for efficiently accessing this information
have come into use. The Internet has contributed greatly to the development of society through thestorage and use of massive amounts of information. Nevertheless, there are many information
retrieval systems that do not necessarily function well, such as systems used by design andmanufacturing departments to access failure information stored in a maintenance department (referred
to below as “failure information retrieval system”). In many companies, failure information obtained
from users and stored in the maintenance department can be extremely valuable to the design andmanufacturing departments at the time of product development since it can help in preventing failuresfrom reoccurring in the market. Problems have arisen, however, in achieving efficient use of thisinformation.
Denoting a group of information created for some purpose as “content,” these problems originate in
the mechanism used for propagating such content from one person to another. Each site on the Internet
can be treated as content, as can a customer’s report of a failure in a failure information retrieval system.
Two issues, in particular, arise in the process of propagating information in content form from one
person to another: comprehension of information expressions in content and search performance ofcontent that expresses information. For the first issue, it is thought that comprehension comes down tothe abilities of the content creators and content users, and for the second issue, that search
performance greatly depends on the information retrieval system itself. The focus of our research is
not on improving comprehension by creating contents in a certain manner but on achieving effective
use of contents that has already been created through an appropriate information retrieval system.
*

Corresponding author, E -mail: nakajima@ari.ncl.omron.co.jp

ISSN-1883-9894/10 © 2010 – JSM and the authors. All rights reserved.

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Knowledge Sharing between Designing/Manufacturing and Maintenance Departments
Focusing on Information Architecture Differences

Why is it that successful information retrieval systems such as the Internet exist while there aresome systems such as for failure information retrieval that do not necessarily function well? We
consider the difference between such successful and unsuccessful systems to lie in the extent to which
the information architecture of the content provider differs from that of the user. On the Internet,
content is generally created with user needs in mind with the result that differences in informationarchitecture between the provider and user are small. A defect information retrieval system, in contrast,
consists of content that places priority on responding to customer -reported failures. As a result,differences in information architecture between the provider and user can be significant.
What kind of Human Centered Design should be used as a cornerstone to improve searchefficiency in an information retrieval system in which information architecture differences between
the content provider and user are great? In this study, we first analyze the reasons as to why efficient
searches cannot be performed by standard keyword searching specifically in a failure information
system. Then, with the aim of improving search efficiency in an information retrieval system in which
information architecture differences between the provider and user are great, we seek to answer theabove question from the viewpoint of information foraging theory[1][2].
This paper is organized as follows. Section 2 analyzes the problems with the keyword searchmethod in information retrieval having major information architecture differences. Section 3 models
the search process and proposes a search method based on abstract concepts. Section 4 evaluates anddiscusses the search performance of a defect information retrieval system using both methods. Finally,
Section 5 summarizes the findings of this study.

2. Problems with the Keyword Search Method in Information Retrieval having
Major Information Architecture Differences
2.1. Differences in information architecture
Failure Mode and Effect Analysis (FMEA) is a technique commonly used in design andmanufacturing processes in the manufacturing industry. An example of a record in a FMEA system ascommonly used in design departments is shown in Fig. 1. Please see Ref. [7] for more details on
FMEA. In actuality, a FMEA record also includes items such as failure mode impact and frequency,
but since these items are not used at the time of searching, we omit them in this study. Here, each
FMEA record is referred to as “content.” An example of a defect report as issued by the maintenancedepartment is shown in Fig. 1(b). Although format and item descriptions may differ between
companies and departments due to the lack of common specifications, the items making up a failure
report are commonly “phenomenon,” “cause,” “countermeasure,” and “details.”
Content X
Content Y

Part
LED
S3D2

Failure Modeburned out
measurement error

Cause
error in rated design
calibration error

Countermeasurerevise design standard
Revise Standard operation Procedure(a) Typical FMEA records

Phenomenon:

Cause:Countermeasure:
Details:

Content A

Content B

Content C

Content D

Abnormaloperation inmeasuring part
Design error in
cam mechanism
Replace part
Numerical data

LED on main
unit notflashing
Damage due
to poor design
Replace board
Pictures, text

Problem inoscillating-circuit display
LED damaged

Power -supply
problem in LED
strip
Installation error;
LED is OK
RepairIllustrations

Redesign board
Text(b) Example of Failure Reports
Fig. 1. Information architecture of FMEA and failure report121

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The FMEA process seeks to assign a relationship between each record and previously generated
failure information. The ability to efficiently search for cases related to each FMEA record inmaintenance department failure reports is therefore of great importance. In Fig. 1, Content B andContent C in the failure report are related to Content X in FMEA records in Fig. 1 (a), but since
information architecture differences between the two are significant, correct search results may not be
obtained by the standard keyword search method. We therefore decided to analyze the differences
between the information architectures of FMEA records and failure reports and to analyze theproblems with the standard keyword search method in information retrieval systems in which
information architecture differences are great.
There are six main elements that form the information architecture of content: item, description,format, perspective, viewpoint, and fluctuation. There are various types of items, such as explicit andimplicit, depending on how the content is configured. There are also various types of relationships
between items, such as parallel, hierarchical, and supplementary. A description provides more detailed
information on an item, and restrictions on the form that descriptions can take depend on the type ofcontent. Format refers to the method of expressing a description, and in addition to media like texts,
figures and tables, graphics, audio, and video, there are also bullets, word lists, detailed explanations,etc. Perspective is the scope of expressing a description. It may be explicit, implicit, or not indicatedat all. Viewpoint, meanwhile, pertains to the person conveying the description, and includes
standpoint, stature, and competence. Fluctuation refers to variety in expression and notation and to thelevel of description.
2.1.1. Differences in items
A FMEA record centers about a particular part and includes failure mode, cause, andcountermeasure as described above. A number of failure modes can exist for the part in question, andeach failure mode may be accompanied by more than one cause and countermeasure. In a failure
report, a record centers about a phenomenon and includes causes, countermeasures, and detailed
descriptions.
Part 1

FailureMode A
FailureMode B

Cause sCountermeasure a

Cause tCountermeasure b

Cause uCountermeasure c(a) FMEA items

Phenomenon A
Function
FailureCause
Positiveⅰ
Negativeⅱ
Negativeⅲ

Counter-measure e(b) Failure Report items

Fig. 2. Differences in FMEA and Failure Report items

2.1.2. Differences in description
In a FMEA record, the cause field describes what caused the failure to occur. A record in a failure
report, on the other hand, frequently describes a complaint about functional failure as a phenomenon,
and in the cause field, describes the part failure that caused that functional failure to occur. It is notuncommon for “cause” in a failure report to span multiple FMEA items. Furthermore, as shown in Fig.
2(b), “cause” in a failure report can include not only positive causes but also negative causes as aresult of investigating causes that may give rise to the phenomenon in question.
Part: part name
Failure Mode: failure nameCause
: cause of failure

none
none

(a) FMEA description

Function + complaint: Phenomenonpart name + failure name: Cause(b) Failure Report description

Fig. 3. Differences in FMEA and Failure Report description

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2.1.3. Differences in format
A FMEA record is usually described in short, concise sentences consisting of words andnumerical data, while a failure report is often described in a relatively free format using figures, tables,
and other media.
2.1.4. Differences in perspective
In FMEA, a record is commonly described taking all related products into account as reflected by
the entry “revise design standard,” while a record in a failure report is often limited to just the part inwhich the failure occurred as reflected by the entry “replace part.”
2.1.5. Differences in viewpointA FMEA record is commonly described using specialized terminology from the viewpoint of adesigner. A defect report, in contrast, is often described using simple and highly abstract expressions
focusing on functional complaints from the viewpoint of the customer.
2.1.6. Differences in fluctuation
Fluctuation in expressions and notation exist in both FMEA and defect reports, and in either case,there is a strong tendency to use compact expressions, but regularity varies with the individual.
Description level is also non -uniform; regularity here is often difficult to find.
2.2. Problems with the keyword search method
When searching for content having major differences in information architecture as in the case of
FMEA and failure reports as described above, three problems with the keyword search method cansignificantly degrade search performance.
The first problem is keyword context mismatch that results from the fact that the same keyword
can be used in various contexts. When a certain keyword is used in an unessential section of content,
many instances of content with low equivalency will appear in the list of results generated by that
keyword. In Fig. 4, the list of results generated by the keyword “LED” includes Content D in which
this keyword is used in the unessential context of “power -supply of LED strip.” In addition, thedescription of negative causes, which reflects differences in description among other differences in
information architecture, is a major factor behind the problem of keyword context mismatch.
“LED” = { Content B : LED on main unit not flashing, Content D : Power -supply problem in LED strip}
Fig. 4. Example of keyword context mismatch

The second problem is keyword expression mismatch in which instances of content with highequivalency are expressed by different keywords. It cannot be denied that contents with highequivalency can exist outside a generated list of contents, which is a major factor behind failed
searches. In Fig. 5, the expression “burned out” is used in Content X in Fig. 1(a), but in Content B andContent C, the expression “damaged” is used. This mismatch results in a failed search. The source of
this problem often lies in the specialized and abstract terminology that is used, which reflects
differences in viewpoint among other differences in information architecture.
“burned out” = Φ
Fig. 5. Example of keyword expression mismatch123

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The third problem is the compound keyword, which, as the name implies, originates in the use of
a compound term as keyword. In Fig. 6, the compound term “design error” will generally be broken
down into two words in the manner of “design ∨ error”, “∨” is OR operation. This results in the
retrieval of Content D having the expression “installation error,” which is totally irrelevant. On theother hand, if “design ∧ error”, “∧” is AND operation, were to be intentionally specified, Content B
and Content C would fail to be retrieved. There is a great tendency for search performance to drop asa result of such differences in fluctuation between FMEA and defect reports.
“design error” → “design” ∨ ”error” = {Content A：design error,
Content B：poor design,
Content C：redesign board,
Content D：installation error}
Fig. 6. Example of compound keyword problem

3. Search Technique Taking Information Architecture Differences into
Account
In the previous section, we showed why the standard keyword search method is not very good at
retrieving information in a system having major information architecture differences. Information
foraging theory[3][4][5][6] suggest that more efficient searching can be performed by treating the target
search space as an aggregate of many partial sets rather than one total set. In our study, we propose atechnique that, instead of directly performing a keyword search on the target search space, performs
searching only after comprehensively examining and classifying that space with the aim of improving
search performance.
3.1. Model of search process
In the following, contents obtained by searching is called “target contents” and information used
to judge whether retrieved target contents are actually the contents needed is called “a base content.”
In addition, the set of contents belonging to conceptual space expressed by some concept is called“content set based on a concept mark.” In Fig. 1, Content X in FMEA records is a base content andContent A, Content B, Content C, and Content D are a content set based on the concept mark “failure
report in a department”. Furthermore, if a content set based on a newly formed concept mark shouldbe generated within an existing content set based on a concept mark, the latter is called a total set andthe former a partial set. The list of results produced by a search is a partial set generated from the total
set of target contents. Analyzing information in multiple instances of contents and evaluating its
equivalency in terms of meaning is called analytical discrimination, and evaluating the equivalency ofconcept marks in contents is called comprehensive discrimination.
The contents search process can be modeled by the following sequence of actions. The process
begins by generating concept marks through concept formation based on information in a base content.
It then generates partial sets of target contents having high conceptual equivalency as determined bycomprehensive discrimination applied to the total set of target contents. The process then performs
analytical discrimination against each instance of target contents included in these partial sets to findas much target contents as possible having high equivalency with the base content.

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Conceptmark
markConcept
Concept
markConcept formation

Comprehensive
discrimination

Base content
Targetcontents
contents
TargetTargetcontents
partialset
partialset
setpartialAnalytical discrimination

Target contents total set
Fig. 7. Model of search process

Here, contents search time is the sum of concept -formation time, comprehensive -discrimination
time, and analytical discrimination time.
Nmi

ST =CT + GT + AT = Σ（cti＋gti＋Σatij）
i=1

j=1

ST: Contents Search Time
CT: Total Concept -formation Time
GT: Total Comprehensive Discrimination Time
AT: Total analytical Discrimination Time
cti : Concept -formation Time per Repetition
gti : Comprehensive Discrimination Time per Repetition
atj : Analytical Discrimination Time per One Instance of Contents
mi : Number of Instances of Contents to be Subjected to Analytical Discrimination
among the Partial Sets of Target Contents with Respect to a Certain concept
N : Number of Repetitions
In the above, concept formation takes up a relatively short time (CT is small). Comprehensive
discrimination, meanwhile, is a principal function provided by search systems, and in many cases, it isautomated as the main function of an information retrieval system. Analytical discrimination, in
contrast, depends on the objectives, knowledge, and experience of users, and is rarely automated. To
do so requires many man -hours. We therefore consider a strategy for minimizing as much as possible
AT, the dominant element of ST.
3.2. Search by classification based on abstract concept formation
We apply information foraging theory as proposed by Pirolli et al.[5][6][7] as a strategy tominimize total analytical discrimination time in the search process model. Information foraging theory
applies the ideas of optimal foraging theory in biology to information retrieval behavior in humans. It
consists of the following three models:- Information Patch Model
Should a searcher continue foraging in the current information site or move on to the next one?
- Information Scent Model
How does a searcher obtain cues or hints for foraging?
- Information Diet Model
Which foraging candidate out of many should be selected?
In this study, we apply the idea behind IP to information retrieval systems, propose a search -by-classification system based on abstract concept formation, and analyze the effectiveness of thissystem. Here, the process of forming concepts by the abstraction of descriptions included in contents
is called “abstract concept formation.” Instead of performing a search on the total set of target
contents straight away by keywords, we first classify information patches by abstract concepts andthen search within those patches.

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In this way, the base content and total set of target contents in Fig. 1 can be classified as shown in
Table 1.
Table 1 Classification of defect reports by abstract concept formation

Electricalperformance
Mechanicalperformance

Design induced
Content X
Content B
Content C
Content A

Assembly induced
Content D

Referring to the table, Content B, Content C, and Content D, while not equivalent by analytical
discrimination, are equivalent by comprehensive discrimination in terms of the concept mark[electrical performance] by abstract concept formation. Similarly, Content A, Content B, and Content
C are equivalent by comprehensive discrimination in terms of the concept mark [design induced] by
abstract concept formation.
concept mark: [electrical performance] = { Content B, Content C, Content D }(a) Partial set generated by concept mark [electrical performance]

concept mark: [design induced] = { Content A, Content B, Content C }(b) Partial set generated by concept mark [design induced]
Fig. 8. Examples of concept marks by abstract concept formation

Partial sets of target content generated by concept marks through abstract concept formation can
result in many items of contents with high equivalency when subjected to analytical discrimination
with a base content. On the other hand, when performing analytical discrimination among a basecontent and target contents outside of the generated partial sets, the probability of finding contents
with high equivalency is low. Abstract concept formation enables target search space to be limited.
Lowering the level of abstraction in a stepwise manner and combining abstract concepts enables target
content with high equivalency to be isolated with a low miss rate.
concept mark: [electrical circuit ∧ design induced] = {Content B, Content C}
Fig. 9. Example of isolating target contents by abstract concept formation

3.3. Comparison of keyword search and search by classification based on abstract concept
formation
The process of keyword concept formation typically examines a base content in an analyticalmanner and extracts keywords. Keyword extraction itself is relatively easy, and search performanceby keyword searching is high when searching against sets of contents having small differences in
information architecture. On the other hand, search performance is low between sets of contents
having large differences in information architecture in which the problems of keyword context
mismatch and keyword expression mismatch can easily occur. In this case, the number of attempts at
keyword extraction in a trial -and -error process increases and the probability of finding correct results
decreases.
Abstract concept formation comprehensively recognizes the total target set including a basecontent and generates partial sets that are conceptually close to the base content before commencing asearch. If a search returns results (“food” in foraging theory), the user can now select the partial set

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Focusing on Information Architecture Differences(“feeding area” in foraging theory) corresponding to one of those results and do a detailed search. If
the user is not satisfied with the information in that partial set, the user can then move to anotherpartial set to investigate further. This behavior is analogous to foraging theory. Please see “Quality
Feedback Model for Trouble Recurrence Prevention” for details on a method for conceptualizing andautomatically classifying words extracted from documents using a semantic network [ 8][9][10][11][12].

4. Evaluation and Discussion of Proposed Search Technique4.1. Data used in experiment
In this experiment, we used a claim information system (CIS) storing defect information on morethan 100 types of industrial electronic equipment as a maintenance department’s defect information
system, and took 200 defect entries from this system as a total set of target content. We also used
FMEA on certain industrial electronic equipment as a design department’s FMEA system, and treated
6 records from this system as search -base content. These FMEA records are shown in Table 2. Notethat only the record items “Part,” “Failure Mode,” and “Cause” used in actual searches are included inthis table.
Table 2 FMEA records used in experiment
Task No.
T1
T2
T3
T4

Part
Display circuit
SwitchLabel
Name plate
Power circuit IC

Failure Mode
Mishandling
Lifetime
Assembly error
Mishandling

T5
T6

SwitchSignal –line

Other
Manufacturing
failureConnector

Cause
Not described
Not described
Not described
Damaged by thermal
stressLow design margin
Wrong part

Table 3 shows the classification based on abstract concept formation. There are 2 large
classifications which are “Cause” and “Phenomenon” and they are not mutually exclusive. The large
classification “Cause” is classified into “Manufacturing”, “Design”, “Part”, "Transporting" and“Usage”, which are mutually exclusive within the large classification “Cause”. The large
classification “Phenomenon” is classified into “Unstable behavior”, “Operation error”, “Display
failure”, “External failure” and “Structural failure”, which are also mutually exclusive within the large
classification “Phenomenon”. Table 3 is given to subjects in experiments.
We classified automatically the 200 items of CIS into the ten concept marks with a software wedeveloped. In the software, the method described in 3.3 was implemented. It took about 20 minutes toclassify the 200 items.
Table 3 Classification based on abstract concept formation

Large
Classification
Cause

Phenomenon

Small
Classification
Manufacturing
DesignPart
Transporting
Usage
Unstable behavior
Operation error
Display failure
External failure
Structural failure

Description
Defects occurred in manufacturing process
Defects occurred in design process
Defects of parts
Defects occurred in packaging/transporting process
Errors in operation by users
Unstable behaviors of the products
Incorrect results
Displayed incorrectly
Defects such as scratches on product surfaces
Missing and/or incorrectly assembled parts

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4.2. Experimental method
Four technicians from a design department were selected as subjects based on the following
conditions:1) Subject is not familiar with CIS content
2) Subject has enough domain knowledge to assess the suitability of search results
Using each row of Table 2 as search -base content, the objective of each task was to retrieve
related examples from the 200 items of CIS data using the keyword search method and classification
search method based on abstract concept formation. To evaluate a learning effect in the second search
performed by a subject, subjects A and B perform search tasks T1, T2, and T3 by the keyword searchmethod and the second time by the classification search method based on abstract concept formation,and search tasks T4, T5, and T6 are each performed in reverse order. Subjects C and D perform searchtasks T4, T5, and T6 by the keyword search method and the second time by the classification searchmethod based on abstract concept formation, and search tasks T1, T2, and T3 are each performed inreverse order.
4.3. Experimental environment
The experiment was performed in a standalone environment. All subjects used the same personal
computer, which was equipped with a search system incorporating modules for keyword searchingand search -by -classification based on abstract concept formation, 200 items of CIS data, and a
stopwatch tool for measurement purposes. All other applications on the computer were disabled.
4.4. Experiment results and discussion
Experimental results for the six tasks are shown in Figs. 10 to 15 and Tables 4 to 9. Search times
are shown in Figs. 10 to 15 Results for the keyword search method are shown in gray and those forthe classification search method based on abstract concept formation in white. Results in black
indicate incorrect search results. While there were several cases of incorrect search results for the
keyword search method, all search results for the classification search method were correct.
Examining only correct results, it can be seen that search time by the classification search method was
shorter than that by the keyword search method in all cases.
Tables 4 to 9 show a history of the search formulas used. The letters on the left of a table identify
the subjects. Each cell in a table shows a concept mark generated by concept formation and thenumber of partial -set elements. The number of columns represents the number of times thatcomprehensive judgment and analytical judgment were repeated. The numeral within ( ) signifies thenumber of partial -set elements in concept -formation results, the numeral within [ ] the number of
elements requiring analytical judgment, and the numeral within { } the number of correct results
found. Non -hatched type in the table indicates classification by keyword and hatched type indicatesclassification by abstract concept formation. Italic type in the table indicates partial sets for whichcorrect results were found.
Experimental results for each task are summarized in sections 4.4.1 to 4.4.6. The results showthat the number of analytical discrimination has a significant impact on overall length of search time.
4.4.1. Analysis of Task T1
For Task T1, there was only one correct case. All subjects obtained good results in terms ofaccuracy and search time by the classification search method. As a compound term, the part name“display circuit” resulted in many hits of unrelated examples. In addition, narrowing down the search
to the keyword “display” still resulted in 56 hits requiring much analytical discrimination time. In an
attempt to prevent so many hits, Subject D specified the search formula “display ∧ circuit” and wasable to narrow down the number of hits to 15, but still failed in retrieving a correct result. This was an
example of a failed search due to compound terms in contents. In the classification search method,128

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three subjects selected the abstract concept “usage ∧ unstable behavior” as the first search formulabased on the failure mode “mishandled”, and while this selection ended in failure, the number of
instances of analytical discrimination was 6 at most thereby shortening search time.

Fig. 10. Task T1 search times
Table 4 History of Task T1 search formulas(a) Keyword search method

A
B
C
D

1st search formuladisplay circuit (106)error[9]display circuit (106)display circuit (106)

2nd search formuladisplay ∧ mistake (2)display circuit (106)display ∧ mistake (2)display ∧ circuit [15]{0}3rd search formuladisplay[56]{1}display[56]{1}display[56]{1}-(b) Classification search method

A
B
C
D

1st search formula
usage ∧ unstable behavior [3]usage ∧ unstable behavior [3]usage ∧ unstable behavior [3]usage ∧ display [3]{1}

2nd search formula
usage ∧ display failure [3]{1}usage ∧ display failure [3]{1}usage ∧ display failure [3]{1}-

3rd search formula-4.4.2. Analysis of Task T2
Next, for Task T2, there was again only one correct case. Search times were good for all subjects
by the classification search method. After selecting the abstract concept “part” in the classification
search method, the search was narrowed by using the keyword “switch” thereby preventing the
keyword context mismatch problem. The number of instances of analytical discrimination was 2 andsearch time was shortened.

Fig. 11. Task T2 search times

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Table 5 History of Task T2 search formulas(a) Keyword search method

A
B
C
D

1st search formulaswitch ∨ lifetime (42)switch [20]{1}switch [20]{1}switch ∧ lifetime [5]{1}

2nd search formulaswitch ∧ lifetime [5]{1}-(b) Classification search method

A
B
C
D

1st search formulapart(22)part (22)part (22)part (22)

2nd search formulaswitch [2]{1}switch [2]{1}switch [2]{1}switch [2]{1}4.4.3. Analysis of Task T3
For Task T3, there were two correct cases. Three subjects terminated the search after retrieving
one correct case using the keyword “label” in the keyword search method. The other correct case wasoverlooked due to the keyword expression mismatch problem in which “name ∨ plate,” anotherexpression for “label,” went unnoticed here. In the classification search method, selection of theabstract concept “manufacture ∧ external failure” resulted in 3 instances of analytical discrimination
enabling retrieval of all two correct cases in a short time.

Fig. 12. Task T3 search times
Table 6 History of Task T3 search formulas(a) Keyword search method
AB
CD

1st search formulalabel [2]{1}label [2]{1}label [2]{1}label [2]{1}

2nd search formulaname ∨ plate [3]{1}-(b) Classification search method
AB
CD

1st search formula
manufacturing ∧ external failure [3]{2}manufacturing ∧ external failure [3]{2}manufacturing ∧ external failure [3]{2}manufacturing ∧ external failure [3]{2}130

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4.4.4. Analysis of Task T4
For Task T4, there were three correct cases. Search times were good for all subjects by theclassification search method. With the keyword search method, there were 12 instances of analytical
discrimination by the search formula “power circuit ∧ IC.” However, after forming the abstract
concept “usage ∧ operation error”, the keyword context mismatch problem caused by the search
formula “power circuit ∧ IC” was avoided resulting in only 5 instances of analytical discriminationand the shortening of search time.

Fig. 13. Task T4 search times

Table 7 History of Task T4 search formulas(a) Keyword search method

A
B
C
D

1st search formula
power circuit (118)
power circuit (118)
power circuit (118)
power circuit (118)

2nd search formula
power circuit ∧ IC [12]{3}power circuit ∧ IC [12]{3}power circuit ∧ IC [12]{3}power circuit ∧ IC [12]{3}

3rd search formula-(b) Classification search method

A
B
C
D

1st search formula
usage ∧ operation error (58)
usage ∧ operation error (58)
usage ∧ operation error (58)
usage ∧ operation error (58)

2nd search formula
power circuit (32)
power circuit (32)
power circuit (32)
power circuit (32)

3rd search formulaIC [5]{3}IC [5]{3}IC [5]{3}IC [5]{3}4.4.5. Analysis of Task T5
For Task T5, there were no correct cases. Search times were good for all subjects by theclassification search method. In the keyword search method, subject D was able to reduce the numberof hits through use of the keyword expression “degree of margin,” but all subjects, after much trial
and error, were forced to analytically discriminate all examples related to the keyword “switch” andcould not avoid the keyword context mismatch problem. In the classification search method, allsubjects without exception searched by the keyword “switch” after forming the abstract concepts“design” and “part” thereby avoiding the keyword context mismatch problem and reaching aconclusion with very good efficiency.

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Knowledge Sharing between Designing/Manufacturing and Maintenance Departments
Focusing on Information Architecture Differences

Fig. 14, Task T5 search times
Table 8 History of Task T5 search formulas(a) Keyword search method

A
B
C
D

1st search
formulaswitch (20)switch [20]switch [20]switch (20)

2nd search formula

3rd search formula

4th search formulaswitch ∧ design (0)switch ∧ design (0)switch ∧ margin (0)Margin (0)Switch [20]-switch ∧ degree of margin [11](b) Classification search method

A
B
C
D

1st search
formuladesign [1]design [1]design [1]design [1]2nd search formula

3rd search formula

part (22)part (22)part (22)part (22)switch [2]switch [2]switch [2]switch [2]4th search formula-4.4.6. Analysis of Task T6
For Task T6, there were three correct cases. Search times were good for all subjects by theclassification search method. In this example, any keywords like “signal line” “connector,”“manufacturing failure”, and “mistaken part” could easily cause the compound keyword problem andthe keyword context mismatch problem to occur. All subjects, as a result, behaved in a perplexedmanner when using the keyword search method. In the classification search method, on the other hand,all subjects formed the abstract concept “manufacturing ∧ structural failure” thereby avoiding the
keyword context mismatch problem and retrieving the three correct cases in an efficient manner.

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Focusing on Information Architecture Differences

Fig. 15. Task T6 search times
Table 9 History of Task T6 search formulas(a) Keyword search method

A
B
C
D

1st search formulaconnector (45)line (37)connector (45)signal line (63)

2nd search formulaconnector ∧ part [44]{3}line ∧ connector [8]{3}connector ∧ signal [8]signal line ∧ connector [13]{3}

3rd search formulasignal line ∧ connector [13]{3}-(b) Classification search method
1st search formulaA
B
C
D

manufacturing ∧ structural failure [4]{3}manufacturing ∧ structural failure [4]{3}manufacturing ∧ structural failure [4]{3}manufacturing ∧ structural failure [4]{3}2nd search
formula-3rd search formula-

5. Conclusion
This study focused on failure information retrieval systems used by design and manufacturing
departments to access failure information in the maintenance department. We proposed aninteroperable failure information knowledge system between design/manufacturing and maintenancedepartments based on a classification search method using abstract concept formation that takes
information architecture differences into account. We also evaluated the proposed system comparing itwith the standard keyword search method. We showed that our proposed classification search method
based on abstract concept formation is superior to the keyword search method in both the accuracy
and speed of information retrieval.
In this study, we use the technique described in 3.3 to classify items automatically. To apply ourclassification search method to various systems, it should be taken into account which technique is themost suitable from viewpoints such as differences of architectures.
In future research, with the above in mind, we aim to make the results of this study more generalin scope and to develop interoperable systems that can deal effectively and efficiently with highly
diverse sets of content having greatly different information architectures.

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Knowledge Sharing between Designing/Manufacturing and Maintenance Departments
Focusing on Information Architecture Differences

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