Far East Journal of Experimental and Theoretical Artificial Intelligence
Volume … Issue …, 2008, Pages …
This paper is available online at http://www.pphmj.com
 2008 Pushpa Publishing House

SUGGESTING SUBJECT HEADINGS USING WEB
INFORMATION SOURCES∗HIROSHI UEDA, HARUMI MURAKAMI and SHOJI TATSUMI
Graduate School of Engineering
Osaka City University
3-3-138, Sugimoto, Sumiyoshi
Osaka 558-8585, Japane-mail: d06tb001@ex.media.osaka-cu.ac.jp∗

Graduate School for Creative Cities
Osaka City University
3-3-138, Sugimoto, Sumiyoshi
Osaka 558-8585, Japane-mail: harumi@media.osaka-cu.ac.jp
Abstract
We proposed a method that suggests subject headings based on user
queries when a pattern-matching algorithm fails to locate subject
searches for Online Public Access Catalogs (OPAC). We combined
information obtained from Wikipedia, Amazon, and Google for query
expansion. Our method has two main advantages: (1) availability for any
library without customizing OPACs, and (2) ability to suggest subject
headings when a query string is not included in OPAC’s bibliographic
information. Three experimental results using computer terms revealed
the following: (1) Suggested subject headings were related to the input
term; (2) Suggested subject headings were better when we used a
mixture of Wikipedia, Amazon, and Google than when just using one ofthem; (3) Our method can suggest subject headings when OPAC mining
Keywords and phrases :OPAC, subject search, subject headings, query expansion,

Wikipedia, Amazon, Google.
Communicated by Shun-Feng Su
Received July 26, 2008; Revised September 11, 2008

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HIROSHI UEDA, HARUMI MURAKAMI and SHOJI TATSUMI
cannot. We conclude that our method can serve as an alternative whenpattern-matching algorithms fail.

1. Introduction
Subject search is one essential search function that enables users tosearch collections by subject information in library Online Public Access
Catalogs (OPAC). Examples of subject information include author names,
subject headings, and classification numbers. Among these, subject
headings are controlled vocabularies and assigned to each collection (e.g.,books, periodicals). When librarians register a collection with OPAC, they
analyze its contents, select appropriate subject headings, and assign
them to OPAC. Subject search is useful when titles do not accurately
express the contents of the collection. Consider a book entitled “How tosearch for information in the internet age” to which subject heading“information retrieval” is assigned. When a user inputs “information
retrieval” as a query, this book will be retrieved although the title does
not contain the word “retrieval.” In addition, when a user selects a
subject heading and performs a subject search, no book unrelated to
information retrieval is included in the search results. Thus, the two
great merits of subject search are that (1) a user can search throughcollections whose titles do not include the user’s query and (2) a subject
search may well give precise results.
Most systems use pattern-matching algorithms to display subject
headings based on user queries. However, when a pattern-matchingalgorithm fails, no subject heading is displayed. In this paper, weinvestigate a method that suggests subject headings when patternmatching algorithms fail.
The following empirical procedure is carried out by librarians
searching for subject headings. First, they perform a keyword search andget some results. Second, they make selections from those results. Third,
they select subject headings contained in these results and perform asubject search. Automating this process may suggest subject headings.
Red Light Green1 [1] by Online Computer Library Center, one example of1

RedLightGreen service ended on December 2006.

SUGGESTING SUBJECT HEADINGS …

3such a system, displays a list of subject headings that are included in the
search results of the keyword search. A secret to Red Light Green’s
success is its “good ranking of search results.” It has a huge catalog ofover 120 million books in worldwide libraries from which it derives its
rankings. When a user inputs a query, it displays subject headings thatare included in high-ranked search results. We call this method “OPAC
mining” in this paper.
However, most OPACs are designed for only one library, and theirorder of search results is alphabetic. In addition, most libraries lack thebudget to modify OPAC software packages developed by vendors. To
implement such OPAC mining as RedLightGreen for suggesting subject
headings, libraries must rely on vendors. Even if they can find a capablevendor and gather the necessary funds, a method that suggests how tolocate subject headings using search results without ranking is unknown.
The other problem with OPAC mining is that no subject heading issuggested when an input query is not included in OPAC’s bibliographic
information (e.g., book titles, author names, publisher names, etc.). Thisoccasionally happens when input terms are relatively new.
We therefore investigate a method that suggests subject headings
using “free” Web information sources instead of customizing OPACs. Our
method suggests subject headings based on user queries by expanding
queries using Web information sources. The advantages of our method
include: (1) availability for any library without customizing OPACs, and(2) ability to suggest subject headings when a query string is not included
in an OPAC’s bibliographic information. In this research, we examine
Wikipedia [2], Amazon [3], and Google [4] as free Web information
sources for reasons stated in Section 3.
Below, in Section 2 we explain the subject headings and BSH4 subject
headings [5] used in this research. Our method is described in Section 3,
and experimental results are described in Section 4. We discuss our
method’s usefulness and related work in Section 5. The examples in thispaper were translated from Japanese into English for publication.

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HIROSHI UEDA, HARUMI MURAKAMI and SHOJI TATSUMI
2. BSH4 Subject Headings
Subject headings are controlled vocabularies in libraries sent tocollections to help subject searches. The most famous subject heading is
the Library of Congress Subject Headings (LCSH) [6]. In Japan, Basic
Subject Headings (BSH) and National Diet Library Subject Headings(NDLSH) [7] are representative. In this research, we investigate amethod to suggest BSH4, which is the latest edition of the most popularsubject headings in Japan.
BSH4 has 7,847 subject headings and 2,873 entry terms. We treat
these 10,720 terms as BSH4 subject headings, which feature “Used for(UF),” “Top Term (TT),” “Narrower Term (NT),” and “Related Term
(RT),”as in a thesaurus. For example, the BSH4 subject heading“information retrieval” has “IR” as a UF, “information science” as a TT,“information science” as a BT, and “indexing” and “database” as NTs.
In Sections 3 and 4, we abbreviate “BSH4 Subject Headings” to“subject headings.”
3. Method
3.1. Overview of our method
Our method is based on the vector space model and consists of threesteps.
Step 1. Creating subject heading vectors. A subject heading is
expanded using narrower terms to create a subject heading vector. Eachsubject heading vector is stored in a subject heading database.
Step 2. Creating a query vector. When a user inputs a query, it isexpanded using information obtained from Wikipedia, Amazon, andGoogle, and its query vector is created.
Step 3. Calculating similarity. The similarity between the query
vector and subject heading vectors is calculated using a cosine measure,based on a vector space model. As a result, ten subject headings aresuggested.

SUGGESTING SUBJECT HEADINGS …

5

Step 1 is a preparation process for a subject heading database. Steps
2 and 3 are processed continuously when a user inputs a query.
Figure 1 shows an overview of our method. When a user inputs“Java,” our method suggests ten subject headings including “computer
programming” and “Internet.”
In this paper, terms are defined as nouns consisting of two or morecharacters; indexing terms for creating both subject headings and query
vectors are defined as nouns consisting of two or more characters
extracted from subject headings. Nouns consisting of one character are
deleted because they are not very useful for indexing terms in Japanese.
Here we call this the “deleting one-character heuristic.”

Figure 1. Overview of our method.
3.2. Creating subject headings vectors
A subject heading is expanded using narrower terms to create its
subject heading vector, which is stored in a database.
For example, the subject heading “information retrieval” has an NT“indexing method” and “database.” The “indexing method” has a NT
“punch card.” In this case, a subject heading vector for “information
retrieval” becomes [information (1), retrieval (1), indexing (1), punch (1),card (1), database (1)], as shown in Figure 2. The number inside () is thefrequency of each term. “Method” is removed due to the deleting onecharacter heuristic. In this way, all subject headings are converted tosubject heading vectors.

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HIROSHI UEDA, HARUMI MURAKAMI and SHOJI TATSUMI

Figure 2. Creating a subject heading vector for subject heading“information retrieval”.
3.3. Creating a query vector
3.3.1. Reasons for using Wikipedia, Amazon, and Google
It is common to add terms contained in databases in query expansion.
However, this research’s starting point is that “we cannot use terms
contained in OPAC databases.” We therefore investigate what kind of
information is useful as a source of query expansion. We examined
Wikipedia, Amazon, and Google for the following reasons.
Wikipedia is a free encyclopedia available on the Web. According to
Guiles [8], Wikipedia approaches Britannica in terms of the accuracy ofits science entries. We believe it is beneficial to use information obtained
from Wikipedia to get relatively new terms related to user query
definitions.
Amazon is one of the most popular shopping sites on the Web. In
particular, Amazon book search is famous for the amount and quality ofits database. In Amazon’s book databases, category data are assigned toeach book as subject information in OPACs. In addition, Amazon’s search
results can be ordered using amount of sales. We therefore thought wecould get similar terms to those obtained in OPAC mining using Amazon.

SUGGESTING SUBJECT HEADINGS …

7

Google is one of the most frequently used Web search engines. Its
ranking of search results has a good reputation. When information isunavailable from Wikipedia or Amazon, Google functions as acomplementary source for query expansion. We thus believe that we canget good terms for query expansion by mixing terms extracted from
Wikipedia, Amazon, and Google.
The procedure for creating a query vector (Step 2) is comprised of thefollowing four steps.
Step 2.1. Getting texts from Wikipedia, Amazon, and Google. Texts
from Wikipedia, Amazon, and Google are searched and obtained.
Step 2.2. Extracting terms from each text. Terms are extracted fromeach text obtained in Step 2.1 and weighted.
Step 2.3. Merging terms. Terms are merged to create one vector.
Step 2.4. Filtering terms. Indexing terms are extracted from theabove terms to create a query vector.
Figure 3 displays an example of creating a query vector when theuser query “Java” was input on July 10th, 2005. We explain this example
in the following sections.
3.3.2. Getting texts from Wikipedia, Amazon, and Google
First, we discuss how to handle multi-sense words in this research.
For example, when a user searches for “Java” in Wikipedia, the first
result is “Java (Indonesian island),” the second result is “Java (coffee),”the third result is “Java (curry)”, the fourth result is “Java (programming
language),” and so on. In contrast, most of the top 100 results in both
Amazon and Google are related to “Java (programming language).”Amazon and Google search results are ordered by reputable ranking
algorithms. To obtain good terms for query expansion, we must
concentrate on one sense to avoid confusing various terms. Assuming that
Google’s search results help define one popular sense, we use the Google
site search to search Wikipedia. In this case, the defined sense becomes“Java (programming language)”. In this research, we get one text from
Wikipedia, three from Amazon, and five from Google.

8

HIROSHI UEDA, HARUMI MURAKAMI and SHOJI TATSUMIOur method first searches Wikipedia using the query through its top
page. If there is one result, then our method gets it; otherwise, it searches
the Google site search for Wikipedia. If there are results, then it gets thefirst result and then searches Amazon (actually, AWS [9], see Section
3.3.3), and if there are results, then it gets the first to third results.
Finally it searches Google (actually, Google Web APIs [10], see Section
3.3.3); if there are results, then it gets the first to fifth results.
3.3.3. Extracting terms from each text
Terms are extracted from each obtained text and weighted based on
the frequency of each term. Each process is described below.
(1) Wikipedia
One Japanese Wikipedia article was obtained in Step 2.1. The
procedure for extracting terms from a Wikipedia article is constructedfrom three steps. First, parts unnecessary for navigation are omitted.
Second, terms are extracted and weighted based on the frequency of theterm. Third, terms contained in <b> tags (bold elements) and <a> tags
(anchor elements) in the upper part of the articles are weighted morebecause they are assumed to be important.
In the example in Figure 3, the article “Java (programming
language)” is obtained. After terms are extracted from the article andweighted, “java” and “oak” etc., extracted from <b> tags, and “object,”“orient,” “programming,” “language,” “sun,” “microsystem,” etc., extracted
from <a> tags, are weighted additionally.
(2) Amazon
Amazon Web Service (AWS) offers Amazon data in XML format as aservice for developers. We use AWS to get precise data about book titles
and category data assigned to book titles. Our method searches AWS(only Japanese books) and gets three results. Among the bibliographic
information of these three books, texts inside <BrowseNodes> tags (thesecontain category data) and book titles are extracted. Terms are extracted
from these texts.
In the example in Figure 3, three XML data are obtained. These booktitles are “Easy Java,” “Design and Implementation of Java: Learning

SUGGESTING SUBJECT HEADINGS …

9from Source Code Reading,” and “Java parallel processing programming:architecture and latest API.” All are related to the Java programming
language. “computer,” “internet,” “programming,” “general,” “java,” etc.
are extracted from < BrowseNodes > tags of the first book “Easy Java.”(3) Google
Google Web APIs is a service for developers that offers Google data.
Our method searches Google (only Japanese pages) and gets five results.
To reduce the processing time, texts are limited to 1,000 characters fromwhich terms are extracted.
In the example of Figure 3, five Google search results are obtained.
These titles are “Free Download of Java Software,” “Sun Microsystems–
Java Technology,” “Java Language-Wikipedia,” “What is Java?-definition:
computer term dictionary e-word,” and “Download Java 2 SDK, Standard
Edition, v. 1.4.2 13 (J2SE).” All are related to the programming language
sense of ‘Java.’ Terms are extracted from these five texts that are limited
to 1,000 characters. “free,” “download,” “java,” and “software,” etc. areexamples of terms extracted from the first result.

Figure 3. Creating a query vector for user input “Java”.

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HIROSHI UEDA, HARUMI MURAKAMI and SHOJI TATSUMI

3.3.4. Merging terms
Terms are merged to create one vector. Terms extracted from
Wikipedia are weighted three times as follows:

W (t ) = 3Wi(t ) + A (t ) + G (t )W (t ) is the weight of term t, Wi(t ) is the number of the frequency ofterms extracted from Wikipedia, and A (t ) and G (t ) are those extracted
from Amazon and Google, respectively. In our preliminary experiment,
since information obtained from Wikipedia outperformed that from
Amazon and Google, we set a higher weight for indexing terms obtained
from Wikipedia.
In the example shown in Figure 3, the candidate query vector
becomes [object (30), orient (30), programming (29), language (27), java
(9), software (2)…].
3.3.5. Filtering terms
Indexing terms (see Section 3.1) are extracted from the query vector
candidate to create a query vector.
In the example of Figure 3, since “object,” and “oriented” are notincluded in indexing terms, they are deleted. When “Java” was input as aquery, a query vector finally becomes [programming (29), language (27),software (2), computer (1), internet (1)…].
3.4. Calculating similarity
The similarity between the query vector and the subject heading
vectors is calculated using a cosine measure, based on a vector spacemodel [11].
Given a query vector: q and subject heading vector si , the similarity

sim(q, si ) is defined as follows:∑ j =1 wqjwij ,sim(q, si ) =tt∑ j =1 wqj2 ∑ j =1 wij2t

SUGGESTING SUBJECT HEADINGS …

11

where t is the number of terms, wqj is the weight of t j in query vector q,
and wij is the weight of t j in subject heading vector si .
As stated in Section 3.1, indexing terms to create both vectors aredefined as nouns consisting of two or more characters extracted fromsubject headings.
3.5. Example
When “Java” was input as a query, the ten suggested subject
headings were “computer programming,” “programming (computer),”“Internet,” “computer graphics,” “computer art,” “computer music,” and“computer crime,” “personal computer,” “computer network,” and “Kanji
processing (computer)” on July 10th, 2005, as shown in Figure 1.
4. ExperimentCompared to OPAC mining, the main advantage of our research isthat it suggests subject headings when the user query seldom or neverappears in OPACs. Examples of such input include new words, jargon,
ambiguous words, etc. However, it is difficult to use these words to obtainappropriate subjects to evaluate our method’s usefulness. Since computer
terms are relatively new and concrete and it is therefore easy to evaluate
their meaning, we used them as input terms to obtain subjects easily.
e-words [12] is one of the most popular website dictionaries ofcomputer terms in Japan. A list of the top 100 accessed computer terms[13] on 9th July, 2005 was used for the experiments.
4.1. Experiment 1
Experiment 1 evaluated the basic usefulness of our method foruniversity students.
4.1.1. Method
The subjects were 41 undergraduate students. A questionnaire
displayed one computer term and ten subject headings suggested by our
method. Five questionnaires were allocated to each subject.
First, the subjects evaluated their familiarity with the term: 5: very

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HIROSHI UEDA, HARUMI MURAKAMI and SHOJI TATSUMI

well; 4: well; 3: neutral; 2: not very well; 1: not at all. We call this the“known” measure.
Next, they evaluated how much these suggested subject headings
were related to the associated term: 3: related; 2: neutral; 1: unrelated.
We call this the “related” measure. We judged data with related values of“3” as relevant to calculate “precision.” The experiment was conducted on
July 14th, 2005.
4.1.2. Results and discussion
The subjects rarely answered “1” or “2” as the known values for theterm related to the values of the subject headings. Therefore, we analyzed
the data whose known values were more than 2. As a result, related
values attached to 31 terms were examined. These 31 terms are shown in
Table 2 in Experiment 3.
Figure 4 shows the average related values at each rank of the tensuggested subject headings. The related value of the top rank (first) was
the highest (2.34). The related values were over 2.00 except for the eighth
rank.
Precision was 55% (21/38) at the top rank (first), 49% (56/114) in the
top 3, and 41% (150/370) in the top 10. The above results suggest thebasic usefulness of our method when we used computer terms.

Figure 4. Average related values at first to tenth rank in Experiment 1.

SUGGESTING SUBJECT HEADINGS …

13

4.2. Experiment 2
We examined the effectiveness of a combination of Wikipedia,
Amazon, and Google compared to separately using each information
source.
4.2.1. Method
We compared our method (d) with each source: (a) Wikipedia, (b)Amazon, and (c) Google. We used 31 terms defined by Experiment 1.
Questionnaires were identical to Experiment 1.
After the subject, a computer science graduate student read thedefinitions of the 31 terms and evaluated how much the suggested subject
headings were related to the terms, as in Experiment 1. She filled 31(terms)* and 4 (conditions) questionnaires. The precision was calculated
just as in Experiment 1.
The experiment was conducted on Dec 7th, 2005.

Figure 5. Average related values of four conditions at first to tenth ranks
in Experiment 2.
4.2.2. Results and discussion
A one-way ANOVA was performed for average related values for eachcondition, and there was significant difference between each condition

14

HIROSHI UEDA, HARUMI MURAKAMI and SHOJI TATSUMI( F (3, 36 ) = 12.86, p < .01). In Fishers LSD test, there were significant
differences between (d) our method and (a) Wikipedia, and (d) our methodand (c) Google (both p < .01) .
For the average related values of the top rank (first), our method (d)was highest (2.35) compared to (a) Wikipedia (1.90), (b) AWS (2.19), and(c) Google (1.90), as shown in Figure 5.
Table 1 outlines the precision results for each condition of the top 1,
3, and 10. Precision was 58% at the top rank, 55% at the top 3, and 43%
at the top 10. Our method was the highest among all conditions.
We found a combination of Wikipedia, Amazon, and Google useful for
query expansion to suggest subject headings.
Table 1. Precision of four conditions of top 1, 3, and 10 in Experiment 2
Top 1

Top 3

Top 10(a) Wikipedia

39% (12/31)

25% (23/93)

24% ( 73/310)(b) Amazon

58% (18/31)

47% (44/93)

38% (119/310)(c) Google

35% (11/31)

27% (25/93)

19% ( 60/310)(d) our method

58% (18/31)

55% (51/93)

43% (132/310)

4.3. Experiment 3
Experiment 3 compared our method to OPAC mining.
4.3.1. Method
The subjects were six information science postgraduate students.
The first author searched Osaka City University’s OPAC (keyword
search for Japanese books) using 31 terms from Experiments 1 and 2. We
checked ten search results for each term. For 10 of the 31 terms, thesearch obtained no subject heading, and for 4 of the 31 terms, only one
subject heading was obtained. We deleted these 14 terms, and used the
17 terms shown in Table 2. The number of subject headings extracted
from the top ten books for 17 terms ranged from 3 to 9. We extracted the
top 3 subject headings using the frequency for each term. In this

SUGGESTING SUBJECT HEADINGS …

15

experiment, we treat the above method as “pseudo-OPAC mining”
because the order of search results of Osaka City University’s OPAC isalphabetized by book titles and does not use ranking as RedLightGreen.
A questionnaire displayed one term and three subject headings
suggested by our method and pseudo-OPAC mining. 31 questionnaires
were allocated to each subject, who evaluated how much the suggested
subject headings were related to the terms, as in Experiments 1 and 2.
We used the same method for calculating precision as in Experiments 1
and 2.
The experiment was conducted on Jan 18th, Apr 19th, and Apr 24th,
2006.
Table 2. 31 Terms and their results in Experiment 3
Computer term
file-swapping software
application software
WMA
Window x64 Edition
MPEG4
MP3
DVD-RW
ADSL
proxy
trojan horse
WinMX
installCPU
format
HTTP
JPEG
CGI
domainblog
HTMLarchive
ISDN
Linux
WWW
cookieprotocolOS
Java
DVD+RW
Windows
serverNumber ofobtaind subject
headings

Number of search Number ofresults
obtained books
0
0
0
0
0
0
0
0
1
1
1
3
4
6
4
7
11
13
21
31
34
38
47
58
92
120
145
188
286
356
596

0
0
0
0
0
0
0
0
1
1
1
3
4
6
3
7
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10

Note
0
0
0
0
10 terms (no0subject heading is0
obtained.)
0
0
0
0
1
4 terms (only one
1subject heading is1
obtained.)
1
3
3
7
8
7
4
4
17 terms (more5than one subject
3
heading is
6
obtained.)
9
8
4
6
4
6
8

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HIROSHI UEDA, HARUMI MURAKAMI and SHOJI TATSUMI

Figure 6. Average related values at first to third ranks in our method
and pseudo-OPAC mining in Experiment 3.
4.3.2. Results and discussion
For 8 (26%) of 31 terms (e.g., file-swapping software, application
software, etc.), there were no search results. For 2 terms (6%) (e.g., proxy,
trojan horse), no subject heading was contained in any search results. In
these cases, neither pseudo-OPAC mining nor OPAC mining could
suggest subject headings.
For 4 terms (13%), pseudo-OPAC mining could only suggest one
subject heading. In this case, our method outperforms pseudo-OPAC
mining. We assume that our method might be better than OPAC mining.
For the remaining 17 terms, the results are as follows. Figure 6
shows the average related values at each rank of the three suggested
subject headings for both our method and pseudo-OPAC mining. The
average related value of the top rank was 2.14, the second rank was 2.10,
and the third rank was 2.06 in our method. For pseudo-OPAC mining, thetop rank was 2.14, the second rank was 2.29, and the third rank was 1.90.
In precision, our method was 42% (64/153) and pseudo-OPAC mining was
42% (64/153). There was no significant difference between our method
and pseudo-OPAC mining.

SUGGESTING SUBJECT HEADINGS …

17

The above results show the following: (a) our method can suggest
subject headings when pseudo-OPAC mining cannot; and (b) our method
and pseudo-OPAC mining are nearly equal when we use computer terms.
Thus our method can serve as an alternative to pseudo-OPAC mining.
When we qualitatively analyze the results, a merit of our method is thenewness of the suggested terms. In pseudo-OPAC mining, the suggested
subject headings tended to include older terms at a glance. Subject
headings once assigned to collection in OPACs are seldom deleted, so old
subject headings remain forever. Old books have old subject headings,
however new they may have been when the books were written.
5. Related Work and Discussion
5.1. Related work
The research described in this paper is a part of a project called
Subject World [14], which visualizes such concepts as subject headings
and classifications and enables users to explore these concepts and search
OPACs. We explore methods for suggesting BSH4 and NDLSH subject
headings, NDC9 [15] classifications, and so on when a pattern-matchingalgorithm fails. This is the first paper to describe our idea, and the threeexperiments that suggest BSH4 subject headings are described.
The objectives of the existing systems that suggest subject headings
are divided into two. One helps users modify their queries. Our researchis classified here. Another helps indexers who assign subject headings tocollections. A typical example is to suggest MeSH subject headings in
MEDLINE based on paper abstracts, which helps indexers assign
appropriate subject headings for the abstracts [16, 17, 18].
Concerning methods, existing systems that suggest subject headings
are based on pattern-matching or OPAC mining, as stated in Section 1.
RedLightGreen is an example of OPAC mining.
Webcat Plus [19] displays terms included in book titles that areincluded in search results. It is identical to RedLightGreen in the sensethat it uses OPAC mining, but different because it displays natural
terms, not a controlled vocabulary like subject headings.

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HIROSHI UEDA, HARUMI MURAKAMI and SHOJI TATSUMI
Many research results support the use of natural terms drawn from

Web information sources instead of Data Mining. In addition, there ismuch research concerning automatic thesaurus construction using Web
information sources (e.g., [20, 21]). Our method does not suggest natural
terms, but uses them as sources for query expansion to suggest subject
headings.
There are many query expansion studies for Web document retrieval
using Web information sources (e.g., [22, 23]). We use Web information
sources for query expansion to obtain subject headings to seek OPACs.
5.2. Discussion
We investigated a method to suggest BSH4 subject headings for
subject searches in OPACs. Most existing systems use a patternmatching algorithm. If it fails, no subject heading is displayed. Some
research systems display subject headings using OAPC mining, which isbased on the ranking of search results. However, few systems haveadopted ranking of search results. How search results are ranked at anindividual library is unknown. Moreover, due to budget shortages, it isdifficult to implement OPAC mining even if it becomes clear how to rank
terms in individual libraries. We pointed out two advantages of our
method: (1) availability for any library without customizing OPACs, and(2) ability to suggest subject headings when a query string is not includedin the OPAC’s bibliographic information. Here we discuss the advantages
and disadvantages of our method compared to OPAC mining.
The biggest advantage of our method is that it can suggest subject
headings when OPAC mining cannot. This happens when user queries
are not included in bibliographic information (mainly book titles) in
OPACs. One typical example is when user queries consist of relatively
new words. It takes several months from the birth of a neologism before it
is included in book titles. It also takes a few more months from thepublication of such a book to its registration in OPACs. In contrast, it
may take just a few days before neologisms appear on Google andWikipedia.
Next, the size of libraries, in other words, the number of collections, isdifferent. In libraries with small collections, it becomes more difficult toSUGGESTING SUBJECT HEADINGS …

19suggest good subject headings because the number of search results issmall. Our method has an advantage when the size of libraries is small.
Third, since we need not rely on vendors to customize OPAC package
software, our method’s implementation is cheaper than OPAC mining, as
stated in Section 1.
Fourth, the results of Experiment 3 showed that our method cansuggest relatively new words better than OPAC mining. Since thesewords are more familiar than words suggested by OPAC mining, it may
be useful to support end-user searching.
Finally, we will describe one example in which our method cansuggest better subject headings than pattern-matching algorithms. When“orz” (a Japanese emoticon representing a kneeling or bowing person) is
an input query, OPAC hits a German document that contains “ORZ” in
its title. In contrast, our method suggested such subject headings as“modern term” and “current term.”
There are some disadvantages in our method. First, the general
precision of subject headings in our method is assumed to be worse than
OPAC mining. In Experiment 3, the evaluation of our method nearly
equals pseudo-OPAC mining. OPAC mining is assumed to be better thanpseudo-OPAC mining.
Second, our method cannot deal with ambiguity, as stated in Section
2. If a user wants subject headings related to “the island of Java” in theexample, our method fails.
Future work is listed below. First, we must improve our method. For
example, we only weighted terms extracted from Wikipedia. We must
examine weight terms extracted by Amazon and Google. We expandeduser queries using Web information sources but did not expand subject
headings. We need to expand subject headings using Web information
sources and test the method. Second, we need to examine different kindsof data as user queries. Finally, we must examine the effectiveness ofsubject searches using suggested subject headings.
6. Conclusion
We proposed a method that suggests subject headings based on user

HIROSHI UEDA, HARUMI MURAKAMI and SHOJI TATSUMI

20

queries when a pattern-patching algorithm fails to locate subject searches
for OPACs. We combined information obtained from Wikipedia, Amazon,
and Google for query expansion.
Our method has two advantages: (1) availability for any librarywithout customizing OPACs, and (2) ability to suggest subject headings
when a query string is not included in OPAC’s bibliographic information.
Three experimental results using computer terms revealed thefollowing. (1) Suggested subject headings were related to the input term.
(2) Suggested subject headings were better when we used a mixture of
Wikipedia, Amazon, and Google than when we just used one of them. (3)Our method can suggest subject headings when OPAC mining cannot.
We conclude that our method can serve as an alternative whenpattern-matching algorithms fail.
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