RELATED WORD EXTRACTION FROM WIKIPEDIA FOR WEB
RETRIEVAL ASSISTANCE
Kentaro Hori
Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka City, Fukuoka, Japan

Tetsuya Oishi, Tsunenori Mine, Ryuzo Hasegawa, Hiroshi Fujita, and Miyuki Koshimura
Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan

Keywords:

Wikipedia, Web retrieval.

Abstract:

This paper proposes a web retrieval system with extended queries generated from the contents of Wikipedia.By
using the extended queries, we aim to assist user in retrieving Web pages and acquiring knowledge. To extract
extended query items, we make much of hyperlinks in Wikipedia in addition to the related word extraction
algorithm. We evaluated the system through experimental use of it by several examinees and the questionnaires
to them. Experimental results show that our system works well for user’s retrieval and knowledge acquisition.

1

INTRODUCTION

In recent years, many people can access Web very
easily thanks to the vast spread of internet as well as
the availability of convenient search engines.
For instance, Google1, Yahoo2, and Goo3 arecommonly used. Giving a few keywords, these systems retrieve such Web pages that users want to seefrom among the huge databases residing on the internet. Google, in particular, successfully presents us themost suitable pages on the first page of the retrieval
results by applying the PageRank algorithm(Page andLopes, 1998) which evaluates relevance of pages
based on page links.
Even though, since the Web sources are so enormous and constantly increasing, it is often the casethat we are not satisfied with the results given bythem.
As for a method to improve the retrieval results,
conjunctive query can be used. If you give multiple
keywords all of which are relevant to your query, youwill be able to obtain better retrieval results comparedto those obtained for a single keyword. However, it
may not always be the case that every keyword you
give is appropriate for your intended query. Even if
1 http://www.google.co.jp/
2 http://www.yahoo.co.jp/
3 http://www.goo.ne.jp/

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the keywords are all relevant to the query, you may
not be always satisfied with the retrieved results. Thisis because search engines will look over such Web
pages that are relevant to the query, but do not contain any keyword in the query. Also, if the area within
which you want to look for some information is notvery familiar to you, you will be unable to give appropriate keywords for your query. Thus, conjunctive
query is not enough.
To solve the problem, there have been many works
on such systems that can offer keywords relevant
to one given by the user, such as (Masada et al.,2005)(Mano et al., 2003). In order to offer related
keywords, these systems require some additional information other than the keyword given by the user.
One method uses, as the additional information, theretrieved results themselves that are obtained for the
initial query(Murata et al., 2008).
For instance, pseudofeedback methods take top
ten Web pages in the ranking of the results as the relevant documents, and other Web pages as irrelevant
documents. Then, some related words are extracted
from the above classified documents. The advantageof the method is that it does not add users burden,
and that it always returns some relevant results. However, these days, advanced services on the internet
such as blogs, bulletin boards, and online shopping
are becoming sources of tremendous information thatHori K., Oishi T., Mine T., Hasegawa R., Fujita H. and Koshimura M. (2010).
RELATED WORD EXTRACTION FROM WIKIPEDIA FOR WEB RETRIEVAL ASSISTANCE.
In Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Agents, pages 192-199
DOI: 10.5220/0002728801920199
Copyright c SciTePress

RELATED WORD EXTRACTION FROM WIKIPEDIA FOR WEB RETRIEVAL ASSISTANCE

would appear in the retrieval results as irrelevant ones.
If these are ranked higher in the result list, and used as
the seemingly relevant documents for the pseudofeedback method, it would surely be the case that significant amount of irrelevant information is included in
the final results.
Therefore, we present a new method and a system
for related word extraction that uses Wikipedia4 as theinformation source. Wikipedia is a well known online
encyclopedia that is well organized with rich contents,
words, and internal links. Moreover, since it can easily be updated by anyone, many researchers have been
giving attention to it. Using Wikipedia, systems will
be able to remove irrelevant information from theirretrieval results, and improve the accuracy of the results especially when the area of interest is unfamiliar
to the user.
The problem with Wikipedia is as follows: it may
occur with high probability that the keyword givenby the user is absent in it, since the total amountof information contained in Wikipedia is quite small
compared to those contained in whole Web pages on
the internet, and the users query may be exotic to
Wikipedia. So, we categorize queries in advance, andbuild a most useful system for a user who wants to
use the Web as if it were a large virtual dictionary.
The usefulness is evaluated on the basis of accuracy
improvement and the quantity of information that is
new and interesting to the user.

2

the Web that includes words from so many fields ofgeneral interest. Yet these system would have somedifficulty to show relevant words when the user reallywants to obtain information quite new to him. This isbecause there should be few information in the user
files which are supposed to suggest words about alienculture, unseen incidents, and unfamiliar history, etc.
As for work concerning Wikipedia, Nakayama(Itoet al., 2008) et.al. have succeeded in constructing
the association thesaurus dictionary for extracting related words from a given query. They calculate cooccurrence of words that have links to other pages interms of the relatedness between those linked pages.
However, as Wikipedia’s internal links are provided
only arbitrarily by the author of that specific page,some pages contain many links, others very few, oreven no internal link at all. In such a case, their system will not work. On the other hand, our system
uses the related word extraction algorithm of our own
where all words, with or without linked words, aretaken into account, and free from the above problem.
The detail of the algorithm is described in section 5.

3

SYSTEM OVERVIEW

Figure 1 shows an overview of our system.

RELATED WORK

Bedsides pseudofeedback, there are two more feedback methods called explicit feedback and implict
feedback. The former depends on the users evaluationof documents, whereas the latter automatically collects documents by analyzing users operations suchas scroll, click and zoom.
Another system uses personal information that reflects a user profile as an auxiliary information(Sieget al., 2007)(Yoshinori, 2004)(Qiu and Cho, 2006)D
Concretely a user profile will be derived from schedules or some database containing his/her interests orfavorites. These help the system to offer related words
that meet his/her intention. For instance, when a user
wants to know about the weather forecast, the system would examine just the regions which are near theplace where he/she lives. As far as the user wants suchinformation that is very specific to his/her interest, it
should be more appropriate to derive related words
from user profiles than to find out them from among
4 http://ja.wikipedia.org/

Figure 1: Image of system.

First, the user inputs a query, refereed as the initialquery, to the system. The goal of our system is to findout related words for the query.
Second, the system collects documents called relevant documents that are related to the initial queryand are used as sources for extracting related words.
Our system takes Wikipedia as a source of relevant documents and extract some paragraphs from
Wikipedia pages that are related to the initial query.
The details will be described in section 4.
Third, the system performs morphological analysis on the above collected relevant documents by

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ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligencemeans of MeCab5 . With MeCab it squeezes only
nouns from the documents, then discards words like“something” or “anything” that are registered in thesystem as unnecessary in advance. Furthermore, aspecial rule is installed to it so that a connected word
like “graduation thesis” will not be broken apart into
two nouns “graduation” and “thesis.”
Finally, the system calculates for each of theabove obtained words an estimate value as the related word. There have been many works on theway to do this. For instance, RSV (Robertson Selection Value)(Robertson, 1990) algorithm considers
such words as more important that appear more often(or less often) in relevant documents than in irrelevant
documents. RSV is not very suitable for our system,
since there is basically no way of providing irrelevant
documents. Therefore, we use a related word extraction algorithm(Kuramoto et al., 2007) of our own that
calculates the value of a word based on the notion of
distance between words in a document. We also makesome correction of the value of words on the basis ofinternal links within Wikipedia. The details are described in section 5.

4

OUR NEW METHOD

We use Wikipedia as the relevant document for oursystem. The reason why we chose Wikipedia is asfollows.
• Wikipedia is one of the largest encyclopedias
on the Web having more than 590,000 Japanesewords (as of June, 2009).
• Its content is very rich6 (Thomas, 2006).
• A number of people can easily access, even modify its content and make it always up-to-date.

4.1 Extraction Method
The way of collecting relevant sentences slightly differs according to the number of keywords in the initialquery.
Initial Query with One Keyword
Let the initial query be “A”. The system retrieves
Wikipedia to find an article on “A”. It extracts
the highest ranked page of the retrieved result andsearches for the paragraph where “A” appears in the
page. The extracted paragraph is used as a relevant
sentence.
5 http://mecab.sourceforge.net/
6 http://woy2007.sbcr.jp/

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Initial Query with Two or More Keywords
Use the following elements.
• Qi (i = 1, · · · , n) :Given query• PQi (i = 1, · · · , n) :Highest rank of result page of
Wikipedia retrieved by Qi• Extract(P, Q) :Extract from “P” the paragraphwhere “Q” appears.
• ExSentence :Extraction result
Extracted sentence are expressed by the following expressions.
nn

ExSentence = ∑ ∑ ( j 6= k)Extract(PQ j , Qk )k=1 j=1+Extract(PQ1∧Q2 ∧···∧Qn , Q1 ∧ Q2 ∧ · · · ∧ Qn )Let the initial query be “Q1 Q2 ”. The system retrieves Wikipedia with three queries “Q1 ”, “Q2 ”, and“Q1 Q2 ”. It collects the highest ranked page for eachquery. Let us call them “PQ1 ”, “PQ2 ”, and “PQ1 Q2 ”,
respectively. From “PQ1 ” the paragraph where “Q2 ”
appears is extracted. From “PQ2 ” the one where “Q1 ”
appears is extracted. From “PQ1 Q2 ” the one where
both “Q1 ” and “Q2 ” appear is extracted.
For instance, when the system retrieves with query“Omelette seasoning”, “Omelette” is first retrieved
with Wikipedia. Since the page of “Omelette” becomes a hit, the system searches for a paragraph withthe word “Seasoning” in “Omelette” page. When it isfound, the paragraph with “Seasoning” is extracted asa relevant sentence. Next, when the system retrieves
with “Seasoning”, the page of “Seasoned laver” becomes a hit. However, nothing is extracted because
there is no paragraph where word “Omelette” exists. Finally, it retrieves by “Omelette seasoning”, andthe page of “Omelet” becomes a hit. The paragraphwhere both “Omelette” and “Seasoning” words exist
in this page is extracted. In this way, the extracted
paragraphs are assumed to be relevant sentences, andthe morphological analysis is performed. Figure 2
shows how the sentences containing the word “seasoning” are searched for, and the corresponding paragraph is extracted.
Although for initial queries with three or more
keywords, a paragraph can be extracted as a relevant
sentence as well, we did not experiment. This is because such a query is issued with a clear purpose sothat we can get enough results, and the extraction timeincreases greatly by the combination.

RELATED WORD EXTRACTION FROM WIKIPEDIA FOR WEB RETRIEVAL ASSISTANCE

extracted words are arranged in appearance order. In
case a word appears more than twice, these occurrences are considered to be different.

5.2 Evaluation of Word in Text
Evaluation of words in the text T is performed as follows:
1. Calculate basic value BV (sh ) of sh ( j = 1, · · · , n)with ki (i = 1, · · · , m) as a criterion.
2. Smooth BV (sh ).
3. Calculate a final value V (t j ) using word frequencies.
Figure 2: Actual extraction image.

5.2.1 Calculating BV (sh )5

RELATED LEVELCALCULATION OF WORD

5.1 Outline of the Algorithm
The algorithm extracts, from words appearing in the
text T , a group of words that are related to a given setof keywords K, and thus are expected to be importantfor K. Based on the distance from a keyword, we
evaluate each word in K or T , and form a set of words
to be output as a result of related word extraction.
The idea behind this algorithm is a distance between words. Concretely, it pays attention to the order of words that appear in sentences, and it considers
the words that appears near to A to be relevant to A.
5.1.1 Notation
We define some symbols as follows:• K: a set of keywords that give a basis for extracting related words.
• T : a text to be given degrees of relation with K.
• ki (i = 1, · · · , m): m keywords appearing in K,
where the keywords k1 , k2 , · · · , km appear in thisorder.
• sh (h = 1, · · · , n): n sentences appearing in T ,where the sentences s1 , s2 , · · · , sn appear in this order.
• Fki (sg )(g = 1, · · · , n): Fki (sg ) = 1when sg contains
key word ki , otherwise Fki (sg ) = 0.
• t j ( j = 1, · · · , o): o words appearing in T , where
the words t1 ,t2 , · · · ,tn appear in this order.

This algorithm evaluates the word based on key word
group K focusing on the distance between words thatappear in K and text T , and first calculates the evaluation value that gives the basis.
We define BVki (sh ) as the score of sh with respect
to ki .
n

BVki (sh ) = ∑ (n − |g − h|)Fki (sg )

(1)

g=1

Here, |g − h| is a distance of sh and a sentencein which ki appears. When k p appears in sq , |g −h| = |q − q| and Fk p (sq ) = 1, so BVk p (sq ) = n. For
the next sentences sq+1 and sq−1 , the distances are|q − (q + 1)| = 1, |q − (q − 1)| = 1, respectively, andthus BVk p (sq+1 ) = BVk p (sq−1 ) = n − 1. Note that
BVki (sh ) takes a larger value, as sh gets closer to thesentence in which ki appears.
After having calculated every BVki (sh ) (i =1, · · · , m), the algorithm calculates BV (sh ) with thefollowing formula.
m

BV (sh ) = ∑ BVki (sh )

(2)

i=1

An example that calculates BV (sh ) is shown in Table 1, where keyword A appears twice in text T .
Table 1: An example of scoring sentences using distance.
keyword K
text T
BVA (sh )BVB (sh )BV (sh )

A
AFB
8
5
13

B
ED
8
4
12

AFC
8
3
11

FE
6
2
8

DE
4
1
5

t j represents a noun word extracted by performing
morphological analysis of words in the text T. The

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5.2.2 Smoothing of BV (sh )The above way of determining BV (sh ) is unfair when
considering the position of sentence sh in T . Forsh (h = 1, n) appearing at the edge of T , the valueof BVki (sh ) ( j = 1 or n) ranges from 1 to n (1 ≤BVki (sh ) ≤ n), while for the word appearing in thecenter of T , it ranges from n/2 to n (n/2 ≤ BVki (sh ) ≤n).
Thus, the expected values of BV (sh ) differ depending on the position h at which sh occurs. As
the word appearing in the center of T will be givena larger value, a fair evaluation cannot be achieved.
To remedy this problem, we smooth the obtained
BV (sh ) using EBV (h), the expected evaluation valueof sh at position h. EBV (h) is calculated as follows:1n(n + 2h − 1) − 2h(h − 1)(3)2nwhere n is the total number of sentence occurrences in
T . Let EBV (sh ) be the evaluation value after smoothing. It is calculated using the following formula.
EBV (h) =EBV (sh ) =BV (sh )EBV (h)(4)

Table 2 shows a process of getting EBV (sh ) fromthe given text T . Since EBV (h) is the expected evaluation value at position h, the value for the position
nearer the center (around h = n/2) becomes larger.
Naturally, both left and right sides are symmetricalwith respect to the center having a peak. As seen in
Table 2, by smoothing of BV (sh ) with EBV (h), unfair
evaluation in BV (sh ) is well remedied.
Table 2: An example of calculating EBV (sh ) using the expected value EBV (h) at occurrence position h.
keyword K
text T
BV (sh )EBV (h)EBV (sh )

A
AFB
13
3
4.33

B
ED
12
3.6
3.33

AFC
11
3.8
2.89

FE
8
3.6
2.22

DE
5
3
1.67

5.2.3 Calculating VT (t j )In addition to the evaluation based on the distance
between sentences, we take into consideration theconcept of Term Frequency that is often used in the
TF/IDF method. In other words, the words which appear larger number of times in a text T should be more
important.
First, we compute the average AveEBV (t j ) of the
expected evaluation values EBV (t j ), for word t j thatappears several times.

196

For instance, if sentence sa and sentence sb (ab)are identical with tc , AveEBV (tc ) = (EBV (sa ) +EBV (sb ))/2.
Of course, for the word t j (t j is a word in sh ) thatappears only once in T , AveEBV (t j ) = EBV (sh ).
Moreover, we compute the weight WT (t j ) using
the t f value of word t j as follows.
WT (t j ) = 1 +t f (t j )logt f (t j )n(5)Here, t f (t j ) is the number of occurrences of word t j
in the text T and n is the total number of occurrencesof words in T . Then by using AveEBV (t j ) and WT (t j ),we calculate the evaluation value VT (t j ) of t j in T as
follows.
VT (t j ) = AveEBV (t j ) ∗ WT (t j )(6)

We assume that VT (t j ) is the evaluation value ofword t j in text T in this algorithm. Table 3 shows
a process of calculating VT (t j ). Because word C appears twice in text T , t f (C)is 2. As for other words,
the t f value is 1. Since WT (t j ) is calculated using t fvalues, the t f value is 1 for words which appear once,
and takes a value greater than 1 for words which appear twice or more.
Then the final evaluation value EBV (C) is calculated. We assume that the words in T are related to K
in the order of F, A, B, E, D and C.
Table 3: VT (t j ) of each words.
keywordKtextT
EBV (sh )

A
AFB
4.33

B
ED
1.67

AFC
2.11

FE
2.22

DE
2.67

word

A
3.61
2
1.28
4.62

B
4.33
1
1
4.33

C
2.11
1
1
2.11

D
1.67
2
1.28
3.20

E
2.50
3
1.66
4.00

AveEBV (t j )t f (t j )WT (t j )VT (t j )

F
2.56
3
1.66
5.23

5.3 Correction by Internal Links of
Wikipedia
5.3.1 Outline
In addition, we pay attention to the hyperlink(internallink) to another article in the article on Wikipedia.
This internal link can be arbitrarily set by the authorof the article, and the word to which an internal link
is set can be considered to be deeply related to the article, and thus an important word that you should pay
attention to. However, since an internal link can be

RELATED WORD EXTRACTION FROM WIKIPEDIA FOR WEB RETRIEVAL ASSISTANCE

set arbitrarily it may not be related to the initial query.
Then, we calculate a degree of relevancy to the initialquery.
5.3.2 Computational Method• LW f ( f = 1cp): a word for which an internal linkis specified• RW S(LW f ): the evaluation value of LW f calculated by the related word extraction algorithm

Actually, informational queries are the principal
subject of our system that are supposed to be treatedmost effectively. Therefore, we concentrate on thisclass of queries for our subject in the following experiments.
About query classification, we plan to use the automatic classification technology like (Fujii, 2007) in
the future, but we manually classified the queries thistime.

• LW f (ki ) : frequency that keyword ki appears in thearticle to which the internal link of LW f points.

7

A final evaluation value is calculated from theabove-mentioned three items as follows.

We evaluate our system by comparing its results to
those obtained in [Goo] and [Web5]. [Goo] is a searchengine operated by NTT Resonant. We compare theaccuracy of results by our system to those obtained by[Goo]. [Web5] is a data obtained for the Web pages
which are ranked in top five of the retrieval results for
an initial query and taken as relevant documents(Oishi
et al., 2008). We examine the accuracy of resultsin terms of the difference of relevant documents, between those of [Web5] and Wikipedia pages used inour system. The effectiveness of the related word
extraction algorithm itself has been demonstrated by
Oishi et.al.(Oishi et al., 2008) The accuracy of the results is measured in terms of MAP (Mean Average
Precision). AP (Average Precision) is the average ofratios of the number of documents that user judges
good to the number of whole documents provided as
the result for the initial query. MAP is an average of
AP over a set of queries.
The AP is defined as follows:• WikiEX(LW f ) = log(Σmi=1 LW f (ki ) ) + 1• WordScore(LW f ) = RW S(LW f ) ∗ WikiEX(LW f )Since preliminary experiments show that LW f (ki )ranges from 0 to 100 or more, we took the logarithm
to reduce an extreme evaluation value difference. We
take this WordScore(LW f ) to be a final evaluation
value. For a word without having an internal link, its
evaluation value of the related word extraction algorithm is assumed to be a final evaluation value.

6

CLASSIFICATION OF QUERY

We classify queries into the following three classes on
the basis of investigation by Andrei(Broder, 2002).
1. Navigational query
2. Transactional query
3. Informational query
A navigational query is the one for which just asingle page is to be sought after. For instance, bygiving “Google” as a query, most probably the user
would like to see the Google’s top page. This typeof queries need not to be augmented, as any existing
search engine can easily present satisfactory results
for them.
A transactional query is the one at which the user
would start taking some action such as shopping,
downloading, and looking for maps. For this type of
queries, it should be effective to perform query augmentation based on user profiles as described in section 2.
An informational query is the one about which theuser wants to obtain some knowledge. When the user
has a word unfamiliar to him/her and wants to know
about something concerning it, he/she would make aquery about the word to obtain its related information.

EXPERIMENTS∑Lr=1 I(r)P(r)R
Where R is the total number of relevant documents; L is the number of results retrieved by a system. In this experiment, we set L to 10 and R to
10, too; I(r) is 1 if the r-th ranked document is relevant, and 0 otherwise; P(r) is count(r); count(r) is thernumber of relevant documents among r documents returned by the system.
Figure 3 shows the result.
80 queries are used for this experiment. We concentrate on how much the accuracy of the retrieval results is improved compared to those obtained by anexisting engine. First, we calculate AP for the results given by Goo. Next, each query is classified
into 11 classes according to the value of AP, first 0,
second 0.1 or less, and so on, and finally 1.0 or less.
Then, we calculate MAP for each class of queries andfor each method being compared. We can say thata MAP value given by any method for those queries
Average Precision =197

ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence

Table 4: query:iPS cell.

GoogleProblem
Patentwiki
Kyoto University
Regenerative medicine
Nobel prize
Yamanaka
Bayer
Embryonic stem cell

Proposal
Gene
Establishment
Group
Embryonic stem cell
Mouse
Success
PossibleHuman
introduce

Table 5: query:NP complete problem.
Figure 3: MAP for each AP of Goo.

having AP of less than 0.2 represents the accuracy ofthe method when Goo gives poor results, and that any
MAP value for those queries having AP of less than
1.0 represents the overall accuracy of each method.
When Goo’s AP is high, or the initial retrieval result is satisfactory for a user, the accuracy will hardly
be improved. This is because adding a word to a very
good query would not change the initial result, or evenworse, degrade the result. As Goo’s AP decreases, theaccuracy of our method increases and becomes higher
than that of Goo. This demonstrates that the query
augmented by our method can reflect more clearly theusers intention of the retrieval than the initial querydoes. The more accurate query is issued, the more
unnecessary information is excluded from the result.
Note also that many good related words can be extracted for informational queries, as a result of thefact that Wikipedia is a kind of encyclopedia. On theother hand, in another experiment where queries are
not classified, our method shows poorer results. Although our method shows poor accuracy also for thedata where Goo’s AP is less than 0.1, we think it isjust deviation due to too small number of the test set.

GoogleExamplerary. On the other hand, our method give words thatare more technical and relevant to the essential meaning of the query word. As for another example, table 5 shows the results for the query “NP complete
problem”, which is well known to people from theengineering field whereas unfamiliar to ordinal people. Google shows up just a single word as its related
word. This is because few people give the query to
Google, and the retrieval records are too small to extract related words. On the other hand, our method
can provide many related words since Wikipedia has
a good text concerning the NP complete problems.

98

CONCLUSIONSDISCUSSION

A function similar to our related word presentation isprovided by Google. This is based on the retrieval
records of all users in the world. We compare therelated words given by Goole and those obtained byour method.
Table 4 shows the results for the query “iPS cell”
for which a Japanese researcher won the Lasker prize
and was much taken about. Google shows up words
like Kyoto University and Yamanaka, who is the winner of the Novel prize, as the related words for thequery word “iPS cell”. These words are just tempo-

198

Proposal
Class NP
Polynomial
NP difficult
possible to return
Satisfiability problem
possible to reduce
Discovery
Theorem
Cook

Using Wikipedia as a source of relevant documents,
better words can be extracted compared to those obtained by using retrieval results of conventional searchengines like Goo. The results of our system would
be much better from the viewpoint of user in acquiring knowledge. Also, compared to the related words
recommendation by Google, better words can be extracted especially when the initial keyword itself isseldom used for a query, or it is just temporarily used
in a specific topic. This is because Google depends on
the record obtained from a large number of retrieval
results for anonymous people, whereas ours are based

RELATED WORD EXTRACTION FROM WIKIPEDIA FOR WEB RETRIEVAL ASSISTANCE

on information that is more specific to the user.
This time, the extraction of the related word waslimited only to Japanese because we had experimented by using goo, Mecab, and Japanese version
Wikipedia. In order to implement it with other languages, it is necessary to use Google as a searchengine, and also a morphological analysis tool andWikipedia for the other language being targeted.
Moreover, the algorithm used is designed based on
the feature of a Japanese syntax. So, when applying to other languages, we need the adjustmentof the parameters or the other algorithms such as
Strube(Strube.M and Ponzetto.S, 2006).
We are going to implement a user friendly interface such that the user can retrieve by choosing therelated words offered by the system, and collect evaluation results.
In future research, we are going to give attention
to another characteristic of Wikipedia that it tends todrive users to other area of interest as he/she goes
through the text in it. This will open up a new application of the related word extraction method to somekind of knowledge discovery.

Oishi, T., Kuramoto, S., Mine, T., Hasegawa, R., Fujita, H.,
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Stanford University.
Qiu, F. and Cho, J. (2006). Automatic identification ofuser interest for personalized search. In International
World Wide Web Conference. ACM.
Robertson, S. (1990). On term selection for query expansion. In Journal of Documentation, 46, 4, pp. 359-364.
Sieg, A., Mobasher, B., and Burke., R. (2007). Web searchpersonalization with onotological user profiles. In In
Proc. of CIKM ’07. CIKM.
Strube.M and Ponzetto.S (2006). Wikirelate! computing semantic relatedness using wikipedia. In AAAI pp.14191424.
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ACKNOWLEDGEMENTS
This work was supported by KAKENHI(21500102).

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