Integrating Multiple Internet Directories by Instance-based Learning
Ryutaro Ichise, Hiedeaki Takeda, Shinichi Honiden
National Institute of Informatics
2-1-2 Hitotsubashi Chiyoda-ku, Tokyo, 101-8430 Japan{ichise,takeda,honiden}@nii.ac.jp

Abstract
Finding desired information on the Internet is becoming increasingly difficult. Internet directories
such as Yahoo!, which organize web pages into hierarchical categories, provide one solution to thisproblem; however, such directories are of limited
use because some bias is applied both in the collection and categorization of pages. We propose
a method for integrating multiple Internet directories by instance-based learning. Our method provides the mapping of categories in order to transfer documents from one directory to another, instead of simply merging two directories into one.
We present herein an effective algorithm for determining similar categories between two directories via a statistical method called the κ-statistic.
In order to evaluate the proposed method, we conducted experiments using two actual Internet directories, Yahoo! and Google. The results show thatthe proposed method achieves extensive improvements relative to both the Naive Bayes and Enhanced Naive Bayes approaches, without any text
analysis on documents.

1

Introduction

The World-Wide Web (WWW) is now used not only by computer specialists, but by all sorts of people, from children
to businessmen and businesswomen, which has resulted in
an enormous quantity of web pages available on the Internet. This makes finding pages containing the desired information rather difficult. Search engines are requisite for finding the desired information. In general, current search engines perform their functions via one of two methods: a keyword search and a directory search. A keyword search engine performs searches by means of user-specified keywords.
Keyword-based search engines like Google can reliably findpages containing the specified keywords. However, if theuser does not have a thorough knowledge of his/her searchdomain and cannot choose the appropriate keywords, thesearch engine is useless. In such a situation, directory-based
search engines do a good job. In a directory-based searchengine, pages are evaluated and organized by humans before

being registered in the search engine’s archive. Directorybased search engines provide knowledge of WWW navigation. Users reach the desired information by going up anddown the directories.
Although pages are carefully selected and well-organized,a single Internet directory is not sufficient because the searchengine tends to have some bias in both collecting and categorizing pages. In order to solve these problems, we herein propose a method that coordinates multiple Internet directories
by estimating directory similarities. The proposed method
does not simply merge multiple directories into a larger directory, but instead determines the relationship between directories. Many such public Internet directories exist. Some aredesigned to cover wide domains, while others focus on special domains. Such Internet directories are difficult to merge.
Moreover, these directories should not be integrated, because
the existing differences in concept hierarchies among the directories is important when selecting and using directorybased search engines. In this paper, we propose a method
to solve this problem by determining the rules for mapping
categories in a directory to those in another directory. Our
solution can be applicable not only to the Internet directory
problem, but also to the integration of web marketplace catalogs [Agrawal and Srikant, 2001]1 and ontology integrationin general.
The remainder of this paper is organized as follows. In
Section 2, we define the problem of coordinating multiple Internet directories and discuss related studies. In Section 3,
we propose a new machine learning method for the abovementioned problem. Next, in Section 4, we compare the performance of the proposed method to that of the Enhanced
Naive Bayes approach [Agrawal and Srikant, 2001] for an
integration problem using real Internet directories. Finally, in
Section 5, we present our conclusions.

2 Integration of Multiple Internet Directories
In order to state the problem, we introduce a model for theInternet directories we intend to integrate. We assume there
are two Internet directories: a source directory and a target
directory. The documents in the source Internet directory are1
For example, the integration of a distributor’s catalog and a web
marketplace.

Source
Internet
Directory

TargetInternet
Directory

SD

TD
D2
D1

?
D2

Dx

D1

Figure 1: Problem statement
expected to be assigned to categories in the target Internet directory. This produces a virtually integrated Internet directoryin which the documents in the source directory are expected
to be members of both the source and target directories. This
integrated directory inherits the categorization hierarchy fromthe target Internet directory.
The Internet directory model for the source and target is asfollows:• The source Internet directory, SD contains a set of categories, Cs1 , Cs2 , . . . , Csn , that are organized into an “isa” hierarchy. Each category can also contain documents.
• The target Internet directory TD contains a set of categories, Ct1 , Ct2 , . . . , Ctm that are organized into an “isa” hierarchy. Each category can also contain documents.
The proposed model permits documents to be assigned tointermediate categories. This model is similar to the catalog
model in [Agrawal and Srikant, 2001], except that the categories are organized into a conceptual hierarchy. Since thecatalog model ignores hierarchy structure and therefore cannot assign documents to an intermediate category, our Internet directory model is more general than the catalog model.
The problem addressed in this paper is finding an appropriate category Ct in the target directory TD for each document Dsi in the source directory SD . An example is shownas the mapping of a black box DX in Figure 1, where theblack circles indicate categories and the hollow boxes indicate documents. What we need to do is determine an appropriate category in TD for a document which appears in SD
but not in TD , because mapping is not necessary if the document is included in both the source and the target directories. Documents D1 and D2 in Figure 1 are examples of such
documents. This mapping can have several possibilities, e.g.,
DX can be mapped to an upper left category or to a lower left
category, etc.

3 Related WorkOne popular approach to this kind of problem is to apply
standard machine learning methods [Langley, 1996]. Thisrequires a flattened class space having one class for every leaf node. The problem can then be considered to
be a normal classification problem for documents. Naive
Bayes(NB) [Mitchell, 1997] is an established method used forthis type of document classification framework. A classifier
is constructed using the words in the documents. However,this classification scheme ignores the hierarchical structure ofclasses and, moreover, cannot use the categorization information in the source directory. Enhanced Naive Bayes [Agrawal
and Srikant, 2001], hereafter referred to as E-NB, is a method
which does use this information. E-NB will be discussed
in the next section. GLUE [Doan et al., 2002] is anothertype of system employing NB. To improve accuracy, GLUE
combines NB and a constraint optimization technique called
relaxation labeling. However, the general performance ofthe system depends on that of NB. Unlike NB, the systems
in [Koller and Sahami, 1997], [Wang et al., 1999] classify
documents into hierarchical categories, and these systems use
the words in the documents for classification rules. However,these systems cannot use the categorization information in thesource directory.
Another type of approach is ontology merging/alignment
systems. These systems combine two ontologies, which
are represented in a hierarchal categorization.
Chimaera [McGuinness et al., 2000] and PROMPT [Noy andMusen, 2000] are examples of such systems and assist in the
combination of different ontologies. However, such systems
require human interaction for merging or alignment. In addition to this requirement, these systems are based on thesimilarity between words, which introduces instability. The
dictionaries used for such systems often have word similarity
bias. FCA-MERGE [Stumme and Madche, 2001] is anothertype of ontology merging method. It uses the attributes ofconcepts to merge different ontologies. As a result, it creates a new concept without regarding the original concepts inboth ontologies. Calvanese et al. [Calvanese et al., 2001] also
discussed an ontology integration framework with respect to
global ontology and local ontology.
As mentioned before, we also tackled the similar problemof catalog integration. An approach, besides E-NB, is to construct the abstract-level structure of two hierarchies [Omelayenko and Fensel, 2001]. This approach does not direct
the transformation of source and target information, but transforms via the abstract-level structure. It is relatively easy totransfer information through many hierarchical structures, but
it is hard to create a common structure for those hierarchies.
The bookmark-sharing systems of Siteseer [Rucker andPolanco, 1997] and Blink [Blink, 2000] also deal with asimilar problem. These systems attempt to share URL information which appears in the source bookmark but not in
the target bookmark. These systems flatten the categorization of bookmarks in the same manner as NB and determine the mapping of categories in each bookmark, based on
the shared URL information. Such systems are problematicwhen a given URL does not fit into an exact category. kMedia [Takeda et al., 2000] is another bookmark-sharing systemthat uses hierarchical structures explicitly, but is dependent
on the similarity of the words within pages.
In the context of information retrieval, Stuckenschmidt [Stuckenschmidt, 2002] has coordinated the formal
model of hierarchies with multiple classification hierarchies.
In our paper, since the upper and lower boundaries of concept
mapping can be determined by subsumption relationships,
we can infer the boundary of valid mapping and also check
whether or not the given mapping is consistent. In our
problem, since we have only partial evidence of classes anddo not know the definitions of classes, we can not apply thismethod directly.

4

SourceInternet
Directory

TargetInternet
DirectorySD

TD

Learning the Relationship between
Categories of Two Internet Directories

In this section, we explain our method to determine the relationship between categories in two Internet directories. One
characteristic of the proposed method is to use the hierarchical structure “as is”. We use all categories including intermediate categories and leaf categories, because information
for categorization can also be obtained from these categories.
Another characteristic of the proposed method is the exclusive reliance on the categorization structure of both directories, i.e., with no reliance on the semantic information of the
documents. We can determine the relationship between categories of the two directories by statistically comparing themembership of the documents to the categories.

SimilarCategory
Pair

Csi

Ctj

D

Figure 2: Document transfer from source Internet directory
to target Internet directory

CategoryCsibelongnot belong

Category Ctj
belong not belong
N11
N12
N21
N22

Table 1: Classification of documents by two categories

4.1

Basic Concept

Although E-NB was developed for a very similar problem,
it is missing an important feature: categorization hierarchy.
According to [Agrawal and Srikant, 2001], the initial definition of the problem is identical to that of this paper. However,the previous paper assumes that “any documents assigned to
an interior node really belong to a conceptual leaf node thatis a child of that node,” and concludes from this assumptionthat “we can flatten the hierarchy to a single level and treat
it as a set of categories.” However, the conclusion overlooks
the categorization hierarchies. The categorizations are not independent of each other. The categorization hierarchies areusually structured using an “is-a” or another relationship. If a
document is categorized in a lower category in the categorization hierarchy, then the document should also be categorized
in the upper categories 2 . If we flatten the categories, such information regarding the relationships between categories will
be lost.
Another problem associated with NB is its sensitivity to
the words in documents. For example, if a document contains the word bank, the category for the document could bea financial category; however, the category could also be aconstruction category. The quality of categorization with NB
is thus not stable according to the contents of documents, due
to the natural language techniques of processing words. It
is critical to integrate Internet directories because mixing reliable Internet directories maintained by human editors withless reliable and less stable machine-generated classifiers will
confuse users and decrease their confidence for them. In addition to the problem of sensitivity, analysis of a document to
extract words is expensive.
Our method focuses on the similarity of the way of categorization, not the similarity of documents. Then, how do wemeasure the similarity of categorization? We utilize shareddocuments in both the source and target Internet directories
as our measurement standard. If many documents in category
Csi also appear in category Ctj at the same time, we consider
2
Note that the reverse is not always true, because documents canbe categorized into intermediate categories.

these two categories to be similar, because the ways of categorization in Csi and Ctj are supposed to be similar, i.e., ifanother document D comes in Csi , it is likely that D will bealso included in Ctj . This method avoids both the keyword
extraction process and the handling of word meaning. The
example shown in Figure 2 illustrates that documents in thebottom category in the source Internet directory can be transferred to a similar category in the target Internet directory.

4.2 κ-Statistic
The remaining problem is how to determine the pairs of similar categories. We adopt a statistical method to determine thedegree of similarity between two categorization criteria. The
κ-statistic method [Fleiss, 1973] is an established method forevaluating the similarity between two criteria. Suppose there
are two categorization criteria, Csi in SD and Ctj in TD . We
can determine whether or not a particular document belongs
to a particular category3 . Consequently, documents are divided into four classes, as shown in Table 1. The symbols
N11 , N12 , N21 and N22 denote the numbers of documents forthese classes. For example, N11 denotes the number of documents which belong to both Csi and Ctj . We may logicallyassume that if categories Csi and Ctj have the same criterionof categorization, then N12 and N21 are nearly zero, and if
the two categories have a different criterion of categorization,then N11 and N22 are nearly zero. The κ-statistic method
uses this principle to determine the similarity of categorization criteria.
In the κ-statistic method, we calculate the probability P ,which denotes the percentage of coincidence of the conceptual criteria, and the probability P 0 , which denotes the percentage of coincidence of the conceptual criteria by chance.
P =N11 + N22
N11 + N12 + N21 + N22

3
Remember that categorization in a directory is determined using
the nodal structure of the categorization hierarchy.

¶
P0 =

(N11 + N12 )(N11 + N21 ) + (N21 + N22 )(N12 + N22 )(N11 + N12 + N21 + N22 )2

As such, the value of the κ-statistic is represented as thefollowing equation:
κ=P − P01 − P0

Next, we examine whether we can assume the κ = 0,
which indicates that the percentage of coincidence for thetwo conceptual criteria is zero. We therefore calculate thetest statistic Z according to the following equation.
r
Z=κ

(N11 + N12 + N21 + N22 )(1 − P 0 )P0(1)

The value Z follows a normal distribution. A null hypothesis is considered to occur when the percentage of coincidenceof the concept criteria is zero. When we assume a significance level of 5% and the following equation is satisfied, we
can dismiss the null hypothesis.
Z ≥ 1.64486
When the null hypothesis can be dismissed, the criteria are
determined to be the same.

4.3 Determination of Pairs of Similar Categories
The relationship between the two categorization criteria is examined from top to bottom. This algorithm is shown in Figure 3. First, the most general categories in the two categorization hierarchies are compared using the κ-statistic. If thecomparison confirms that the two categories are similar, thenthe algorithm outputs this pair of categories. At the sametime, the algorithm generates all possible pairs of their children categories. It generates two lists of categories each ofwhich is a list of the confirmed category and its children categories. Then, the algorithm picks one category from eachlist and makes a pair, except for the original pair. This newpair is then evaluated recursively using the κ-statistic method.
When a similar pair is not generated, the algorithm outputs
the pairs of similar concepts between the two categorization
hierarchies. They are used as mapping rules from the source
directory to the target directory4 .
This top-down comparison approach attempts to reduce theexploration space, using the structure of both directories as a
guide. Assuming that similar categories include similar subcategories, we can skip the combination of categories which
is likely unnecessary5 . This is another benefit of using the “asis” hierarchy, in contrast to the flattening approach.
4

The rules do not guarantee the consistency of the mappings,
because category hierarchies have the possibility of inconsistency
with regard to similar relationships.
5This assumption may sometimes be too strong, e.g., a child category of a category is not so relevant, whereas its child category (agrandchild category of the original category) is relevant. In suchcases, we should relax this constraint when exploring future candidates.

³

main algorithm
Input:
Cs1 , // Top category in SD
Ct1 , // Top category in TD
Output:R;// A set of similar category pairs
begin
t := 1;
R := φ;X1 := {[Cs1 , Ct1 ]};while Xt 6= φ
Xt+1 := φ;while Xt 6= φ
[C1 , C2 ] := any element in Xt ;if C1 and C2 is similar
Xt+1 := Xt+1 + make comb(C1 , C2 );R := R + [C1 , C2 ];fi;
Xt := Xt − [C1 , C2 ];end;
t := t + 1;
end;
return R;
end;—————————————————make comb
Input:Cs , // Category in SD
Ct ,// Category in TD
Output: S;// A set of category pairs in SD and TD
begin
S := {[Csi , Ctj ] | Csi is either Cs or a child of Cs ,Ctj is either Ct or a child of Ct } − [Cs , Ct ];return S;
end;

µ

´

Figure 3: Algorithm to determine similar category pair

4.4

Application Policy of Rules

Since the proposed method uses categorization similarity, if
a category in the source Internet directory does not have asimilar category in the target directory, then those documents
cannot be categorized in the target Internet directory. In order
to avoid this problem, we once again use the categorization
hierarchy. If the system cannot find a similar category pair
for the category containing the document, it applies the rule
generated for the parent category instead.

4.5

Computational Complexity

In this section, we discuss the computational complexity ofthe proposed algorithm shown in Figure 3. Suppose that twoconcept hierarchies SD and TD are trees of width b (the number of subcategories for each category) and height d (the number of categories from top to bottom) for the sake of simplicity. In this case, we calculate the total number of categories
N using the following equation:
N = b0 + b1 + . . . + bd
Comparison of the similarity among all categories requires
N × N comparisons. As a result, the order of computational
complexity is O(b2d ).

Next, we consider the cost of our algorithm. Assumingthat after the similarity tests using κ-statistic have been conducted, n% of the tests are confirmed to be similar to othercategories. The number of pairs produced by make comb
is (b + 1)2 − 1 because it makes pairs from the two lists,
each pair of which consists of the category itself and its childcategories, except for the original pair. The number of expected combination pairs after processing of the κ-statistic
is n(b2 + 2b) + (1 − n) · 0. The second half of the formula(1−n)·0 is applied because no similar pair is produced whenthe κ-statistic does not confirm the similarity. We finally obtain the total number of nodes M to be compared as the product of the number of examined nodes and the expected number of nodes. M is given by the following equation:
M

=

Autos
MoviesOutdoors
Photography
SoftwareDatasetAutos
MoviesOutdoors
Photography
Software
Average+n2 (b2 + 2b)2 + . . . + nd (b2 + 2b)d5

Experiments Using Internet Directories

5.1 Experimental Settings
In order to evaluate the proposed algorithm, we conducted
experiments using data collected from the Yahoo! [Yahoo!,
2001] and Google [Google, 2001]6 Internet directories. The
data was collected in the fall of 2001. In order to compare
the proposed method to that in [Agrawal and Srikant, 2001],
we selected the same locations in Yahoo! and Google for theexperimental data. The locations are as follows:• Yahoo! : Recreation / Automotive
Google : Recreation / Autos• Yahoo! : Entertainment / Movies and Film
Google : Arts / Movies• Yahoo! : Recreation / Outdoors
Google : Recreation / Outdoors• Yahoo! : Arts / Visual Arts / Photography
Google : Arts /Photography• Yahoo! : Computers and Internet / Software
Google : Computers / Software
Table 2 shows the numbers of categories, the links in each
Internet directory and the links included in both Internet directories. Links are considered to be the same when the URLs
in both directories are identical.
We conducted ten-fold cross validations for the sharedlinks. The shared links were divided into ten data sets; nineof these sets were used to construct rules, and the remaining
set was used for testing. Ten experiments were conducted for6
Since the data in Google is constructed by the data indmoz [dmoz, 2001], we collected data through dmoz.

Googlecategories
links
583
7909
4623
21244
825
13725
170
3999
2142
35628

shared
links
1002
2735
716
536
952

Table 2: Statistics on the experimental data

b0 + n1 (b2 + 2b)1

The order of complexity is then obtained as O(nd b2d ) =O((nb2 )d ). Since n denotes the percentage of category pairs
confirmed to be similar, 0 ≤ n ≤ 1 is satisfied. As a result,
we obtain less computational complexity using the algorithm
in Figure 3. In other words, although the depth remains thesame, the search algorithm can reduce computation by constraining the breadth size.

Yahoo!
categories
links
782
5200
4001
16947
2247
13932
375
4173
997
4686

Accuracy
E-NB SBI
76.2
88.0
42.6
77.9
77.8
74.0
72.8
79.7
62.4
73.4
64.4
78.4

Improvement
11.8
35.3
-3.8
6.9
11.0
14.0

Table 3: Results of Yahoo! as the source Internet directoryand Google as the target Internet directoryeach data set, and the average accuracy is shown in the results. In order to compare our proposed method to the E-NB
approach, the classifiers are assumed to correctly assign documents when the document is categorized either in the same
category as the test data or in the parent categories of the test
data. E-NB constructs classifiers for only the first-level categories in the experimental domain, whereas the proposed
method uses all of the categories from top to bottom. The
significance level for the κ-statistic was set at 5%.

5.2

Experimental Results

The experimental results are shown in Tables 3 and 4. Table 3 shows the results obtained using Yahoo! as the source
Internet directory and Google as the target Internet directory,
and Table 4 shows the results obtained using Google as thesource Internet directory and Yahoo! as the target Internet
directory. For comparison, these tables also include the results of [Agrawal and Srikant, 2001]. E-NB denotes themethod of [Agrawal and Srikant, 2001] and SBI denotes thesimilarity-based integration method that is proposed in thispaper. The data obtained for the proposed method and that
presented in [Agrawal and Srikant, 2001] are not truly comparable because the collection date is different7 .
The proposed algorithm did well compared to E-NB, performing more than 10% better in accuracy on the averages. In
the Movies domain, the proposed algorithm performs muchbetter in accuracy than E-NB. One reason for this is that thepages related to movies contain various words that indicate
numerous movie settings, making classification using wordbased systems difficult. On the other hand, in the Outdoors
domain, performance is similar for both methods because the
Outdoors domain treats pages using special outdoors-related
7
In addition, differences may have occurred due to data selection. For example, Yahoo! has links that use @(the at mark). The
treatment of such links was not discussed in [Agrawal and Srikant,2001]. In the present study, we did not use such links.

Dataset
Autos
MoviesOutdoors
Photography
Software
AverageAccuracy
E-NB SBI
73.1
83.6
46.2
68.1
65.4
68.9
51.3
60.4
58.6
74.0
58.9
71.0

Improvement
10.5
21.9
3.5
9.1
15.4
12.1

Table 4: Results of Google as the source Internet directoryand Yahoo! as the target Internet directory

words, and no classification method for the Outdoors domain
has been established for human readers.
As mentioned earlier, in the experiments describedin [Agrawal and Srikant, 2001], the classifiers induced by thesystem have the ability to classify the documents into only the
first-level categories of the test domain in the target Internet
directory. The proposed classifiers can classify the documents
into sub-categories as well. For example, we used 4,623 categories for document assignment in the Movies domain ofYahoo! as the source Internet directory and Google as the target Internet directory, whereas their system used only 40 categories. Therefore, the classifiers obtained by the proposed
method are more reliable and useful than those obtained byother methods.
Next, we compare our method with GLUE [Doan et al.,2002]. The basic steps of GLUE are as follows. First, the user
trains learners for each concept for target taxonomies. Learners can be a combination of different strategies like name- orcontents-based learning. Then, they determine that the source
concept and target concept are similar, if the application oflearners for the source concept yields good enough. Finally,
the system applies the relaxation labeling method for similar concept pairs. The method does not use the hierarchicalstructures in learning very well, and relies mainly on the performance of the learning method, namely NB. On the other
hand, our method does use hierarchical structures in learning
very well. So, we do not need a semantical analysis in learning. They report an accuracy of 65-95% which is equivalent
to our results, but they use only a few categories, i.e., 40-363,
while we use 170-4623 categories. We can say from boththeoretical and practical viewpoints that our method is more
appropriate in learning for large hierarchical structures.

6

Conclusions

In this paper, a statistical-based technique was proposed for
integrating multiple Internet directories by determining therelationship between these directories. The proposed method
uses the κ-statistics to find similar category pairs, and transfers the document categorization from a category in thesource Internet directory to a similar category in the target
Internet directory. The proposed method has an advantage
in document treatment in that it relies on the category structure only, and not on words or word similarity in a document. The performance of the proposed method was tested
using actual Internet directories, and the results of these tests
show that the performance of the proposed method was morethan 10% better in accuracy than that of previous methods,
although the number of categories were by far larger for our
proposed method. In addition, our problem modeling is moregeneral than the other models in terms of assignment documents on intermediate categories. From this, we can conclude
our approach shows great promise in comparison to systems
employing NB, such as E-NB [Agrawal and Srikant, 2001],
GLUE [Doan et al., 2002], and so on.
Although the present results are encouraging, much has
yet to be done. The limitation of the proposed method isthat this method is based on the existence of shared links.
In other words, the proposed method is less reliable if there
are fewer shared links. To improve our proposed method,various methods might be used to obtain semantic information in order to increase the number of shared links, e.g., toregard similar pages as the same links. Another option forthis problem might be to combine semantic information orother information about the documents. If we could a establish good combination of our proposed method and suchinformation, it is possible that we could greatly improve ourmethod. Moreover, the present method can find only one-toone mapping rules. If the system is able to find one-to-manyor many-to-many mappings, it would work more correctly incategorization. For the development of these mappings, wemight invent probabilistic mapping representations and also
introduce a subsumption technique [Stuckenschmidt, 2002].
Finally, the proposed method should be expanded so that it
can apply to more than three concept hierarchies. In sucha case, despite the conflict between several concept hierarchies, we would expect more information to be obtained. The
aforementioned tasks for improvement will be investigated infuture studies.

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