An Examination of the Relationships between
Internet Directories
Ichise, R., Takeda, H. and Honiden, S.
Intelligent Systems Research Division,
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 provides one solution to this problem, however, such directories are of limited use because some bias is
applied both in the collection and categorization of the pages. Therefore,
we propose a method for integrating multiple internet directories. Our
method provides mapping of categories to be able to transfer documentsfrom a directory to another, not simply merging two directories into one.
Each document in a category of the source internet directory can be
categorized into a similar category on the target internet directory. We
present herein an effective algorithm for determining similar categories
between two directories via a statistical method called κ-statistic. The
benefits of the proposed method are twofold. First, unlike other methods, which use only a flat classification structure, the proposed method
uses knowledge of hierarchies. Second, the proposed method does notuse semantics of pages. The proposed method can determine the relationship between directories via hierarchical structure, whereas othermethods often use semantics of pages such as keywords. In order to evaluate the proposed method, we conduct experiments using actual internet directories, Yahoo! and Google. The obtained results show that the
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 being available on
the internet. This makes finding the pages containing the desired information
rather difficult. Search engines are requisite to find desired information on theinternet. Current search engines perform their functions via one of two methods
in general: keyword search and directory search. A keyword search engine performs searches by means of user-specified keywords. Now keyword-based search

IIengines like Google are reliable to find pages containing the specified keywords.
However, if the user does not have a thorough knowledge of his/her search domain, the search engine is useless because the user cannot choose appropriate
keywords. In such a situation, directory-based search engines do a good work. In
a directory-based search engine, pages are evaluated and organized by humans
before being registered within the search engines archive. Directory-based searchengines provide knowledge for navigation of WWW. Users can reach the desired
information in the issue of going up and down the directories.
Although pages are carefully selected and well-organized, a single internet
directory is not sufficient because the search engine 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 rather determines the relationship between directories. Many such public internet directories exist. Some aredesigned to cover wide domains, while others focus on special domains. Suchinternet directories are difficult to merge. Moreover, these directories should not
be integrated, because existing the difference in concept hierarchies among directories is important when selecting and using such directory-based search engines.
In this paper, we propose a method to solve this problem by determining rules
by which to map categories in a directory to those in another directory. Our
solution can be applicable not only to the internet directory problem, but also
to integration of web marketplace catalogs [1]1 .
This paper is organized as follows. In section 2, we define the problem of
coordinating multiple internet directories and discuss related studies. Next, insection 3, we propose a new machine learning method for the above-mentioned
problem. In section 4, we compare the performance of the proposed method tothat of the Enhanced Naive Bayes approach [1] for an integration problem using
real internet directories. Finally, in section 5, we present our conclusions.

2Integration of multiple internet directories

In this section, we discuss the problem formalism and related research.
2.1

Problem specification

In order to state the problem, we introduce a model for the internet directories
we intend to combine and assume two internet directories: a source directory
and a target directory. The documents in the source internet directory are expected to be assigned to categories in the target internet directory. This produces
a virtually integrated internet directory in which the documents in the source
directory are expected to be members of both the source and target directories. This integrated directory inherits categorization hierarchy from the target
internet directory.
1

For example, integration of a distributor’s catalog and a web marketplace.

III

Category
Document

Fig. 1. Internet directory model

The internet directory model for source and target is as follows:– The source internet directory, SD contains a set of categories, Cs1 , Cs2 , . . . , Csn ,that are organized into an “is-a” hierarchy. Each category contains documents.
– The target internet directory TD contains a set of categories, Ct1 , Ct2 , . . . , Ctm
that are organized into an “is-a” hierarchy. Each category contains documents.
The model can be represented as shown in Figure 1. The proposed model
permits documents to be assigned to intermediate categories. This model issimilar to the catalog model in [1], except that the categories are organized into
a conceptual hierarchy. Since the catalog model ignores hierarchy structure andtherefore cannot assign documents to an intermediate categories, our internet
directory model is more general than the catalog model.
The problem addressed in this paper is to find an appropriate category Ct
in the target directory TD for each document Dsi in the source directory SD .
An example is shown as mapping of a black box DX in Figure 2. What weshould do is to determine an appropriate category in TD for a document whichappears 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 the Figure 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.
2.2

Related work

One popular approach to this kind of problem is to apply standard machinelearning methods [7]. This requires a flattened class space having one class forevery leaf node. The problem can then be considered as a normal classification
problem for documents. Naive Bayes(NB) [9] is an established method used forIV
SourceInternet
Directory

TargetInternet
Directory

SD

TD
D2
D1

?
D2

Dx

D1

Fig. 2. Problem statement

this type of document classification framework. A classifier is constructed using
words in documents. However, this classification scheme ignores the hierarchicalstructure of classes and, moreover, cannot use categorization information in thesource directory. Enhanced Naive Bayes [1], hereafter referred to as EnhancedNB, is a method which does use this information. Enhanced-NB will be discussed
in the next section. Unlike NB, the systems in [6], [14] classify documents into
hierarchical categories, and these systems use words in documents for classification rules. However, these systems cannot use 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 [8] and PROMPT [11] are examples of such systems and assist
in the combination of different ontologies. However, such systems require humaninteraction for merging or alignment. In addition to this requirement, these systems are based on similarity between words, which introduces instability. The
dictionaries used for such system often have word similarity bias.
The bookmark-sharing systems of Siteseer [12] and Blink [2] also treat a similar 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 themapping of categories in each bookmark based on shared URL information. Suchsystems are problematic when a given URL does not fit into an exact category.
kMedia [13] is another bookmark-sharing system that uses hierarchical structures explicitly but is dependent on similarity of words within pages. BookmarkAgent [10] uses another approach, using bookmarks based on keywords. However,this system has the same problem as ontology merging/alignment systems.
2.3

Naive Bayes classification and enhancement thereof

Since we will be comparing Enhanced-NB approach and the proposed approach,
we introduce NB and Enhanced-NB briefly in this section.

V

Naive Bayes is one method used to create classifiers from documents and theircategories. The basic concept of the learned classifier is that the classifier assigns
the category of maximum probability for the document we want to classify. When
NB calculates the probability, the probability of keywords in the document areused as training data. In the context of the problem defined in section 2.1, theclassifier is constructed using categories Ct1 , Ct2 , . . . , Ctm as well as keywords
in documents Dt1 , Dt2 , . . . , Dtv in target internet directory TD . The classifieris then applied to documents Ds1 , Ds2 , . . . , Dsu in source internet directory SD
and assigns a category in TD for each document.
As described above, NB does not use the categorization information Cs1 , Cs2 , . . . , Csn
in the source internet directory SD . Enhanced-NB is an extension in which categorization information is used in order to increase accuracy. The basic concept behind this approach is that if documents belong to the same category in
SD , then the documents are more likely to belong to the same category in TD .
Enhanced-NB does not calculate conditional probability of category provided
that only documents are given, but rather calculates conditional probability of
category provided that both documents and categories in the source directory
are given. Enhanced-NB then constructs a classifier to maximize the conditional
probability. This treatment is more suitable to the problem stated in section 2.1.
Agrawal and Srikant report in [1] an improvement over NB for a similar problem.

3

Determination of the relationship between categoriesof two internet directories

In this section, we explain our method in order to determine the relationshipbetween 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 such intermediate categories. Anothercharacteristic of the proposed method is reliance exclusively on the categorization structure of both directories, i.e., without semantic information of documents. We can determine the relationship between categories of two directories
by statistically comparing the membership to categories of documents.
3.1

Basic concept

Although Enhanced-NB is developed for a very similar problem, it is missing animportant feature, that is, categorization hierarchy. According to [1], 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 that is a child of that node” and concludes from thisassumption that “we can flatten the hierarchy to a single level and treat it as aset of categories”. However, the conclusion overlooks the categorization hierarchies. The categorizations are not independent of each other. The categorization
hierarchies are usually structured using an “is-a” relation or other relations. If a

VI
SourceInternet
Directory

TargetInternet
DirectorySD

TD
SimilarCategory
Pair

Csi

Ctj

D

Fig. 3. Document transfer from source internet directory to target internet directory

document is categorized in a lower category in the categorization hierarchy, thenthe document should also be categorized in the upper categories 2 . If we flatten
the categories, such information of relations between categories will be lost.
Another problem associated with NB is sensitivity to words in documents. For
example, if a document contains the word bank, the category for the document
could be a financial category; however, the category could also be a construction
category. The quality of categorization with NB is thus not stable according
to contents of documents due to natural language techniques how words areprocessed in it. It is critical to apply integration of internet directories because
mixing of reliable internet directories maintained by human editors and suchless reliable and stable machine-generated classifiers will make users confused
and will hurt confidence for them. In addition to the problem of sensitivity, theanalysis of the document to extract words is expensive.
We focus on similarity of the way of categorization not similarity of documents. Then how can we measure similarity of categorization? We utilize shard
documents in the both internet directory as its measurement. If many documents
in a category Csi also appears in another category Ctj at the same time, we regard that these two categories are similar, because the ways of categorization in
Csi and Ctj are supposed to be similar, i.e., if another document D comes in
Csi , it is likely that D will be also included in Ctj . This method allows both the
keyword extraction process and the handling of word meaning to be avoided.
The example shown in Figure 3 illustrates that documents in the bottom category in the source internet directory can be transferred into a similar category
in the target internet directory.
3.2

κ-statistic

The remaining problem is how to determine pairs of similar categories. In order to establish similar category pairs, we adopt a statistical method to deter2

Note that the reverse is not always true because documents can be categorized tointermediate categories.

VII
Table 1. Classification of documents by two categories
Category Ctj
belong not belong
Category belong
N11
N12
Csi
not belong N21
N22

mine the degree of similarity between two categorization criteria. The κ-statistic
method [4] is an established method to evaluate similarity between two criteria.
Suppose there are two categorization criteria, Csi in SD and Ctj in TD . We candetermine whether a particular document belongs to a particular category or
not3 . 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 logically assume that if categories Csi and Ctj
have the same criterion of categorization, then N12 and N21 are nearly zero, andif the two categories have a different criterion of categorization, then N11 andN22 are nearly zero. The κ-statistic method uses this principle to determine thesimilarity of categorization criteria.
In the κ-statistic, 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 =

P0 =

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

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

Next, we examine whether we can assume the κ = 0, which indicates thatthe percentage of coincidence of two conceptual criteria is zero. We therefore
calculate test statistic Z according to the following equation.
r(N11 + N12 + N21 + N22 )(1 − P 0 )
Z=κ
(1)
P0
The value Z follows a normal distribution. A null hypothesis is considered tooccur when the percentage of coincidence of concept criteria is zero. When we
3

Remember that categorization in a directory is determined using the nodal structureof categorization hierarchy.

VIII

assume a significance level of 5% and the following equation is satisfied, we candismiss the null hypothesis.
Z ≥ 1.64486
When the null hypothesis can be dismissed, the criteria are determined to be
the same.
3.3

Determination of pairs of similar categories

The relationship between the two categorization criteria is examined from top tobottom. This algorithm is shown in Figure 4. 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, then the algorithm
outputs this pair of categories. At the same time, the algorithm generates all
possible pairs of their children categories. It generates two lists of categories
each of which is a list of the confirmed category and its children categories, thenpicks up one from each and makes a pair except the original pair. This new pairis then evaluated recursively using the κ-statistic method. When a similar pairis not generated, the algorithm outputs the rules between the two categorization
hierarchies.
This top-down comparing approach attempts to reduce the exploration space
using the structure of both directories as a guide. Assuming that similar categories include similar sub-categories, we can skip the combination of categories
which is likely unnecessary4 . This is another benefit of using hierarchy “as is”,
in contrast to the flattening approach.
3.4

Application policy of rules

Since the proposed method uses categorization similarity, if a category in thesource internet directory does not have a similar 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 categorization hierarchy. If
the system can not find a similar category pair for the category containing the
document, it applies the rule generated for the parent category instead.
Let us explain this process by using an example. Figure 5 illustrates theabove-mentioned situation. The bottom category in the source internet directory has no similar category and the parent category has a similar category
represented by the broken line. In this case, the system applies the rule for theparent category to the document in the bottom category. As a result, the documents are assigned to a category in target internet directory according to therule for its parent category.
4

This assumption is sometimes too strong, e.g., a child category of a category is not so
relevant, whereas its child category (a grandchild category of the original category)is relevant. In such cases, we should relax this constraint when exploring future
candidates.

IX

¶

³

main algorithm
Input:
Cs1 , // Top category in SD
Ct1 , // Top category in TD
Output: R;// Set of similar category pair
begin
t := 1;
R := φ;X1 := [[Cs1 , Ct1 ]];while Xt 6= φ
while Xt 6= φ
I := element in Xt ;C1 , C2 := two categories in I;
if C1 and C2 is similar
Xt+1 := Xt+1 + make comb(C1 , C2 );R := R + I;
fi;
Xt := Xt − I;end;t := t + 1;
end;
return R;
end;—————————————————make comb
Input:Cs ,// Category in SD
Ct ,// Category in TD
Output: S;// Set of category pair in SD and TD
begin
S := set of category pair, Cs and childs in Ct ;S := S + set of category pair, Ct and childs in Cs ;S := S + set of category pair made by combinationof childs in Cs and childs in Ct ;return S;
end;

µ

´
Fig. 4. Algorithm to determine similar category pair

X
SourceInternet
Directory

SimilarCategory

TargetInternet
Directory

Fig. 5. An example of rules for a category which has no similar category in the target
internet directory

3.5

Computational complexity

In this section, we discuss the computational complexity of the proposed algorithm shown in Figure 4. Suppose that two concept hierarchies SD and TD aretrees of width b (the number of subcategories for each categories) and heightd (the number of categories from the top to the bottom) for sake of simplicity.
In this case, we calculate the total number of categories N using the following
equation:N = b0 + b1 + . . . + bd
Comparison of 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. Assuming that after the similarity tests using κ-statistic have been conducted, n% of the tests are confirmed to be similar to other categories. The number of pairs produced bymake comb is (b + 1)2 − 1 because it makes pairs from two lists each of which
consists of the category itself and its child categories except the orignal pair.
The number of expected combination pairs after processing of the κ-statistic isn×(b2 +2b)+(1−n)×0. The formula (1−n)×0 arises because no similar pair is
produced when the κ-statistic does not confirm the similarity. We finally obtainthe 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 = b0 + n1 (b2 + 2b)1+n2 (b2 + 2b)2 + . . . + nd (b2 + 2b)d
The order of complexity is then obtained as O(nd × b2d ) = O((n × b2 )d ).
Since n denotes the percentage of category pairs confirmed to be similar, 0 ≤XI
Table 2. Statistics on the experimental data
Yahoo!
Googlesharedcategories links categories links links
Autos
782
5200
583
7909 1002
Movies4001 16947 4623 21244 2735
Outdoors
2247 13932
825
13725 716
Photography
375
4173
170
3999 536
Software
997
4686
2142 35628 952

n ≤ 1 is satisfied. As a result, we obtain less computational complexity using
the algorithm in the Figure 4. In other words, although the depth remains thesame, the search algorithm can reduce computation by constraining the breath
size.

4

Experiments using internet directories

4.1

Experimental settings

In order to evaluate proposed algorithm, we conducted experiments using datacollected from the Yahoo! [15] and Google [5]5 internet directories. The data wascollected in the fall of 2001. In order to compare the proposed method to thatin [1], we selected the same locations in Yahoo! and Google for the experimentdata. 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, links in each internet directory andlinks included in both internet directories. The links are considered to be thesame when the URLs in both directories are identical.
We conducted 10-fold cross validation for shared links. Shared links weredivided into 10 data sets; nine of these sets were used to construct rules, andthe remaining set was used for testing. Ten experiments were conducted for each5

Since data in Google is constructed by the data in dmoz [3], we collected data
through dmoz.

XII
Table 3. Results of Yahoo! as the source internet directory and Google as the target
internet directory
Accuracy
Dataset Enhanced-NB [1] SBI Improvement
Autos
76.2
88.0
11.8
Movies
42.6
77.9
35.3
Outdoors
77.8
74.0
-3.8
Photography
72.8
79.7
6.9
Software
62.4
73.4
11.0
Average
64.4
78.4
14.0

data set, and the average accuracy is shown in the result. In order to compare theproposed method to the Enhanced-NB approach, the classifiers are assumed tocorrectly 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. EnhancedNB constructs classifiers for only the first-level categories in the experimental
domain, whereas the proposed method uses all of the categories from top tobottom. The significance level for the κ-statistic was set at 5%.
4.2

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 thetarget internet directory, and Table 4 shows the results obtained using Google
as the source internet directory and Yahoo! as the target internet directory. For
comparison, these tables also include the results of [1]. Enhanced-NB indicatesthe method of [1] and SBI indicates the similarity-based integration method,
proposed in this paper. The data obtained for the proposed method and that
presented in [1] are not truly comparable because the collection date is different6 .
The proposed algorithm did well enough compared to Enhanced-NB, performing more than 10% better in accuracy than Enhanced-NB. In the Movies domain, the proposed algorithm performs much better in accuracy than EnhancedNB. One reason for this is that pages related to movies contain various words
to indicate numerous movie settings, making classification using word based
systems difficult. On the other hand, in the Outdoors domain, performance issimilar for both methods because the Outdoors domain treats pages using special
outdoors-related words, and no classification method for the Outdoors domain
has been established for human readers.
As mentioned earlier, in the experiments described in [1], the classifiers induced by the system have the ability to classify the documents into only the first6

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[1]. In the present study, we did not use such links.

XIII
Table 4. Results of Google as the source internet directory and Yahoo! as the target
internet directory
Accuracy
Dataset Enhanced-NB [1] SBI Improvement
Autos
73.1
83.6
10.5
Movies
46.2
68.1
21.9
Outdoors
65.4
68.9
3.5
Photography
51.3
60.4
9.1
Software
58.6
74.0
15.4
Average
58.9
71.0
12.1

level categories of the test domain in the target internet directory. The proposedclassifiers can classify the documents into sub-categories as well. For example,
we used 4,623 categories for document assignment in the Movies domain of Yahoo! as the source internet directory and Google as the target internet directory,
whereas their system used only 40 categories. Therefore, the classifiers obtainedby the proposed method are more reliable and useful than those obtained byother methods.

5Conclusions

In this paper, a statistical based technique was proposed for integrating multiple internet directories by determining the relationship between these directories.
The proposed method uses κ-statistics to find similar category pairs, and transfers the document categorization from a category in the source internet directory
into a similar category in the target internet directory. The proposed method
has an advantage in document treatment, whereby it relies on only the category
structure, not words or word similarity in a document. The performance of the
proposed method was tested using actual internet directories, and the resultsof these tests show that the performance of the proposed method was morethan 10% in accuracy better than that of the previous method even though thenumber of categories are by far larger.
Although the present results are encouraging, much has yet to be done. The
limitation of the proposed method is that this method is based on the existenceof shared links. In other words, the proposed method is less reliable if there
are fewer shared links. To improve it, various methods might be used to obtainsemantic information in order to increase the number of shared links. e.g., regarding similar pages as the same links. Moreover, the present method can find
only one-to-one mapping rules. If the system is able to find a set of categories
in the source directory and a set of categories in the target directory that are
similar, the categorization hierarchies can be divided into smaller parts. Finally,
the proposed method should be expanded so as to apply to more than three
concept hierarchies. In such a case, despite the conflict between several concept

XIV

hierarchies, more information is expected to be obtained from other concept
hierarchies. The aforementioned tasks for improvement will be investigated infuture studies.

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