Web-page classification by using hypertext is a major approach
to categorizing large quantities of Web pages. Two major kinds of approaches have been studied for Web-page classification:
content-based and context-based approaches. Typical
content-based classification methods utilize words or phrases of
a target document in building the classifier and can achieve only
limited accuracy. This is because very often a Web page contains
no obvious clues textually for its category. For example, some
pages contain only images and little text information. By
exploiting the hyper-textual information, context-based
approaches additionally exploit the relationships between the Web
pages to build a classifier [14,8,7]. As a consequence, they are found
to be more accurate than pure content based classifiers. However,
the hyperlinks sometimes may not reflect true relationships in
content between Web pages, and the hyperlinks themselves may be
sparse. A technical Web page may link to a commercial page
describing a beauty product. In such situations, it is unreliable
to employ hyperlinks for classification.
We observe that hyperlinks provide only one kind of linkages
between Web pages. By properly utilizing the Web query logs, we
can discover other important linkages that are just as important.
In this paper, we propose to extract a new kind of links known as
implicit links from the Web query logs that are accumulated by
search engines. These logs record the Web users' behavior when
they search for information via a search engine. Over the years,
query logs have become a rich resource which contains Web users'
knowledge about the World Wide Web (WWW). In order to mine the
latent knowledge hidden in the query logs, much research has
been conducted on the query logs [17,2]. Many applications of log analysis
have been conducted in categorization and query clustering
Consider the behavior of a Web user. After submitting a query, a
Web user gets a long list of Web pages with snippets returned by
a search engine. Then the user could look through some of the
returned pages selectively. All the queries the users submit and
the Web pages they click on constitute the query log. Although
there may be some noise in query logs, the clicks still convey
important information on the similarity between Web pages and
queries in a large query log. In the context of Web page
classification, an implicit type of similarity information we can
use is the fact that the Web pages that are clicked by users
through the same query usually belong to the same categories. It
is this kind of knowledge that we make use of in order to build
the implicit links between Web pages.
In this paper, we define two kinds of implicit links that can
be extracted from a query log and three kinds of explicit links
using the traditional hyperlink concepts. We further define two
different approaches in making use of these links. The first one
is to classify a target page according to the labels of the
neighbors of that page. We show that using this algorithm for
classification, the implicit links are more reliable in
classification. The best result of using implicit links is about
20.6% higher than the best result through the explicit links in
terms of the Micro-F1 measure. The second classification method
we use is to construct a virtual document based on different
kinds of links, and then conduct classification on the virtual
documents. The experiments on the Web pages crawled from the Open
Directory Project (ODP)1 and the query logs collected by MSN
show that using the implicit links can achieve 10.5% improvement
compared to the explicit links in terms of Macro-F1.
The main contributions of this paper could be summarized as
- We introduce a new resource - the query log - to help
classify Web pages. Based on the query logs, a new kind of
links - the implicit links - is introduced. Comparison between
the implicit and explicit links on a large dataset shows that
the implicit links are more helpful for Web-page
- We define the concept of a virtual document by extracting
''anchor sentence (AS)'' though implicit links which
corresponds to ''anchor text (AT)'' and ''extended anchor text
(EAT)'' associated with hyperlinks. We show that using our
proposed virtual documents, the classification performance can
be improved by more than 10.5% relative to the best result
obtained by explicit links in terms of the Macro-F1
The rest of the paper is organized as follows. In Section 2,
we present the related works on query logs and Web classification
through hyperlinks. We give the definition of two kinds of
''Implicit Link'' and three kinds of ''Explicit Link'' in Section
3. Section 4 gives the approaches to utilize the Links. The
experimental results on the ODP dataset and query logs collected
by MSN as well as some discussions are shown in Section 5.
Finally, we conclude our work in Section 6.
In the past,
much work has been done on context-based Web page classification
by exploiting hyperlinks among web pages. Chakrabarti et al. used
predicted labels of neighboring documents to reinforce
classification decisions for a given document . Oh et al. proposed a practical
method for exploiting the hypertext structure and hyperlink
information . They modified
the Naive Bayes algorithm to classify documents by using
neighboring documents that were similar to the target document.
Both the predicted labels and the text contents of the
neighboring documents were used to assistant classification. The
experimental results on an encyclopedia corpus that contains
hyperlinks validate their algorithms. F¡¡ì¹rnkranz also reported a
significant improvement in classification accuracy when using the
link-based method as opposed to the full-text alone  on 1,050 pages of the WebKB
corpus2, although adding the entire text of
''neighbor documents'' seemed to harm the ability to classify
pages . In , the authors investigated six kinds
of regularities in a hypertext corpus with three classifiers. The
conclusion in  showed that
using words in Web pages alone often yields sub-optimal
performance for classifiers, compared to exploiting additional
sources of information beyond document content. It also showed
that linked pages can be more harmful than helpful when the
linked neighborhoods are highly noisy and that links have to be
used in a careful manner. Eiron and McCurley found that the anchor text
is typically less ambiguous than other types of texts; thus they
can be used to produce a good representation for web pages
. Glover et al. came to the
same conclusion in  on a
subset of the WebKB dataset as F¡¡ì¹rnkranz used  and a dataset crawled from Yahoo!
which consists of less than ten thousand pages. Glover et al.
 concluded that the
full-text of a Web page is not good enough for representing the
Web pages for classification. They created virtual documents by
incorporating anchor text and extended anchor text. The
experimental results demonstrated that the virtual documents,
especially when constructed through extended anchor text are of
great help. In this paper, we enhance the notion of virtual
documents by the implicit and explicit links for classification.
Our experimental results on a large dataset not only confirm the
previous conclusions, but also show that the virtual documents
constructed through our proposed implicit links are consistently
better than those constructed through hyperlinks.
Query logs are being accumulated rapidly with the growing
popularity of search engines. It is natural for people to analyze
the query logs and mine the knowledge hidden in them.
Silverstein et al. made a static analysis of an AltaVista query
log from six weeks in 1998 consisting of 575 million nonempty
queries . Beitzel et al.
 studied a query log of
hundreds of millions of queries that constitute the total query
traffic for an entire week of a general purpose commercial web
search service which provided valuable insight for improving
retrieval effectiveness and efficiency. By analyzing and
leveraging the information from query log, many applications
become feasible or easier. Raghavan and Sever described an elegant method
of locating stored optimized queries by comparing results from a
current query to the results from the optimized query . Beeferman and Berger proposed
an innovative query clustering method based on query log
. By viewing the query log
as a bipartite graph, with the vertices on one side corresponding
to queries and those on the other side corresponding to URLs,
they applied an agglomerative clustering algorithm to the graph's
vertices to identify related queries and URLs. Wen et al.
incorporated click-through data to cluster users' queries
. They analyzed a random
subset of 20,000 queries from a single month of their
approximately 1-million queries-per-week traffic. Chuang and Chien
proposed a technique for categorizing Web query terms from the
click-through logs into a pre-defined subject taxonomy based on
their popular search interests . Xue et al. proposed a novel
categorization algorithm named IRC (Iterative Reinforcement
Categorization algorithm) to categorize the interrelated Web
objects by iteratively reinforcing individual classification
results via relationships across different data types extracted
from query logs .
However, to the best of our knowledge, no research has considered
building implicit links between documents through query logs for
Web page classification. In the next section, we formally
introduce the concept of implicit links.
Consider a typical scenario when a Web user finds
information by a search engine. A user issues a query which may
be a well-formed natural language question or one or more
keywords or phrases to a search engine. The search engine replies
with a list of Web pages to the user together with a summary of
each page. The summary, together with other clues such as the URL
and title, can give the user a general idea of the contents of
the page. Based on this information, the Web user clicks on some
Web pages that appear to be most relevant to the query. The query
log records the process, by keeping an entry in query log for
every session in which the Web user submits a query and clicks on
some returned pages. An example of such a quadruple is as
where U denotes the web user who submits the query. The user is
often represented by the IP address from which the user accesses
the Internet. Q denotes the query the Web user submits to the
search engine. represents the returned Web pages clicked by the
user. T represents the time when such an entry occurs. In this
work, we only focus on using the query logs for Web page
classification. Thus, we omit T and consider an entry as
. In fact, we could further simplify
the entry as by omitting U. However, often times
when the same query is issued by two different users, it may mean
different things. Such ambiguities between users make the pages
that are constrained by Q alone to be more heterogeneous in
content than the pages that are constrained by both U and Q.
Based on a query log, we define two kinds of implicit link
relations according to two different constraints:
- : and appear in the same
entries constrained by U and Q in the query log. That is
and are clicked by the same user
issuing the same query;
- : and appear in the same
entries constrained by the query Q only. That is and
are clicked according to the same query, but the
query may be issued by different users. It is clear that the
constraint for 2 is not as strict as that for
. Thus, more links of can be found than
, however they may be more noisy.
Similar to the implicit links, we define three kinds of
explicit links based on the hyperlinks among the Web pages
according to the following three different conditions:
- there exist hyperlinks from to
(In-Link to from );
- there exist hyperlinks from to
(Out-Link from to );
- either or holds.
We denote these three types of explicit links under the above
conditions as , , respectively.
In the above definitions, we distinguish between the in-link
and out-link because, given the target Web page, the in-link is
the hyperlink created by other Web page editors who refer to the
target Web page. In contrast, the out-link is created by the
editor of the source Web page. They may be different when used to
describe the target Web page.
Based on other considerations, it is possible for us to define
other links. For example, we could define a kind of implicit link
between two pages if they belong to two different entries and the
intersection of the two clicked-page sets of the two entries is
not empty. We could also require that the similarity between two
pages be larger than a certain threshold to define a certain kind
of explicit link. However, for the purpose of comparing explicit
and implicit links, we will only focus on the above five
different types of links.
From the above definitions, we observe that the implicit links
give the relationships between Web pages from the view of Web
users. However, the explicit links reflect the relationships
among Web pages from the view of Web-page editors.
A straightforward approach to utilizing
links for Web page categorization is to predict the label of the
target Web page by the labels of its neighbors through majority
voting. We call this method ''Classification by Linking
Neighbors'' (CLN for simplicity). The formal definition of this
method is given below:
where represents a kind of link; is the set of Web pages
which relate to d through ;
denotes the number of the neighbors which belong to the class
. This algorithm is similar to k-Nearst Neighbor (KNN).
However, k is not a constant as in KNN and it is decided by the
set of the neighbors of the target page.
The CLN method classifies a Web page only based on the labels
of its neighboring pages. It does not consider the content of Web
pages. In this work, we enhance classification performance
through the links by constructing virtual documents. Given a
document, the virtual document is constructed by borrowing some
extra text from its neighbors. Although originally the concept of
the virtual document is pioneered by , we extend the notion by
including different links.
To form a virtual document, we consider two cases. One is to
adjust the non-zero weights of existing terms in the original
keyword vector that represents the page. The other is to bring in
new terms from the neighbors so that the modified vector will
have additional non-zero components. The second case changes the
vector more drastically. The experimental results in  showed that the latter method
resulted in a 23.9 % decrease in F1 measure. Thus, in this paper,
we adopt the former to construct virtual documents.
The plain text in Web pages, as well as the rich information
marked by HTML, such as the title, meta-data and paragraph
headings could be all used for Web-page classification. Some
research works have studied the contribution of these elements to
Web-page classification [15,8]. In
order to show the contribution of different links on Web page
classification, we try to find an appropriate way to
represent web pages by local words and then take it as a baseline
to compare with the virtual document representation. In our
previous study, we find that the weighted combination of Plain
text, Meta-data and Title usually achieves the best
classification performance. Thus, we take these as the local
representation of web pages. We give these concepts below:
Due to the different characteristics of the implicit and
explicit links, we define different types of virtual documents.
For explicit links, Anchor text (AT) is usually employed to form
a virtual document. Since a Web page often has other pages linked
to it, the accompanying anchor text often describes the target
Web page. These anchor text provides a very good description of
the content of the page in different contexts and by different
people. In , Glover et
al. defined a concept of extended anchor text (EAT) which incudes
the set of rendered words occurring up to 25 words before and
after an associated link as well as the anchor text itself. Their
experimental results showed that both anchor text and extended
anchor text could improve Web-page classification greatly.
Therefore, we also use both methods in constructing the virtual
documents to compare with our proposed virtual documents
constructed through implicit links.
- Plain Text: Plain text refers to the remaining text after
all the html tags are removed from web pages.
- Meta-data: Meta-data refers to the content embedded in the
html tags Meta and /Meta, such as
''Keywords'' and ''Description'';
For the implicit links, there is no ''anchor text'' as defined
in the case of explicit links. As a consequence, in this work, we
define a corresponding concept-''anchor sentence''. Since the
implicit links among the Web pages are built through ''queries'',
we could define the ''anchor sentence'' through a query. If an
implicit link between and is created according to a
query Q, the set of sentences in which contain all the words in
Q are regarded as the ''anchor sentences''. We then collect the
anchor sentences to construct a virtual document. The query Q is
preprocessed as shown in section 5.1. We require the anchor
sentences include all the words in the query to guarantee that
the content of the virtual document focuses on the query.
After constructing the virtual document through links, any
traditional text classification algorithm could be employed to
classify the web pages. In this paper, we take Naive Bayesian
classifier and Support Vector Machine as the classifiers to
compare the quality of different virtual documents.
have defined a new kind of links between documents: implicit
links. In this section, we experimentally verify the merits
of introducing this type of links. We introduce the experimental
data set, our evaluation metrics, and the experimental results
based on those metrics with the analysis. All the classification
results shown in this paper are obtained through 10-fold cross
validation to reduce the uncertainty of data split between
training data and test data.
The dataset used in this work
contains 1.3 million Web pages, which are crawled from ODP. All
these Web pages have been manually classified into hierarchical
directories, whose first level contains 17 categories. Web pages
in the ''Regional'' category are also included in other
categories. In addition, Web pages in the ''World'' category are
not written in English. Therefore, these two categories are not
considered in our experiments. Under the remaining 15 first-level
categories, there are 424 second-level categories. Our
experiments are conducted at the second level. Table 1 and Table
2 present the three largest and three smallest categories. Among
the 424 second-level categories, there exist 76 categories each
of which contains less than 50 pages. The unbalanced distribution
may be the main reason for the low value of Macro-F1 measurement
in the following experimental results.
Table 1: Three largest categories
||religion and spirituality
Table 2: Three smallest categories
||news and media
||chats and forums
A subset of the real MSN query log is collected as our
experiment data set. The collected log contains about 44.7
million records of 29 days from Dec 6 2003 to Jan 3 2004. Some
preprocessing steps are applied to queries and Web pages in the
raw log. We processed the log into a predefined format as shown
in Section 3. All queries are converted into lower-case, and are
stemmed using the Porter algorithm.3 The stop words are
removed as well. After preprocessing, the log contains 7,800,543
entries, having 2,697,187 users, 308,105 pages and 1,280,952
queries. That is, among the 1.3 million ODP Web pages, 308,105 of
them clicked by users in the 29 days are studied in this
paper. The average query length is about 2.1 words.
paper, we would apply two classifiers including Naive Bayesian
classifier (NB) and Support Vector Machines (SVM) on the virtual
documents. A brief introduction of them is given below.
NB is a simple but effective text classification
algorithm which has been shown to perform very well in practice
[12,13]. The basic idea in NB is to use the
joint probabilities of words and categories to estimate the
probabilities of categories given a document. As described in
, most researchers employ
NB method by applying Bayes' rule:
can be calculated by counting
the frequency with each category occurring in the training
data; is the number of categories; stands for the probability that word
occurs in class . This last quantity may be small
in the training data. Thus, Laplace smoothing is chosen to
estimate it; is the number of occurrences of a word
in ; n is the number of words in the training data.
SVM is a powerful learning method introduced by
Vapnik et al. [5,19]. It is well founded in terms of
computational learning theory and has been successfully applied
to text categorization .
The SVM algorithm is based on the Structural Risk Maximization
theory, which aims to minimize the generalization error instead
of the empirical error on training data alone. Multiple variants
of SVM have been developed ; in this paper we train the
classifier using the software package4 due
to its popularity and high performance in text categorization.
For all the following experiments, we used a linear kernel for
comparison purposes because of its high accuracy for text
categorization . The
trade-off parameter C is fixed to 1 for comparison purpose and
the one-against-all approach is used for the multi-class case
We employ the standard measures to evaluate
the performance of Web page classification, i.e. precision,
recall and F1-measure .
Precision (P) is the proportion of actual positive class members
returned by the system among all predicted positive class members
returned by the system. Recall (R) is the proportion of predicted
positive members among all actual positive class members in the
data. F1 is the harmonic average of precision and recall as shown
To evaluate the average performance across multiple categories,
we apply two conventional methods: micro-average and
macro-average. Micro-average gives equal weight to every
document; while macro-average gives equal weight to every
category, regardless of its frequency. In our experiments, both
of them are used to evaluate the performance of
In this section, we show the statistics of different links in the data set
firstly. Then the results of the two approaches of leveraging different links
are presented, based on which we compare the merits of implicit links and
Table 3: Statistics on links of
Table 3 introduces the characteristics of the five types of links
in the data set. In this table, consistency refers to the
percentage of links that have the two linked pages from the same
category. This quantity can reflect the quality of the links, in
that the higher the consistency, the better the quality of the
link is. #Links refers to the number of links of each kinds of
link in our dataset and #Links/page refers to the density of
links. From Table 3, we could find that the consistency of
is about 23.2% higher than and which
indicates that is more reliable for guessing the category
of a target page by the labels of its linked neighbors. One fact
for explaining this observation is that very often hyperlinks are
created not according the similarity of content between Web
pages, but for other reasons such as commercial advertisement. We
could also find that the consistency of to be much lower
than , though the number of links of is far larger than
. Such an observation shows that we could get more
links by relaxing the condition, but the quality of links may
Table 3 supports the published facts that linked pages can be
more harmful than helpful and that links have to be used in a
careful manner . In these
cases, two pages linked together by or any kind of explicit
links tend to belong to different categories with a probability
more than 0.5.
to compare the contributions of different links for the
classification task, we run the experiments of CLN using a subset
of the ODP Web pages. The subset is determined by the coverage of
the links. For example, given 1, we collect all the pages which
are covered by implicit link of type 1 and then split them into
10 groups randomly to perform the 10-fold cross validation. The
results listed in Figure 1 are the average of the experiment
results of 10 runs. Micro-F1 and Macro-F1 represent the micro and
the macro average F1 measure from the 424 categories. From Figure
1, we could see that the two kinds of implicit links both achieve
better results than the three kinds of explicit links. The best
result achieved by implicit links is about 20.6% in terms of
Micro-F1 and 44.0% in terms of Macro-F1 higher than the best
result achieved by explicit links. We can also see that the
1 based classification method outperforms that based on
. The explanation of this observation is that the
average number of In-Links per page (4.38) is much larger than
the average number of Out-Links per page (3.23) which can help
remove the effect of noise.
From our previous study, we find that the
weighted combination of Plain text (PT), Meta-data and Title (MT)
can achieve the best classification performance among all local
representations of the Web page. Thus, in this paper, we simply
adopt the weighted combination of PT and MT (by 1:2, which
achieve the best result on our dataset) as the baseline (denoted
as LT, Local Text) and do not present the detailed comparison
between the different local representations since we focus on the
comparison between implicit links and explicit links. For each
kind of virtual document, it is first tokenized with a stop-word
remover and Porter stemming. Then, each document is represented
as a bag-of-words, in which the weight of each word is their term
frequency. In this experiment, the document frequency selection
(DF)  is applied for feature
selection. The words whose DF are lower than four are removed
from the feature set.
In Section 4.2, we gave a brief description of
the construction of virtual documents. In this section, we
describe the virtual documents in more detail along with an
empirical comparison. Given a Web page, we have two ways to
construct a virtual document: (1) through the anchor text or the
extended anchor text if explicit links are considered.
Alternatively, this can be done through anchor sentences if the
implicit links are considered. (2) Through local text within a
Web page. The local text refers to the weighted combination of
Plain text, Meta-data and Title. In this part, we only consider
the best links of implicit link and explicit link as shown in
results of CLN (that is 1 and 1) to construct virtual
documents. Together we have five combinations through which to
form a virtual document, as listed in Table 4.
Figure 1: Results of CLN on different kinds of Links
Table 4: Approaches to constructing
In this table, ELT refers to the virtual documents constructed
by combining the local text from all the pages that link to the
target Web page through 1. ILT refers to the virtual
documents constructed by combining the local text from all pages
which link to the target Web page through 1. EAT refers to
virtual documents consisting of the extended anchor text. These
are the set of rendered words occurring up to 25 words before and
after the anchor text. They also include the anchor text itself.
In order to test the impact of implicit links and explicit links,
in the following experiments, we only work on a subset of the ODP
dataset that we collected in which all the pages are covered by
both 1 and 1. The subset contains 56,129
pages. Table 5 shows the performance of classification on
different kinds of virtual documents, where Mi-F1 refers to
Micro-F1 and Ma-F1 refers to Macro-F1.
Table 5: Performance on different kinds
of virtual document
(1) Classification performance by SVM
(2) Classification performance by NB
To analyze the results of different kinds of virtual
documents, two factors should be considered. One is the average
size of the virtual documents and the other is the consistency or
purity of the content of the virtual documents. The average size
of each kind of virtual document is shown in Table 6.
Table 6: Average size of different kinds
of virtual document (in terms of KB)
From Table 5, we observe that although it is supposed that AS,
EAT and AT can reflect the content of the target pages correctly,
the classification performance based on them is just as good as
the baseline, or even worse than the baseline. One possible
reason may lie in the fact that the average sizes of AS, EAT and
AT are usually too small to obtain any satisfying classification
results. On the other side, though the average size of ELT is large
enough compared to the average size of LT, too much noise may be
introduced when it is constructed through 1 and the included
noise biased the classification results. We also see that ILT is
much better than ELT, with a relative increase of 12.8% in terms
of Micro-F1 through NB classifier. The reason may be that the
content of these Web pages linked by 1 may be quite different.
Thus, by combining them together, the diversity of the content
may bias the classification result. However, the content of the
Web pages linked by 1 tend to be similar to each other.
Therefore, it is more appropriate to employ the implicit links
when constructing a virtual document to improve the
Table 7: Classification performance of
different combinations between AT, EAT, AS and LT
(1) Results on SVM
(2) Results on NB
|Impr* = the greatest
improvement in each row compared to LT (that is the
Although the classification results of AT, EAT and AS shown in
Table 5 are not very encouraging, there are special cases for
which AT, EAT and AS can capture the meaning of the target Web
page well. In the next experiment, we construct a virtual
document by integrating the AT, or EAT, or AS to the target Web
pages. The weight between AT, EAT, AS and the local text in the
combination is changed from 4:1 to 1:4. The detailed result is
given in Table 7. From Table 7 we could see that either AT, EAT
or AS can improve the performance of classification to some
extent with either classifier in terms of either Micro-F1 or
Macro-F1. In particular, AS can provide greater improvement,
especially with the NB classifier in terms of Macro-F1. The
improvement is about 41.2% compared to the baseline, 12.8%
compared to EAT and 16.1% compared to AT. Another observation
from Table 7 is that the different weighting schemes do not make
too much of a difference, especially in terms of Micro-F1.
In addition to the results reported above, we also combine the
LT, EAT and AS together to test whether they complement each
other. However, this combination makes a relatively very small
improvement compared to the combination of LT and AS only.
We have shown that when combined with the local
text, AS, EAT and AT all can improve the classification
performance on the subset of ODP dataset in which all the pages
are covered by both 1 and 1. AS can consistently
outperform AT with either NB or SVM classifier. In this part, we
test the effect of the query log's quantity. We divide the
entries in the query log into ten parts according to the time when
the entries are recorded. Each part contains about 10 percent of
the total entries. Firstly, we use the first part to construct
implicit links, and then construct AS. After that, we incorporate
these AS to the subset of ODP dataset that we used in previous
experiments (with the weighting schema as LT: AS = 2 : 1) and do
classification on the combined dataset. Then, we use the first
two parts and then the first three parts, , until all the
parts are used. The results are shown in Figure 2. From Figure 2,
we could find that with more query logs used, the classification
performance improves steadily. Such an observation is very
encouraging since we can expect to get better results with larger
query logs. Another fact is that query log data are accumulated
rapidly with the popularity of search engines. Therefore, it is
safe to claim that the implicit link will play more and more
important roles in Web page classification.
Figure 2: The effect of query log quantity
With the growing popularity of search engines, more and more
query logs are being collected. We have shown here that these
data can become an important resource for Web mining. In this
paper, by analyzing the query log, we define a new relationship
between Web pages - implicit links. In addition, we define
different types of explicit links based on the existing
hyperlinks. Similar to the Anchor Text used in the traditional way,
we define a new approach to construct virtual documents by
extracting the Anchor Sentences. Through the two different
classification approaches, the ink-based method and the content-based
method, we compared the contribution of the implicit links and
the explicit links for Web page classification. The
experimental results show that implicit links can improve the
classification performance obviously as compared to the
baseline method or to the explicit links based methods.
In the future, we will also test whether the implicit links
will help Web page clustering and summarization. In addition,
we wish to explore other types of explicit and implicit links
by introducing other constraints, and compare them with the
links given in this paper. Besides furthering the research
following the framework of this paper, we will try to exploit
more knowledge from the query logs. For example, when dealing
with query logs, most researchers only consider the clicked
pages and assume that there exist some ''similarity''
relationships among these pages. They neglect the
''dissimilarity'' relationships among the ''clicked Web pages''
and ''unclicked Web pages''. In our future work, we will study
the possibility of constructing ''dissimilarity'' relationships to
help Web page classification and clustering.
- D. Beeferman and A. Berger.
Agglomerative clustering of a search engine query log.
In KDD '00: Proceedings of the sixth ACM SIGKDD International Conference on Knowledge
Discovery and Data
Mining, pages 407-416, New York, NY, USA, 2000.
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