Search Web Page Classification Using Form Structural Characteristics

Search Web Page Classification Using Form Structural Characteristics
Myungsook Klassen
Computer Science Department
California Lutheran University
60 West Olsen Rd
Thousand Oaks, CA 91360
mklassen@clunet.edu
Chenxiao Wang
Computer Science Graduate Program
California Lutheran University
60 West Olsen Rd,
Thousand Oaks, CA 91360
cwang@clunet.edu
Abstract
Web pages with HTML forms are either search pages or
non search pages such as registration or blogging.
Search forms are external search, site search or internal
hidden database search. The hidden web pages provide
highly relevant quality content and represent a large
sector of online information source, yet general purpose
search engines do not find hidden web pages due to
difficulties of identifying them. In this paper, we present
a method to identify search forms from non search
forms using a small number of HTML input elements
extracted from user input HTML forms. Using the
random forest classifier, the classification rates obtained
are 89.12% with two classes, search form and non
search form. A small number of HTML input elements
and attributes used to identify four different HTML
forms proved not to be strong discriminators.
1. INTRODUCTION
Surface webs crawled by search engines contain only
a fraction of the contents available on-line. Most users
are only aware of information found by search engines
such as Google, Yahoo, or Bing. According to
Wikipedia, surface web contains an estimated 11.5
billion web pages in the publicly indexable web as of
January 2005. In the earliest days of the web, there
were relatively few web pages and most of them were
static web pages, so search engines found needed
information with relatively high user satisfaction level.
When the web became preferred medium for
commerce and information transfer, static pages could
no longer provide complete information. It is able to
cover 20% of it, which means 80% of valuable
information and data is still remains untouched on the
web surface. In general, searchable forms are very
sparsely distributed. Barbosa [2] points out that a
crawler retrieves only 94 movie search forms out of
100,000 crawled pages. The paradigm of using
conventional search engines no longer applies to the
information content of the internet. The development of
technology which accesses database back end
operations for the web search engines provide the path
to a paradigm switch to database driven web pages.
Web resources are constantly being added and
modified, so it is important to identify hidden web
pages in an efficient and automated way, requiring no
site specific scripting or analysis. There has been
increase of interest in techniques that find hidden web
pages as the volume of hidden information grows. One
is by creating a vertical search engines for specific
domains. This is a very costly operation for a search
engine since it is required to build mediator forms for
each domain and it is difficult for a search engine to
identify which queries are relevant to each domain. The
other approach is to explore all interesting HTML forms
and identify search interfaces.
On the web, there are many HTML forms and many
are not search interface. In particular, HTML forms for
login, blog, comments, registration, subscription, and
polling are not search interfaces. There are three
different search forms: site-search, external search, and
hidden database search. The “site search” forms are
what many web sites nowadays provide for searching
their own HTML texts on their sites. These pages
simply scan non HTML tag text and provide
information. They are not dynamically produced using a
database. External search engine is a HTML form
interface to search popular search engines such as
google and Yaoo. A hidden web (sometimes called as
deep web) is a web page that searches one or more
backend databases, through a HTML form as its query
interface.
The entrance search HTML forms must be
distinguished from non search web pages in order to
enable the web crawler to locate the entrance and extract
information further. Further more, hidden web HTML
forms must be identified from external search form and
text site-search form.

In this paper, we make an attempt first to identify
search forms from non search forms, and second, to
identify hidden web search forms from two other
types of search forms using a small number of HTML
input element attributes, since a sparse matrix data are
often very lager and are over fitted by classifiers if care
is not taken. Besides HTML input elements attributes
such as names, values, labels and URLs in the sparse
matrix contain many different string values and
variant string values which are hard to be used without
daunting string/text processing task.
Another contribution of our work is that we
conducted our experiments on a real data set we
collected by crawling web pages to acquire the newest
web page trends instead of using dated archived data
from data repositories such as UIUC. And lastly,
performance of a random forest classifier is explored
to evaluate its goodness in identifying different types
of forms.
The rest paper is organized as follows: section 2
presents related works in the field, and section 3
describes input values created from web pages. In
Section 4, the Random Forest classifier is briefly
discussed. Numerical results are presented in section 5,
followed by discussions and conclusions in section 6.
2. RELATED WORK
In what follows, we give an overview of previous
works on the hidden web classification and a brief
review of approaches which address different aspects of
hidden wed data retrieval.
In year 2000, He et al [6] surveyed the deep web
studying the scale, subject distribution, and search
engine coverage and reported their findings in 2007.
They report that 72% interfaces were found within a
depth 3 and 94% web databases appeared within depth
3. They classified web databases into two types:
unstructured databases such as images, audio and video,
and structures databases which provide data objects as
structured “relational” records with attribute-value pairs.
Hidden web database is dominated by structured
database with 77.2%. The top three subjects of hidden
web pages are business & economy, computers&
Internet, and education. The last question presented in
their paper was “ how do search engines cover the deep
web?”. They report that Google and Yahoo indexed
32% of hidden web pages while MSN had 11%.
Automatic classification of Hidden web database
classification: post query techniques
There are two methods to classify hidden page web
forms. One is pre-query, which classifies web database
according to the features of query forms such as HTML
tag input types and labels. The other is post query,
which identifies the search interfaces, submit probe
queries and analyzes the results.
Hidden web database classification is performed
based on probing[4][5]. Gong et el [4] created prototype
for classifying hidden web database into a predefined
category hierarchy using query probing and link
evaluation. Features for each class is extracted from
randomly selected web pages. The hidden web is probed
by analyzing the results of the class –specific query to
the hidden database. Classification methods used are k-
means nearest neighbor method. In [5] authors have
done similar work, but used extensive number of rules
or queries for probing multiple times while Gong et al
used only one query for probing each category. In [11],
authors used semantic information to feature vectors of
forms and centroid vector to classify hidden web
database. The support Vector machine classification
method is used with data obtained from UIUC databases
to obtain high classification rates between 90.87% and
98.37% in five categories. In [5], authors trained a rule
based document classifier, and then uses the classifier’s
rules to generate probing queries. The queries are sent to
the databases which are then classified based on the
number of matches that they produce for each query.
UCI KDD archive database is used for their work.
Identifying hidden web pages: pre query techniques
Pre-query techniques use visible features of forms.
Form attribute labels are used for classifiers. In [2],
authors proposed adaptive crawling strategies to
efficiently locate the entry points to hidden web
databases. The strategies use both links and forms to
enhance identification of hidden databases. For link
classification, features present in the anchor, URL, and
text around links are extracted and from them, terms
with highest document frequency are selected.
Hess and Kushmerick [7] used input tag labels,
input name and input types from 129 forms collected.
For instance HTML fragments “Enter name: ”, the sequence
[“enter”,”name”,”user”, “text”] is gathered to be used
for a Naïve Bayes classifier. They reported the
classification rate of 82%.
Barbosa and Freire [1] used 216 searchable forms
from the UIUC repository and gathered 259 non
searchable forms for the negative examples. HTML
form structure attributes such as number of hidden tags,
number of radio tags, number of file input, number of

submit tags, number of image inputs, number of buttons,
number of resets, number of password tags, number of
textboxes, number of items in selection lists, sum of text
sizes in textboxes and submission method (post versus
get) are used as attributes to several classifiers. Two
thirds were used as a training data set and one third as a
test data set for a multi layer perceptron, Naïve Bayes,
C4.5 and Support Vector Machines. They reported error
rates 9.05% by C4.5 and 24% by Naïve Bayes classifier.
Ye et al [10] used a sparse matrix of HTML
structure features. Features are value and name
attributes for “input”, “select”, “textarea”, and “label”
from input types to create a sequence such as [ input1,
input1-name, input1-value] for each HTML input type.
A number of “input” and a number of “select” , a
number “label”, and a number of “textarea” in each
form are computed to be used as features. In addition,
attributes “name” and “method” from a HTML form
element, “src” and “alt” values of “input-image”
element are used. Made up data set from Mataquerier
project [10] and websites crawled from Search Engine
Guide were used for their experiments. Bayes, C4.5
decision tree, support vector machine and random forest
classifiers produced from the lowest 79.78% to the
highest 93.88% classification rates.
3. FORM ENTRY DATA
3.1 Form collections
There are many different HTML forms and
many of them are not search interfaces. The forms are
categorized into the following four groups:
• External search: forms which provide
external web search sites such as to the
google site for convenience of users. They
are not considered as an entry to an
internal database.
• No search: forms for login, subscription,
registration, polling, or blogging. They are
not considered as an entry to an internal
database.
• site search: forms which many web sites
now provide for searching its own HTML
pages. They are not considered as an entry
to an internal database.
• Internal database search: forms which
provide entry to its own site backend
databases. These are considered true
hidden web pages.
(a) External search form
(b) Non search form
(c) Site search
(d) internal database search form
Figure 1: different HTML forms
The data used for our experiments were created by
crawling the web site www.searchengineguide.com with
the web crawler WebLech with a depth 3 and each
form was manually categorized into one of four types.
Examples of each type are shown in Figure 1. Initially
792 HTML forms were collected by the crawler and
only 9.8% of all forms were internal database forms.
Other categories collected are 12.86%, 46.36%, and
30.96% for external search, site search, and no search
respectively. To increase the number of internal
database search forms, 89 forms were manually
collected in 10 different subject areas and added to the
sample set (confirm with chenxiao) which now contains
874 samples.
3.2 Form input elements
A home grown parser was written in Java to scan
web pages and break into HTML elements and their
attributes. Inside HTML forms, following
elements(often called tags) were considered important
for users to put information for internal database search.
Initially based on work by Ye et al [10] but with

changes with our own ideas, the following elements and
their attributes were gathered.
• form element attributes action and name. For
action, only the file name from the URL was
used. The “method’ attribute was not used.
• input element type=”text”: name and value
attributes. This forms a pair for each input text.
• input element type=”checkbox”: data is
collected same as input type text.
• input element type=”radio”. data is collected
same as input type text.
• select element: name and value attributes. This
forms a set of triplets
for each select element.
• Textarea element: name and value attributes.
This forms a set of triplets for each textarea element.
• Labels from all elements and types inside
HTML were gathered as one attribute field
containing comma delimited text values.
The numbers of elements in forms vary much.
Some forms contain a large number of form elements
while some don’t contain any at all. Table 1 shows the
maximum number of HTML form elements found in
one HTML form. As a result, data created in this fashion
is a large sparse matrix with 144 attributes with 877
samples.
HTML form elements Maximum numbers
Input type text 14
select 6
Input type checkbox 16
Input type radio 18
textarea 2
Table 1:maximum number of form input elements
There are two main problems with this data set to
be used as features of a classification method. Not only
the data set is very sparse, but also names and values
have gathered have many variations ( name, nam, user
name, last name, lname, for instance) if meaningful
names used at all, but many names and values used are
surprisingly meaningless such as “tinyturing” and
“lang-­‐de”. And frequently an element name and value
are missing since they are not required in HTML.
Laborious preprocessing of text strings of name, value
and labels is required for them to be useful as
classification features.
4. RANDOM FOREST CLASSIFIER
The Random forest[3] is a meta-learner which
consists of many individual trees. Each tree votes on an
overall classification for the given set of data and the
random forest algorithm chooses the individual
classification with the most votes. Each decision tree is
built from a random subset of the training dataset, using
what is called replacement, in performing this sampling.
That is, some entities will be included more than once in
the sample, and others won't appear at all. In building
each decision tree, a model based on a different random
subset of the training dataset and a random subset of the
available variables is used to choose how best to
partition the dataset at each node. Each decision tree is
built to its maximum size, with no pruning performed.
Together, the resulting decision tree models of the
Random forest represent the final ensemble model
where each decision tree votes for the result, and the
majority wins.
There are two different sources of randomness in
Random forest: random training set (bootstrap) and
random selection of attributes. Using a random
selection of attributes to split each node yields favorable
error rates and are more robust with respect to noise.
These attributes form nodes using standard tree building
methods. Diversity is obtained by randomly choosing
attributes at each node of a tree and using the attributes
that provide the highest level of learning. Each tree is
grown to the fullest possible without pruning until no
more nodes can be created due to information loss. In
Breiman’s early work[2], each individual tree is given
an equal vote and later version of random forest allows
weighted and un-weighted voting.
The random forest algorithm computes the out-ofbag
error. The average misclassification for the entire
forest is called as out-of-bag error which is useful for
predicting the performance of the classifier without
involving the test set example nor cross-validation. The
out-of-bag error of random forest depends on the
strength of the individual trees in the forest and the
correlation between them. With a less number of
attributes used for split, correlation between any two
trees decrease and the strength of a tree decreases.
These two have reverse effect on error rates of random
forest: less correlation increases the error rate while less
strength decrease the error rate.
5. EXPERIMENTS
For our work, to eliminate sparse matrix problem
and to avoid preprocessing of strings, numeric data
such as the numbers of input element text type,
checkbox type, radio type, select element and textarea
element in a form were gathered.
In addition, all strings from label elements and
name attribute and value attribute of input type text,

checkbox and radio, element select and element
textarea are scanned for word “search” and its
synonyms. Proximity of a string to a certain input
element or type is not considered. Some synonyms and
antonym of the word “search” in the context of our
research work are “inquiry”, “examine”, “inspect”,
“investigate”, “look”, “query” and “find”. However
the word “search” is dominantly used over 99% and
“query” was found 4 times and “find” 2 times. Other
words were not used at all in our data samples.
In our analysis, Random forest in WEKA 3.5.6
software developed by the University of Waikato was
used for experiments. The data file has 6 input
attributes and 1 target class attribute. The target class
attribute values are external search, non search, site
search, and internal database search. Table 2 shows
numbers of samples of each class.
External
search
Non search Site search Internal db
search
104 288 344 151
Table 2: sample numbers of each class
The number of attributes to be used in Random
selection, “numFeatures” (is called Mtry in WEKA),
and the number of trees to be generated “numTrees”
were two parameters controlled in our work for
performance evaluation. The number of tree depth was
set to 0 to build trees of any depth. For all data sets,
numTrees were set to max 10. With each numTree,
classification rates of Mtry values 3,4, and 5 are shown
in Figure 2.
5.1 4 classes classification
Two test methods were used: 10-cross
validation and using separate test samples. With 10-fold
cross validation as a test option, its result is shown in
Figure 2 for various numTrees and attributes. The
highest 66.93% was obtained when all 5 attributes were
used with one tree. The next was to do testing with a
test sample set. The entire file is divided into two files:
70% as a training data set and 30% as a test data set.
The classification rates obtained are in a range between
62.89% and 62.63%, about 4.% lower than those
obtained by 10 folds cross validation.
The confusion matrix is shown in Table 3. The site
search results is surprisingly low with zero
classification rate while internal database search shows
0.99 classification. However there are many false
positive (FP) internal database search from external
search, non search and site search classes with values
0.42, 0.18 and 0.50 respectively.
Figure 2: classification rates by 10-fold cross
validation
5.2 2 classes classification
External
search
Non
search
Site
Search
Internal
db search
External
search
Non
search
Site
search
Internal
db
search
0.02 0.56 0.0 0.42
0.02 0.80 0.0 0.18
0.09 0.41 0.0 0.50
0.0 0.0 0.01 0.99
Table 3:test sample classification confusion matrix
The second experiment was to classify any searches
(external, site search, internal db search) from non
search such as password entry and registration. The
classification rates with the test sample set are 89.12%
consistently for numbers of attributes from 1 to 5 and
numbers of trees from 1 to 10. Table 4 shows statistics
from the test data set classification. The classification
with only two classes increases from 66.93% to
89.12%.
Classification rate 0.8912
Out-of-bag error 0.1705
Mean absolute error 0.1667
RMSE 0.2872
Search class TP rate 0.944
Search class FP rate 0.279
Non search class TP rate 0.721
Non search class FP rate 0.056
Table 4: statistics of 2 class test sample data set
classification.

6. CONCLUSIONS AND FUTURE
WORKS
It is apparent from our results that the
numbers of input type text, input type checkbox, input
type radio, input element select, input element textarea
along with the word ‘search” are not enough to
distinguish 4 different types of classes: site search,
external search, internal database search and non search.
Login, registration, discussion and blogging are
considered non search. Three classes, external search,
internal database search and text search are very similar
in HTML & PHP codes and have very similar structure
in appearance and layout. External search facility and
site search is provided by many web pages in recent
years and almost become a standard feature in any web
pages.
There is not enough statistical difference with
the attribute the number of input type text as shown in
the scatter diagram (Figure 3). Other HTML elements
and types used for other work show similar results. And
when we combined three search types into one and
made our data as two class problems, similar results
were obtained.
Figure 3:scatter diagram of the numbers of input
type text(Y axis) for 4 classes (x-axis).
In contrast, the word “ search” is a good discriminator to
distinguish non search class from three search classes.
Over 99.9% search classes contain the word “search”
while approximately 20% of non search class contains
the word “search.” This is due to the fact that in our
research work, we didn’t consider a proximity of the
word ‘search’ or similar occurrence to the location of a
HTM input form element or type. In one case, an
irrelevant URL away in an input text from a non
search page contained the word ‘search’.
Our 2 class classification rate we obtained is
close to 90%, which implies that we use a simple set of
input attributes to identify search form from login or
registration pages. We propose our future work that it is
an incremental learning machine architecture which
consists of
Stage One: non search forms are separated from any
search forms.
Stage Two: Three search sites, site-search sites,
external search sites and internal database search
sites are separated
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