=Paper=
{{Paper
|id=Vol-471/paper-2
|storemode=property
|title=From Web Pages to Web Communities
|pdfUrl=https://ceur-ws.org/Vol-471/paper2.pdf
|volume=Vol-471
|dblpUrl=https://dblp.org/rec/conf/dateso/KudelkaSHH09
}}
==From Web Pages to Web Communities==
https://ceur-ws.org/Vol-471/paper2.pdf
From Web Pages to Web Communities
Miloš Kudělka1 , Václav Snášel1 , Zdeněk Horák1 , and Aboul Ella Hassanien2
1
VSB Technical University Ostrava, Czech Republic
milos.kudelka@inflex.cz, {vaclav.snasel, zdenek.horak.st4}@vsb.cz
2
Faculty of Computer and Information, Information Technology Department, Cairo
University, Egypt
abo@cba.edu.kw
Abstract. In this paper we are looking for a relationship between the
intent of Web pages, their architecture and the communities who take
part in their usage and creation. From our point of view, the Web page
is entity carrying information about these communities and this paper
describes techniques, which can be used to extract mentioned informa-
tion as well as tools usable in analysis of these information. Information
about communities could be used in several ways thanks to our approach.
Finally we present an experiment which illustrates the benefits of our ap-
proach.
Keywords: Web community, Web site, Web pattern, genre
1 Introduction
Metaphor: A Web page is like a family house. Each of its parts has its sense,
determined by a purpose which it serves. Every part can be named so that
everybody imagines approximately the same thing under that name (living room,
bathroom, lobby, bedroom, kitchen, balcony). In order that the inhabitants may
orientate well in the house, certain rules are kept. From the point of view of these
rules, all houses are similar. That is why it is usually not a problem e.g. for first
time visitors to orientate in the house. We can describe the house quite precisely
thanks to names. If we add information about a more detailed location such as
sizes, colors, equipment and further details to the description, then the future
visitor can get an almost perfect notion of what he will see in the house when he
comes in for the first time. We can also approach the description of a building
other than a family house (school, supermarket, office etc.). Also in this case the
same applies for visitors and it is usually not a problem to orientate (of course
it does not always have to be the case, as well as bad Web pages there are also
bad buildings).
In the case of buildings, we can naturally define three groups of people, which
are somehow involved in the course of events. The first group are the people
defining the intent and the purpose (those who pay and later expect some profit),
the second one are those who construct the building (and are getting paid for it)
K. Richta, J. Pokorný, V. Snášel (Eds.): Dateso 2009, pp. 13–22, ISBN 978-80-01-04323-3.
14 Miloš Kudělka, Václav Snášel, Zdeněk Horák, Aboul Ella Hassanien
and the third group are “users” of the building. These groups fade into another
and change as society and technology evolve.
As we describe in the subsequent text, the presented metaphor can - up to
certain point - serve as an inspiration to seize the Web pages content and also
the whole Web environment.
This text is organized as follows. In the second section we describe the Web
page from the view of groups of people sharing the Web page existence. The third
section describes tools and techniques required for our experiment. In particular
our own Pattrio method, which is designed to detect Design patterns within Web
pages, and FCA used for clustering. In the fourth section we describe experiment
dealing with Web site description. The last section contains paper recapitulation
and focuses on possible directions of further research.
2 From Web pages to Web communities
Every single Web page (or group of Web pages) can be perceived from three
different points of view. When considering the individual points of view we were
inspired by specialists on Web design ([29]) and on the communication of humans
with computers ([6]). These points of view represent the views of three different
groups of communities who take part in the formation of the Web page (fig. 1).
Fig. 1. Views of three different groups
(1) The first group are those whose intention is that the user finds what he
expects on the Web page. The intention which the Web page is supposed to
fulfill is consequently represented by this group. For the sake of clarity, we can
say that this group is often represented by Web site owners. (2) The second
group are developers responsible for the creation of the Web page. They are
therefore consequently responsible for fulfilling the goals of the two remaining
groups. (3) The third group are users who work with the Web page. This group
consequently represents how the Web page should appear outwardly to the user.
It is important that this performance satisfies a particular need of the user.
As an example we can mention blogs. The first community are the companies,
which offer an environment and technological background for blog authors and to
From Web Pages to Web Communities 15
Fig. 2. Social network around Web pages
some extent they also define the formal aspects of blogs. The second community
are the developers who implement the task given by the previous group. The
visible attribute of this group is that they – to a certain degree – share their
techniques and policies. The third group consists of blog authors (in the sense
of content creation). They influence the previous two groups retroactively. The
second example can be the product pages - the intention of the e-shop is to sell
items (concretely to have Web pages where you can find and buy the products),
the intention of the developers is to satisfy the e-shop owners as well as the Web
page visitors. The intention of the visitors is to buy products, so they expect
clearly stated and well-defined functionality. From this point of view, the web
pages are elements around which the social networks are formed (fig. 2). For
further details and references, please see [1] and [15] (which considers also the
aspect of network evolution).
Under the term Web community we usually think of a group of related Web
pages, sharing some common interests (see [28], [20], [21]). As a Web community
we may also consider Web site or groups of Web sites, on which people with
common interests interact. It is apparent, that all three aforementioned groups
participate in the Web page life cycle. The evolution of a page is directly or
indirectly controlled by these groups. As a consequence, we can understand the
Web page as a projection of interaction among these three groups. The analysis
of the page content may uncover significant information, which can be used to
assign the Web page to a Web community.
3 Tools and techniques
Our aim is to automatically discover such information about Web pages, that
comes out of intentions of particular groups. Using these information we can
find the relations between the communities and describe them (on the technical
level). The key element for Web page description is the name of the object,
16 Miloš Kudělka, Václav Snášel, Zdeněk Horák, Aboul Ella Hassanien
which represents the intention of the page. It can be “Home page”, “Blog” or
“Product Page”. In the detailed description we can distinguish, for example,
between “Discussion”, “Article” or “Technical Features”. We can also use more
general description, such as “Something to Read” or “Menu” (see [14]).
Fig. 3. Product page scheme (a), (b)
The first group of intentions represents so-called Genre (see [5]). The second
group is very close to Web Design Patterns [30]. Figure 3 contains schematically
depicted product Web page with hierarchy of solved tasks (each task represents
one particular intention). The ability to discover aforementioned elements (Gen-
res and Web design patterns) is required to obtain the Web page description
(and consequently also the intentions represented by mentioned communities).
Genre is a taxonomy that incorporates the style, form and content of a docu-
ment which is orthogonal to topic, with fuzzy classification to multiple genres [4].
In the same paper are described existing classifications. Regarding these classifi-
cations there are many approaches on genre identification methods. The goal of
paper [11] is to analyze home page genres (personal home page, corporate home
page or organization home page). In paper [7] authors have proposed a flexible
approach for Web page genre categorization. Flexibility means that the approach
assigns a document to all predefined genres with different weights. In [9] paper,
there is described a set of experiments to examine the effect of various attributes
of Web genre on the automatic identification of the genre of Web pages. Four
different genres are used in the data set (FAQ, News, E-Shopping and Personal
Home Pages).
3.1 Pattrio method
Design patterns describe proven experience of repeated problem solving in the
area of software solution design. While the design patterns have been proven in
real projects, their usage increases the solution quality and reduces the time of
their implementation. Good examples are also the so called Web design patterns,
which are patterns for design related to the Web. Even in this area, the patterns
are getting quite common (they are collected and published in the form of printed
or Internet catalogues, e.g. see [29], [30]).
From Web Pages to Web Communities 17
We have designed our own Pattrio method used for the detection of Web de-
sign pattern instance in web pages. In Pattrio method we work with 24 patterns
(mostly e-commerce and social domain). Pattrio method is based on analysis of
technical (architectural) and semantical attributes of solutions of the same tasks
in the environment of Web, for details see [13], [14].
Detection algorithm In the context of our approach, there are elements with
semantic contents (words or simple phrases and data types) and elements with
importance for the structure of the web page where the Web pattern instance can
be found (technical elements). The rules are the way that individual elements
take part in the Web pattern display. While defining these rules, we have been
inspired by the Gestalt principles (see [27]). We are using four rules based on
these principles. The first one (proximity) defines the acceptable measurable
distances of individual elements from each other. The second one (closure) defines
the way of creating of independent closed segments containing the elements. One
or more segments then create the Web pattern instance on the web page. The
third one (similarity) defines that the Web pattern includes more related similar
segments. The forth one (continuity) defines that the Web pattern contains more
various segments that together create the Web pattern instance. The relations
among Web patterns can be on various levels similar as classes in OOP (especially
simple association and aggregation).
The basic algorithm for detection of Web patterns then implements the pre-
processing of the code of the HTML page (only selected elements are preserved
e.g. block elements as table, div, lines, etc.), segmentation and evaluation of rules
and associations. The result for the page is the score of Web patterns that are
present on the page. The score then says what is the relevance of expecting the
Web pattern instance on the page for the user.
The accuracy of our method is about 80% (see [12]). Figure 4 shows the
accuracy of Pattrio method for tree selected products (Apple iPod Nano 1GB,
Canon EOS 20D, Star Wars Trilogy film) and for the Discussion pattern and the
Purchase possibility pattern. We used only the first 100 pages for each product.
We manually and using Pattrio method evaluated the pages using a three-degree
scale:
+ Page contains required pattern.
? Unable to evaluate results.
- Page do not contain required pattern.
Then we compared these evaluations. For example the first value 61% ex-
presses the method accuracy for the pages with Canon EOS 20D product where
there was a discussion.
3.2 Formal Concept Analysis
As one of the suitable tools for analyzing this kind of data we consider Formal
concept analysis. When preprocessing Web pages we often cannot clearly state
18 Miloš Kudělka, Václav Snášel, Zdeněk Horák, Aboul Ella Hassanien
Fig. 4. Accuracy of Pattrio method for detection of Discussion and Purchase Possibility
patterns - percentage of agreement between human and Pattrio method evaluation on
sets of Web pages returned for different search queries
the presence of an object in the page content. We are able to describe the amount
of its presence at some scale and this information can be captured using fuzzy
methods and analyzed using a fuzzy extension of Formal Concept Analysis ([3]).
But since we are dealing with a large volume of data ([8]) and a very imprecise
environment, we should consider several practical issues, which have to be solved
prior the first applications. Methods of matrix decomposition have succeeded in
reducing the dimensions of input data (see [26] for application connected with
Formal concept analysis and [18], [17] for overview).
Formal concept analysis (shortly FCA, introduced by Rudolf Wille in 1980)
is well known method for object-attribute data analysis. The input data for FCA
we call formal context C, which can be described as C = (G, M, I) – a triplet
consisting of a set of objects G and set of attributes M , with I as relation of G
and M . The elements of G are defined as objects and the elements of M as
attributes of the context.
′
For a set A ⊆ G of objects we define A as the set of attributes common to the
′
objects in A. Correspondingly, for a set B ⊆ M of attributes we define B as the
set of objects which have all attributes in B. A formal concept of the context
′ ′
(G, M, I) is a pair (A, B) with A ⊆ G, B ⊆ M , A = B and B = A. B(G, M, I)
denotes the set of all concepts of context (G, M, I) and forms a complete lattice
(so called Gallois lattice). For more details, see [10].
4 Experiment
For the need of our experiment we have implemented a Web application with user
interface connected to the API of different search engines (google.com, msn.com,
yahoo.com and the Czech search engine jyxo.cz above all). Users from a group
From Web Pages to Web Communities 19
of students and teachers of high schools and our university were using this ap-
plication for more than one year to search for ordinary information. We have
not influenced the process of searching in any way. The purpose of this part of
experiment was to view the World Wide Web using the perspective of normal
users (as the search engines play key role in World Wide Web navigation). In the
end we have obtained dataset with more than 115,000 Web pages. After clean
up, 77,850 unique Czech pages remained. For every single Web page we have per-
formed the detection of sixteen objects. The page did not have to contain any
object, as well as it may have contained 16 objects (Price information, Purchase
possibility, Special offer, Hire sale, Second hand, Discussion and comments, Re-
view and opinion, Technical features, News, Enquire, Login, Something to read,
Link group, Price per item, Date per item, Unit per item). We have used such
preprocessed dataset for following experiment.
In the experiment we have tried to visualize the structure and relations
of Web sites (and as a result also Web communities) referring to one specific
topic. As an input we have used the list of domains created in the previous
experiment. Only Web sites with more than 20 pages in the dataset have been
taken into consideration. Each domain is accompanied by detected objects. This
list is transformed into a binary matrix and considered as a formal context. Using
methods of FCA we have computed a concept lattice which can be seen on fig-
ure 5. The resulting matrix has 516 rows (objects) and 16 columns (attributes),
computed concept lattice contains 378 concepts.
Fig. 5. Lattice calculated from whole dataset
20 Miloš Kudělka, Václav Snášel, Zdeněk Horák, Aboul Ella Hassanien
From the computed lattice we have selected a sub-lattice containing 18 Web
sites dealing with cell phones. Only 5 attributes have been selected and the vi-
sualization was created in a slightly different manner (see figure 6 and attached
legend). Each node of the graph corresponds to one formal concept. To increase
the visualization value, the attributes are represented by icons and the set of ob-
jects (Web sites) is depicted using small filled/empty squares in the lower part.
It can be easily seen that using created visualization we can think of dividing
the whole set of Web sites into two groups - the first one contains sites where
users are enabled to buy cell phones and the second one where the users are
allowed to have a discussion. The illustrated division is in the soft sense only —
one may think of concept nr. 8 as being part of the shopping group also. Web
sites presented in higher levels of lattice are considered in more specific context.
Deeper insight gives you more detailed information about Web site structures
and relations.
Fig. 6. Part of lattice
The concept lattice forms a graph, which can be interpreted as an expression
of relation between different Web sites. As a consequence, it describes the rela-
tion between different Web communities, because behind the shopping-related
domains we can see the group of users interested in buying cell phones and be-
hind the information–sharing pages we see the community of users interested
only in the technical aspects, features of cell phones and their discussing.
From Web Pages to Web Communities 21
5 Conclusions and future work
In this paper we have described three kinds of social groups which take part in
Web page creation and usage. We distinguish these groups using their relation to
the Web page - whether they define the intent of the page, whether they create
the page or whether they use the page. By using this analysis we can follow the
evolution of the communities and observe the expectancies, rules and behavior
they share. Obtained information can be surely used to improve the searching
process. From this point of view, Web 2.0 is only a result of the existence and
interaction of these social groups.
Our experiment shows that if we focus ourselves on Web sites and the Web
page content they provide, we can ask interesting questions. These questions may
bear upon the Web sites’ similarity and the similarity of social groups involved
with these pages. For us this shows the direction of further research in which we
will investigate answers to these questions in more detail.
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