Paper24

Interactive Visualization of Retrieved Information
Mamoon H. Mamoon, Hazem M. El-Bakry, Amany A. Salama
Faculty of Computer Science & Information system
Mansoura University, EGYPT
Abstract:- Interactive visualization of information
retrieval has become for many applications. Using
information retrieval system backs more retrieval
results, some of them more relevant than other, and
some is not relevant. While using search engine to
retrieve information has grown very substantially,
there remain problems with the information retrieval
systems. The interface of the systems does not help
them to perceive the precision of these results. It is
therefore not surprising that graphical visualizations
have been employed in search engines to assist users.
The main objective of Internet users is to find the
required information with high efficiency and
effectiveness. In this paper we present brief sides of
information visualization's role in enhancing web
information retrieval system as in some of its
techniques such as tree view, title view, map view,
bubble view and cloud view and its tools such as
highlighting and Colored Query Result.
Keywords: Information Retrieval, Information
Visualization
1. Introduction
The typical generic scenario for searching,
retrieving, and displaying information is the
following. A user has an information need about a
certain topic. With a user interface he/she formulates a
query to the system [1]. The query starts an action in
the system (search engine, information retrieval (IR)
system, digital library, or other) [2]. The system will
retrieve (or not) objects and will display them with
appropriate messages and layouts in the same
graphical user interface (GUI) where the user entered
the query [3]. Finally, the user decides if the
documents are relevant or not. He/she can either exit
the system because the information was found or
refine the query and start again [2]. Information
retrieval (IR) is the task of representing, storing,
organizing, and offering access to information items
[1]. The problem for search engines is not only to find
topic relevant results, but results consistent with the
user’s information need. How to retrieve desired
information from the Internet with high efficiency and
good effectiveness is become the main concern of
internet user-based [3].
Search engines interfaces are intuitive and in
some cases restricted by the nature of the WWW.
There is a limited use of color, no pull-down menus,
and limited user interaction. The typical input
interface is a simple box where the user fills the terms
to search plus button to submit the query. The
visualization process of the answers can be text only
or more rich and complex with the use of a graphical
metaphor. In the text only approach, the user gets a list
of the top 10 or 20 best documents that potentially
contains the information. The list usually contains the
title, its URL, size, date, and an abstract of no more
than 4 lines of the document. The user opens each
document until the desired information is finally
located. This is not a problem when the target
document is located in the first 20 answers. It becomes
a problem when the output of a query is a list of
hundreds or thousands of documents. A graphical
metaphor presents a rich interface in which the user
can browse, filter, process, and reformulate the query
[2]. User behavior, performance and attitude were
recorded as well as usability problems. The system
had few usability problems and users liked the
visualizations, but recall performance was poor. The
reasons for poor/good performance were investigated
by examining user behavior and search strategies.
Better searchers used the visualizations more
electively and spent longer on the task, whereas poorer
performances were attributable to poor motivation,
difficulty in assessing article relevance and poor use
of system visualizations [15].
Hence, visualization is an effective tool to
partially solve data overload problems in WWW
retrieval when answers contain hundreds of
documents. The visualization of quantitative
information consist of principles to help achieve the
main goal: communicate complex ideas with clarity,
precision, and efficiency [2].
This survey paper contains problems that
faced web information retrieval system whether
because of the web nature or user activity or searching
process itself. Then, how the search engine works and
models of information retrieval. Next, the meaning of
visualization, information visualization as one of its
application and how it enhances web information
retrieval system. Finally, real systems used
information visualization tool in reducing and solving
some of web information retrieval system's problems.
2. Problem definition
The World Wide Web is a huge, widely
distributed, global source for information services,
hyper-link information, access and usage information
and web site content and organization [4]. There is a
huge quantity of text, audio, video, and other
International Journal of Knowledge Engineering and Research Vol 2 Issue 4 April 2013
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Mamoon H. Mamoon et.al. www.ijker.org217

documents available on the Internet, on about any
subject. Users need to be able to find relevant
information to satisfy their particular information
needs. There are two ways of searching for
information: to use a search engines or to browse
directories organized by categories (such as Yahoo
Directories). There is still a large part of the Internet
that is not accessible (for example private databases
and intranets) [1]. By all measures, the web is
enormous and growing at a staggering rate, which has
made it increasingly intricate and crucial for both
people and programs to have quick and accurate
access to web information and services [4]. It is not
surprising that about 85% of internet users surveyed
claim to be using search engines and search services to
find specific information of interest [5,6]. The same
surveys show, however, those users are not satisfied
with the performance of the current generation of
search engines; the slow speed of retrieval,
communication delays, and poor quality of retrieved
results (e.g., noise and broken links) are commonly
cited problems [5]. Search engines have played a key
role in the World Wide Web's infrastructure as its
scale and impact have escalated. Although search
engines are important tools for knowledge discovery
on the web, they are far from perfect. The poor quality
of retrieved results, handling a huge quantity of
information, addressing subjective and time-varying
search needs, finding fresh information and dealing
with poor quality queries are commonly cited glitches
[4].
There are many problems with different
reasons which it can be by the web nature, users,
search engine and hardware.
2.1 Problem when interacting with the web (web
nature):
The problems appeared when interacting with the web
are:
a. The "abundance" problem:
With the phenomenal growth of the
web, there is an ever increasing
volume of data and information
published in numerous web pages.
According to world wide
websize.com, the indexed web
contains at least 27.56 billion pages
(Sunday, 24 august, 2008) [4], 27.87
billion pages (Sunday, 22 June,
2008) [6] and about 8 billion web
pages were indexed by Google in
2005 [1].
b. Web search results usually have low
precision and recall:
For finding relevant information, the
search services is generally a
keyword-based, query-triggered
process which results in problems of
low precision (difficulty to find
relevant information) and low recall
(inability to index all information
available on the web).
c. Lack of personalization of
information and limited customization to
individual users:
Most knowledge on the web is
presented as natural-language text
with occasional pictures and
graphics. This is convenient for
human users to read and view but
difficult for computers to
understand. It also limits the state of
art search engines, science they
cannot infer contextual meaning.
For example the occurrence of word
'bat' refers to a bird or to a cricket
bat. These factors uphold the
inevitable creation of intelligent
server and client-side systems that
can effectively mine for knowledge
both across the internet and in
particular web localities [4].
d. Heterogeneity:
- Information/data of almost
all types exist on the web, e.g.,
structured tables, texts, multimedia
data, etc.
- Much of the web
information is semi-structured due
to the nested structure of HTML
code.
- Much of the web
information is linked
- The web is noisy: a web
page typically contains a mixture of
many kinds of information, e.g.,
main contents, advertisement,
navigational panels, copyright
notices.[4, 6]
- Much of the web
information is redundant [6].
e. Dynamics:
The freedom for anyone to publish
information on the web at anytime
and anywhere implies that
information on the web is constantly
changing. It is a dynamic
information environment whereas
traditional systems are typically
based on static document collection
[4,6]. This dynamic nature
guarantees that at least some
portions of any manuscript on the
subject will de out-of-date before it
reaches the intended audience,
particularly URLs which are
referenced [5].
f. Duplication:
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Several studies indicate that nearly
30% of the web's content is
duplicated, mainly due to mirroring
[4,6].
g. A comprehensive coverage of all of
the important topics is impossible, because so
many new ideas are constantly being proposed
and either quickly accepted into the internet
mainstream or rejected [5].
The difference between IR and IR on web =
challenges = problem definition that facing searchers
and developers because of web nature
2.2 Problems about users (information searching
activity)
The typical Information Retrieval (IR) systems now
available are characterized by a representation of a
request for information (query) and the system usually
responses with a set of results which most closely
matches the request. Whatever representation of a
request the seeker has to formulate, he has often to
face with problems related to the clear specification of
his information needs [7]. If the search is performed in
a distributed and heterogeneous environment as web,
the search becomes harder: the seeker anxiety grows
up according to the heterogeneity and the amount of
information available in World Wide Web. It
generates the following problems [8]:
1. All users are not created equal:
2. Different users may use different terms to
describe similar information needs; the
concept of "what is relevant" to a user has
only become more and more unclear as the
web has matured and more diverse data have
become available. Because of this, it is of key
interest to search services to discover sets of
identifying features that an information
retrieval system can use to associate a
specific user query with a broader
information need [4].
3. The ambiguity of the natural language
(English or other languages) that makes it
difficult to have perfect matches between
documents and user queries [1].
4. User search behaviour:
5. The users have different expectations and
goals such as informative, transactional and
navigational. Often they compose short, ill-
defined queries and impatiently look for the
results mainly in the top 10 results [6].
6. Problem of vocabulary: "Which term to use?"
The difference in terms of knowledge and
perception between the information providers
and the seeker has been modeled in terms of
informative space and cognitive space. The
former is defined as a set of object and
relations among them held by the system
whereas the latter is defined as a set of
concepts and relations among them held by
individual. Information providers organize
their resources according to their knowledge
and to the vocabulary that concurs in building
the “informative spaces”. If seekers have a
different knowledge background, or a
different purpose, then his cognitive space
has a poor overlapping to the information
space. This make reasonable to assume they
will use different terms to identify the same
concept. So they have to discover which the
proper terms to express a query in the
information space.
7. Query formulation/refinement: “how to
modify the query to find more relevant
information?".
8. Seeker anxiety: The gap between what the
seeker understands and what he thinks he
should understand generates anxiety. This
happens whenever information does not
fulfill his needs.
9. Seeker and provider relationship: seeker and
provider have different skill levels and
different domain of knowledge. Moreover
there is usually no direct interaction among
them.
10. Seeker knowledge: the seeker has often only
a perception of his information needs. He has
a limited knowledge of what he is looking
for.
11. Database selection: "which search engine to
select?" The problem is well known in the
WWW because the actual search engines are
able to cover a limited portion of the web
resources. The seeker has to decide which
search engine to make use of.
12. Information overload: "how to explore many
retrieved documents?" user still has to face
with huge amount of candidates, which are
all pertinent to what he is looking for. He
needs to be supported in the analysis of
heterogeneous information sources to be able
to choose the most suitable ones for his
purpose [8].
13. Query coordination: The seeker may need to
be supported in the choice for queries.
Human behavioral studies during the search
activity have shown that the user is lazy and
usually tends to create short queries and
rarely adopts Boolean expression in his query
criteria. Whenever the seeker needs
information, which might seriously affect the
results of his work, he is forced to a deeper
search.
2.3 Problems faced search engine in IR process on
the web
The unprecedented growth of available data coupled
with the vast number of available online activities has
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introduced a new wrinkle to the problem of search: it
is now important to attempt to determine not only
what the user is looking for, but also the task they are
trying to accomplish and the method by which would
prefer to accomplish it [4].
a. There are many publicly available search engines,
but users are not necessarily satisfied with:
1. The different formats for inputting queries.
2. Speeds of retrieval.
3. Presentation formats of the retrieval results.
4. Poor quality of retrieved information [5,6].
In particular, speed (i.e., search engine and retrieval
time plus communication delays) has consistently
been cited as " the most commonly experienced
problem with the web" in the bi-annual WWW
surveys conducted at the Graphics, visualization, and
Usability Center of Georgia Institute of Technology
63% to 66% of web users in the past three surveys,
over a period of year and a half were dissatisfied with
the speed of retrieval and communication delay, and
the problem appears to be growing worse. Even
though 48% of the respondents in the April 1998
survey upgraded modems in the past year, 53% of the
respondents left a website while searching for product
information because of "slow access". "Broken links"
registered as the second most frequent problem in the
same survey. Other studies also cite the number one
and number two reasons for dissatisfaction as "slow
access" and "the inability to find relevant information"
respectively [5].
b. Limited query interface based on keyword-oriented
search:
It is hard to extract useful knowledge out of
information available because the search service used
to find out specific information on the web is
retrieved-oriented, whereas to extract potentially
useful knowledge out of it, is a data-mining oriented,
data-triggered process [4].
C. Indexing web pages to facilitate retrieval is a much
more complex problem than with classical databases
because of:
D. The enormous number of existing web pages and
their rapid increase.
E. Frequent updating.
F. Removal of spurious information(e.g., newsgroup
discussions, FAQ postings) [5].
G. Handling a huge quantity of information,
addressing subjective and time-varying search needs.
H. Finding fresh information.
I. Dealing with poor quality queries [6].
So we can summarize challenges that face motivating
researchers in web IR in improved system that retrieve
the most relevant information available on the web to
better satisfy a user's information need, or in the other
words, combination of challenges that stem from
traditional information retrieval and challenges
characterized by the nature of the World Wide Web.
3. Web Information retrieval
3.1 How web search engines work
A search engine operates in the following order: Web
crawling, Indexing, and Searching, as declare in figure
n.1. Web search engines work by storing information
about many web pages, which they retrieve from the
HTML itself. These pages are retrieved by a Web
crawler (also known as a spider — an automated Web
browser which follows every link on the site).
Exclusions can be made by the use of robots.txt. The
contents of each page are then analyzed to determine
how it should be indexed (for example, words can be
extracted from the titles, page content, headings, or
special fields called Meta tags). Data about web pages
are stored in an index database for use in later queries.
A query can be a single word. The index helps find
information as quickly as possible. Some search
engines, such as Google, store all or part of the source
page (referred to as a cache) as well as information
about the web pages, whereas others, such as
AltaVista, store every word of every page they find.
This cached page always holds the actual search text
since it is the one that was actually indexed, so it can
be very useful when the content of the current page
has been updated and the search terms are no longer in
it. This problem might be considered a mild form of
linkrot, and Google's handling of it increases usability
by satisfying user expectations that the search terms
will be on the returned webpage. This satisfies the
principle of least astonishment, since the user
normally expects that the search terms will be on the
returned pages. Increased search relevance makes
these cached pages very useful, even beyond the fact
that they may contain data that may no longer be
available elsewhere. When a user enters a query into a
search engine (typically by using keywords),where the
channel connection between his and the system is user
interface the engine examines its index and provides a
listing of best-matching web pages according to its
criteria, usually with a short summary containing the
document's title and sometimes parts of the text. The
index is built from the information stored with the
data and the method by which the information is
indexed. Most search engines support the use of the
Boolean operators AND, OR and NOT to further
specify the search query. Boolean operators are for
literal searches that allow the user to refine and extend
the terms of the search. The engine looks for the
words or phrases exactly as entered. Some search
engines provide an advanced feature called proximity
search, which allows users to define the distance
between keywords. There is also concept-based
searching where the research involves using statistical
analysis on pages containing the words or phrases you
search for. As well, natural language queries allow the
user to type a question in the same form one would
ask it to a human. A site like this would be ask.com.
The usefulness of a search engine depends on the
relevance of the result set it gives back. While there
may be millions of web pages that include a particular
word or phrase, some pages may be more relevant,
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popular, or authoritative than others. Most search
engines employ methods to rank the results to provide
the "best" results first. How a search engine decides
which pages are the best matches, and what order the
results should be shown in, varies widely from one
engine to another. The methods also change over time
as Internet usage changes and new techniques evolve.
There are two main types of search engine that have
evolved: one is a system of predefined and
hierarchically ordered
keywords that humans have programmed extensively.
The other is a system that generates an "inverted
index" by analyzing texts it locates. This first form
relies much more heavily on the computer itself to do
the bulk of the work [10].
3.2 User interface
Information seeking has become increasingly
interactive as tools and services on the WWW have
evolved. Thus, there is more to searching than typing
in a query and waiting for the search engine to display
a set of possible web pages. The only way to achieve
substantial advances in search and browse capabilities
is to combine research and development in human-
computer interaction with research and development
in information retrieval to create highly interactive
systems that engage the user in defining their needs
iteratively and going beyond retrieval to
understanding the corpus and the retrieved
information [9]. The current user interface and its tool
and evaluation in detail in user interface section, and
its more activity and its developing in information
visualization section.
Web Information Retrieval models
Retrieval models form the theoretical basis
for computing the answer to a query. A Retrieval
Model is a formal representation of the process of
matching a query and a document. The model of Web
IR can be defined as a set of premises and an
algorithm for ranking documents with regard to a user
query. More formally, a Web IR model is a quadruple
[D, Q, F, R (qi,dj)] where D is a set of logical views of
documents, Q is a set of user queries, F is a
framework for modeling documents and queries, and
R(qi,dj) is a ranking function which associates a
numeric ranking to the query qi and the document dj.
The model is characterized by four parameters:
1. Representations for documents and queries,
which define the model.
2. Matching strategies for assessing the
relevance of documents to a user query,
which involves learning parameters from
query.
3. Methods for ranking query output.
4. Mechanisms for acquiring user-relevance
feedback.
Retrieval models can describe the Computational
process, for example, how the documents are ranked
and note that how documents or indexes are stored is
implementation. The Retrieval models can also
attempt to describe the User process, for example, the
information need and interaction level. The Retrieval
variables are usually depicted by queries, documents,
terms, relevance judgments, users & information
needs. They can have an explicit or implicit definition
of relevance.
First Dimension: Computational Process: The
Mathematical Basis
According to the first dimension, the models
can be classed into three types: set theoretic, algebraic
and probabilistic models. In the following sections, we
describe instances of each type.
1- Set theoretic models
Documents are represented by sets that
contain terms. Similarities are derived using set-
theoretic operations. Implementations of these models
include the Standard Boolean Model, the Extended
Boolean Model and the Fuzzy Model. The strict
Boolean and fuzzy-set models are preferable to other
models in terms of computational requirements, which
are low in terms of both the disk space required for
storing document representations and the algorithmic
complexity of indexing and computing query-
document similarities.
2- Algebraic models
Documents are represented as vectors,
matrices or tuples. These are transformed using
algebraic operations to a one-dimensional similarity
measure. Implementations include the Vector Space
Model and the Generalized Vector Space Model. The
strength of this model lies in its simplicity. Relevance
feedback can be easily incorporated into it. However,
the rich expressiveness of query specification inherent
in the Boolean model is sacrificed.
3- Probabilistic models
Document's relevance is interpreted as a
probability. Documents and queries similarities are
computed as probabilities for a given query. The
probabilistic model takes these term dependencies and
relationships into account and, in fact, specifies major
parameters such as the weights of the query terms and
the form of the query document similarity. Due to its
simplicity and efficient computation, the Vector
Model is the most widely used model in IR. The
model requires term-occurrence probabilities in the
relevant and irrelevant parts of the document
collection, which are difficult to estimate. However,
this model serves an important function for
characterizing retrieval processes and provides a
theoretical justification for practices previously used
on an empirical basis (for example, the introduction of
certain term-weighting systems).
Second Dimension: User Process: The Relevance
Basis
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Another dimension of defining different
categories of Web IR models can be based on their
applications as follows:
1- Classical models
• Query languages, indexing (Boolean)
• Introducing ranking and weighting (Vector
Space).
2- Topical relevance models
• IR as Bayesian classification, relevance
information, tf.idf weights (BM25)
• Probabilistic models of documents, queries,
topics (Language Modeling).
3- User relevance models
• Combinations of evidence, features, query
language (inference network, Inquery).
4- Linear feature-based models
• Learning weights, arbitrary features,
optimizing effectiveness measures (Ranking
SVM, Linear Discriminant, MRF)
• “Learning to Rank”, learning ranking rather
than classification, preferences [6].
User interface
The current user interface in most search engine as
Google, yahoo!, … etc called List View. The List
View (figure 2) is the classic search results view. It
contains a list of files that, based on Boolean logic,
match the users query. Each file name is shown along
with the number of “hits” from the query. A hit is
defined as one occurrence of one query term in the file
contents. The files are listed in descending order of the
total number of hits.
This view also contains a File Viewer window that
will display the textual contents of a selected file in
order to allow quick review of the file contents.
Matching query terms are highlighted in different
colors to aid the user identify where these terms occur
in the document. Any non-textual content, such as
images, etc. in the actual document are not displayed.
Similarly, some of the source document formatting
will be lost as only line and paragraph breaks are
preserved in the extraction process.
Double clicking on a selected file displays the original
document in a separate window. Such a view,
although elementary, is simple, intuitive, provides
clarity and a quick preview of the documents.
Unsurprisingly, most evaluation participants gave
strong support to this view as both easy to use (89%
who Agree or Strongly Agree) and useful (86%) in
reviewing the results. The evaluators liked the
highlighting of the search terms in the file viewer and
clear indication of the number of hits per result file,
and suggested improvements related to more flexible
sorting of the results and more document and result
information to be made easily available. These results
confirm that the basic de-sign and operation of the
desktop search engine is effective and useful [11].
The following describe efforts to improve search
interfaces by incorporating visual information into
display using techniques from the field of information
visualization.
4. Visualization
Visualization is any technique for creating
images, diagrams, or animations to communicate a
message. Visualization through visual imagery has
been an effective way to communicate both abstract
and concrete. Applications of visualization are
scientific visualization, educational visualization,
information visualization, knowledge visualization,
product visualization, systems visualization, visual
communication, and visual analytics [14].
Most web search engines are text-based.
They display results from input queries as long lists of
pointers, sometimes with and sometimes without
summaries of retrieved pages. Future commercial
systems are likely to take advantage of small,
powerful computers and will probably have a variety
of mechanisms for querying non-textual data (e.g.,
hand drawn sketches, textures and colors, speech) and
better user interfaces to enable users to visually
manipulate retrieved information [5]. From that the
role of information visualization appears as declare in
the following.
4.1 Information visualization
Information visualization is all about making
data visible or more precisely, the patterns that are
hidden in the data. This is a method of presenting data
or information in non-traditional and interactive
graphical forms. By using 2-Dor 3-Dcolor graphics,
text and animation, these visualizations can show the
structure of information, allow one to navigate
through it, and modify it with graphical interactions
[13].
Chaomei chen writes "information
visualization aims to maximize our perceptional and
cognitive abilities to make sense of visual-spatial
representations". Information visualization strives to
make the information more accessible and less
structured to improve usability. In the Web,
Information Visualization provides visualization
approaches to manage big amount of information in a
summarized way and graphical interaction techniques
to manipulate the search results [7]. The human
perceptual system is highly attuned to images, and
visual representations can communicate some kinds of
information more rapidly and effectively than text.
The goal of information visualization (INFOVIS) is to
translate abstract information into a visual form that
provides new insight about that information [12]. And
is not pictures, but insight, It’s not about looking at
pictures; it’s about interacting with them to "amplify
cognition".
Information visualization joins the human’s
capacity of visual thinking and the computer’s
capacity of analytical computing, thereby building a
bidirectional visual and interactive interface between
human user and the information resources. Very few
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information visualization applications do away with
text altogether. The goal is to find the representation
appropriate for a particular task. In many situations
text remains the best form of representation. But we
all know from experience that many complex ideas are
best represented visually. Justas movies did not
eliminate the novel; information visualization will not
eliminate the need for text.
Information visualization will only succeed if
it solves the scalability problem. This view assumes
that the really big problems are the only interesting
ones, and the only hard ones. It also assumes that if
the data set has billions of elements, it is important to
display all of those elements at once. In many
situations the real challenge is to narrow the billions
down to a more reasonable and manageable subset.
This is where data mining begins to play an important
role. Size and scalability are important issues, but it is
a mistake to think that information visualization only
applies to extreme problems.
Information visualization is about speed. It is
sometimes said that information visualization aims to
help us move from slow reading to faster visual
perception, and that it can help us deal with
information overload by allowing us to process more
information faster. This is only true up to a point.
Information visualization is about insight, not
pictures. Insight means understanding and creating
knowledge and learning. Those processes often
require reflection, combination, and rearrangement.
The speed element of information visualization aims
to reduce the cognitive load of certain tasks so that
larger, more complex tasks become possible.
Particular tasks may be made more efficient, but
information visualization can also open up a range of
new tasks that were previously impossible or simply
not feasible because they were too burdensome [13].
Guidelines for designing information
visualizations are available from writers such as Few
(Few, 2006, Few, 2009) and Tufte (Tufte, 1983, Tufte,
1990b). Some of these guidelines overlap with
guidelines from graphic design, including the need to
present information clearly, precisely, and without
extraneous or distracting clutter. Other guidelines
relate to the special purposes of visualization. Good
visualizations use graphics to organize information,
highlight important information, allow for visual
comparisons, and reveal patterns, trends, and outliers
in the data. Visualization guidelines are also derived
from principles of human perception, and urge the
designer to be aware of the perceptual properties
which can affect the design [12].
4.2 Technique for interactive visualization
The challenge is to improve efficiency and
effectiveness of search and result selection. In this
respect, the metaphor that “a picture paints a thousand
words” neatly encapsulates the concept that well
presented graphical views can convey large amounts
of complex information in a simple and easy to
understand manner. It is therefore not surprising that
graphical visualizations have been employed in search
engines to assist users. While each of the individual
visualization might not be new by itself, we believe
that the seamless integration of these views and value-
added functionality in them are novel to assist in the
results review, selection and query refinement.
The design of the user interface was based on the
following research premises:
• Visualizations can assist users to search for
documents.
• Different visualizations can be used to support
different elements of the searching process
(results review and query reformulation).
• Different graphical techniques can be used to
assist users to visualize different kinds of
information.
• Visualizations work best when they are kept
simple.
The search engine GUI has a plug-in view architecture
that allows different views to be created independent
of the searching mechanism [11]. Several interactive
techniques are important to information visualization
[12]. Five views were constructed for use and
evaluation: Tree View, Map View, Bubble View, Tile
View and Cloud View.
Tree View
The Tree View is similar to the List View (in figure 3)
except the result files are organized based on their
underlying folder structure. For each file in the results
list, all of its parent folders are added to the folder
hierarchy (avoiding duplicates). The Result files are
then added into the tree at the appropriate folder for
their physical location.
This view is very similar to the Microsoft Windows
Explorer view. However, only files that match the
query string are displayed and only the parent folders
of these files are included in the tree. The purpose of
this view is to use the physical file structure as part of
the results display. If users have taken the time to
organize their documents into meaningful folders and
hierarchies then this information may be useful when
reviewing results. This view is particularly suited for
thesaurus or taxonomy based folder organizations
where documents are stored in the respective nodes of
this organization scheme. As such, related documents
would already have been assessed and organized into
folder hierarchies that will help users to quickly zoom
into documents of interest. With the familiarity of
Windows Explorer, participants strongly indicated that
this view was easy to use (93%) and useful (91%) in
reviewing the results. They found the view clear and
obvious. 87% of them acknowledged that if they had
organize their documents logically in folders, then this
view would be especially useful for them.
This confirms the design premise that the user’s folder
structure is a useful aid to present search results, as
well as a means to logically organize information in
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thesaurus/taxonomy-like structures that can support
browsing as well as searching.
Map View
The Map View (figure 4) provides an overview of the
relationship between the query terms and the result
files. Each query term is depicted as a blue rectangle
and each result file as a green ellipse. Lines link
related query terms and results files. These are
annotated (in red) with the number of occurrences of
the query term in the result file.
The view can be zoomed and rotated and individual
shapes can be moved around on screen to obtain views
that are more legible and avoid cluttering. If the
mouse is moved over a query term it will display a
popup window that lists all the result files that contain
the query term along with their respective number of
hits (not shown in figure 4). Similarly, if the mouse is
moved over a result file then a popup window will
display all the query terms found in this file with their
respective hit counts (figure 4).
This view shows how individual query terms affect
the results and which files contain one or more query
terms. This bird’s eye view can be used to detect
problems in the query specification if the required
results are not as expected. It will clearly show the
relative influence of each query term in producing the
result files and therefore help the user in deciding
whether the query needs to be reformulated and how
to do so. The evaluation found that slightly over half
of the evaluators (51%) agreed or strongly agreed that
the Map View was useful in reviewing their query
results and reformulating their query. The distribution
of responses for ease of use and usefulness are very
similar. Qualitative comment analysis indicated that
the most useful aspect noted by the evaluators (35%)
was the ability to see an overview of the relationship
between the query terms and the results files. This was
the design premise for the Map View – to provide a
clear overview of the query and its effect on matched
results. However, the view can become very crowded
for complex Boolean queries with a large number of
items displayed resulting in overlapping of the graphic
objects. A significant number of evaluators (36%)
indicated that this caused confusion.
Bubble View
Boolean logic systems make it difficult to judge the
relevance of a result file. The total number of hits
alone is not necessarily a good guide to relevance
especially when document length is taken into
consideration. Therefore, it neither is desirable to
normalize this measure to take into account document
size. In this work, a hit density is calculated as the
number of hits per 1,000 searchable terms (non-stop
words) in the document.
The intention of the Bubble View (figure 5) is to help
the user better assess the relevance of different
documents. The axes of the graph are the number of
hits and the calculated hit density. These measures are
used to distribute the documents along each axis as
they provide good document discrimination in order to
achieve a better visualization. The diameter of the
bubble is determined by the number of query terms
present in the result file and its color represents its file
type.
Quadrant 1 is expected to contain the most relevant
documents as both the number and density of hits is
greatest. Correspondingly, quadrant 4 will be expected
to contain the least relevant documents, as both the hit
count and density are smallest. The display suggests
that documents should be explored in priority
according to the Quadrant numbers. The view
therefore attempts to provide an overview of
document relevance for a given query and aids the
review of documents most likely to be relevant to the
query.
The evaluation results show that 46% of the evaluators
found this view useful in re-viewing their query
results. The majority found the position (65%) and
size (59%) of the bubbles gave them useful
information, which supports the concept of this view
as a means to convey several dimensions about the
relevance of the results documents. The comments
analysis showed that useful features were the ability to
get a quick and easy overview of the relevancy of the
results and the ability to see the hit density.
The major confusion factors related to the display of a
large number of result documents where the titles
overlap and become unreadable and the display was
found to be very cluttered and messy. Suggestions for
potential improvement relate mainly to improving the
layout to increase clarity and for help on how to
interpret the view.
Tile View
The Tile View (figure 6) presents each result file as a
colored tile using a Tree map. A Tree map is “a space-
constrained visualization of hierarchical structures”.
The size of each tile is determined by a measure such
as Total Number of Hits, File Size, and Hit Density
(Hits per 1,000 searchable terms). Using the control
panel, the user can change the measure used to
determine the size of a tile. As before, the color of a
tile is determined by its file type and the display can
be restricted to certain file types.
The Tile View can optionally include the folder
hierarchy of the results files (not shown in figure). In
this variant, all the result files in a specific folder are
grouped together in a “super tile”. Each folder is
enclosed within a tile representing its parent so that
the entire folder structure of the results files can be
displayed. This is an alternative display of the tree
view but with value-added information in the tiles.
The purpose of the Tile View is to allow users to
review the results visually and judge their relevance
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based on different criteria with larger tiles denoting
the most relevant documents.
The results of the evaluation of the Tile View show
over half the evaluators (59%) agreed or strongly
agreed that the Tile View was useful in reviewing
their results. Over two thirds found the tiles to be
obvious and easy to understand (68%) and the ability
to use different criteria to control their sizing was
found to be useful (69%). This supports the design
objective for this view to easily support the use of
different criteria for judging the relevance of the
results documents. The ability to group files by folders
also received strong support with 75% of evaluators
agreeing or strongly agreeing that this was useful. The
comments analysis indicated that useful features were
the ability to change tile size based on different
criteria, the ability to group files by folder and the use
of color to distinguish file types.
Cloud View
The Cloud View (Figure 7) is adapted from the Tag
Clouds popular on social networking sites such as
Flicker. A Tag Cloud is a weighted list which contains
the most popular tags used on that site and the relative
popularity of each tag is indicated by changing its font
size. It is thus easy to see the most popular tags. The
Cloud View creates a Word Cloud based on the
(indexable) content of the result files. The file contents
are examined and stop words and non-indexable terms
are removed. The words are then stemmed and a
simple term count of the documents contents. The top
300 terms are then displayed in a Word Cloud as they
represent the most common indexable terms.
Only files selected in the Results List (in the left hand
window) have their contents included in the Word
Cloud. If the selection of files is changed, the Word
Cloud is dynamically refreshed with information
based on the new selection of files. When the user
clicks on a word in the Word Cloud a popup menu
appears offering the choice to expand (OR), restrict
(AND) or exclude (NOT) the word from the cur-rent
query or to create a new search (NEW) using the
selected word (Figure 7).
The purpose of the Cloud View is to provide users
with the most common words (300 in this instance)
found in the selected files in the result lists thereby
providing an idea of the contents of the result files (i.e.
basically a concordance) and information on potential
words that can be used in the query refinement
process.
The evaluation results for the Cloud View showed that
nearly two thirds of the evaluators found the Cloud
View useful in reformulating their query (63%) and
easy to use (61%). However, the distribution profile
for Question 37 (usefulness of Cloud View) is
different with a bi-polar distribution, with a peak for
Disagree and Agree. This implies that the evaluators
were split into two groups, a conclusion strongly
supported by a review of the comments. Those
evaluators who scored the usefulness of the Cloud
View very low (Strongly disagree or Disagree)
reported a lot of confusion as to the contents of the
Cloud. In other words, they did not find the view
useful because they did not understand what it does.
Those who did rated it highly. This implies that some
users had not seen this type of visualization before nor
understood its potential. [11]
4.3 Web visualization/ visualization tool/
visualization in www
Web visualization tools have been used to
help users maintain a "big picture" of the retrieval
results from search engines, web sites, a subset of the
web, or even the entire web. The most well known
example of using the tree-metaphor for web browsing
is the hyperbolic tree developed by Xerox PARC.
These visualization systems, machine learning
techniques are often used to determine how web pages
should be placed in the 2-D or 3-D space [4]. There is
a study show how the existing tools to browse the
WWW adopt visualization to satisfy seeker needs. It
has been limited to some of the most well known tools
such as Kartoo, Grokker, Web Theme [14], Aduna
AutoFocus. To achieve this purpose the following
research activities have been performed:
1. Identification of the main functionalities
provided by these tools.
2. Analysis both of the correlation among these
functionalities and of the problems in the
information search [7].
Now, How Typical Visualization Tool Works?
1. Visualization tool takes set of key words
from user and gives to search engine.
2. Search engine gives results to visualization
tool as query per document.
3. In each Query, frequent words, no of
occurrences of each frequent word, URL is
there.
4. Creates concepts by taking some
combinations of frequent words.
5. Do text clustering by using concepts.
6. Displays whole documents by using some
visualization technique [13].
The results of these activities are summarized in Table
1 and Table 2. Table 1 is the result of the first activity.
It illustrates the association between the tools
(columns) and some of their functionalities (rows). It
have identified some heterogeneous functionalities:
graphical visualization functionalities (Hierarchical
Visualization, Clustering Visualization, Map Based
Visualization), graphical interaction functionalities
(Visualization Manipulation, Graphical Selection) and
those functionalities that are a combination of them
(Highlighting, Colored Query Result, Filter Result
Representation, co-occurring term
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interaction/visualization). In the following, a
description for each of them is provided:
• Hierarchical Visualization: the visualization
represents its content according to different
levels of granularity. This allows browsing
the information at different levels of detail
(as Grokker).
• Clustering Visualization: the content is
visualized (grouped) according to some
similarity criteria. The groups can be
obtained either by applying a clustering
algorithm (galaxy view) or according to
properties specified by the user (cluster map).
• Map Based Visualization: it imitates the
geographical map appearance; the content is
organized according to thematic terms or co-
occurrence criteria, which are represented as
peaks in the map (i.e. Kartoo represents the
isograms and the name of the mountains
respectively as concentric isolines and
thematic terms on the top of them).
• Visualization Manipulation: the interaction
between user and the graphical representation
allows to re-organize the elements displayed,
to move them and to add new ones (i.e.
Grokker and Kartoo allow to add a new web
site to the search and to insert it in the
displayed graph according to user needs).
• Graphical Selection: the selection of a single
(Grokker, Aduna AutoFocus, Kartoo) or
many elements at a time allows the user to
select different information source such as
URI, PDF or DOC document in Grokker,
Aduna Autofocus, Kartoo or data as in Web
Theme.
• Highlighting: whenever an element of the
visualization is selected, all the sources
related to such element are highlighted too.
Aduna AutoFocus and Kartoo allow
highlighting the related co-occurring terms,
whereas Grokker permits the highlighting
both of the related co-occurring terms and of
the related elements in the visualization.
• Colored Query Result: Web Theme allows to
query the visualized data set and to set a
particular color to each result set. This
facilitates the comparison among different
queries (results).
• Filter Results Representation: some filters
can be applied to the contents shown in the
visualization. For instance, Grokker allows
filtering on the rank, on the domain and on
the source, whereas Kartoo allows filtering
on the co-occurring terms.
• Co-Occurring Terms Visualization: As users
tend to formulate their queries using common
words, a statistical thesaurus expands these
queries with other highly frequent terms that
should help the user in discriminating
relevant documents.
Table 2 is the result of the activities to identify the
contribution of the functionalities to solve problems
related to seeker needs (information overload, query
formulation, vocabulary, and database selection). It is
possible to argue that:
• Graphical visualization functionalities:
provide different results. They give a
structured organization of information
offering the user an overview of the available
information relieving the information
overload problem. They support the query
formulation/ refinement: a correct and rapid
understanding of search results is the
prerequisite to have a successfully query
refinement. Graphical visualization
functionalities provide useful hints to solve
the vocabulary problem by map based and
clustering visualizations. They show co-
occurring terms as cluster representative or in
map representation permitting to learn which
terms belong to the information space and
how terms are related to each other.
• Graphical interaction facilitates the
information overload and query formulation
/refinement problems: visualization
manipulation supports in the analysis of
results by modifying the layout, whereas
graphical selection provides a visual and
intuitive way to select results user is
interested to.
• Finally, the functionalities based on the
integration between interaction and
visualization techniques support in the entire
problem mentioned about. In particular,
functionalities as Colored Query Result allow
comparing the results of different queries
supporting in the queries coordination
problem. Whenever the information about
which search engines have found a result is
maintained, such functionality can be
exploited to compare the results coming from
different search engines supporting the
solution of database problem [7].
5. Conclusion
Despite the success of web as a preferred
source of information, the retrieval of information
from the web is still an unsolved problem with many
different applications probably undiscovered.
Specifically, the operative challenges motivating
researchers in web IR include problems relating either
to data quality or user satisfaction. The problems
facing successful web information retrieval are a
combination of challenges that stem from traditional
information retrieval and challenges characterized by
the nature of the World Wide Web.
The ultimate challenge of web IR research is
to provide improved systems that retrieve the most
relevant information available on the web to better
satisfy a user's information need.In researcher's
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journey to overcome most of the previous problems,
they accept data mining, annotation, semantic web and
visualizing the retrieval results as a helpful techniques
utilize in facing web information retrieval process’
problems. Some of these problems can’t be solved but
do the effort to adapt with them. These are abundance,
dynamic, and heterogeneity because they are a web
information’ characteristic. The most challenges when
interacting with the web are: (1) Attempt to determine
not only what the user is looking for, but also the task
they are trying to accomplish and method by which
they would prefer to accomplish. (2) Creating new
knowledge out of the information available on the
web. These described as challenges that are difficult
needs a lot of skill and effort to do.
Of course, there is always the new
development, and it will be exciting to see what that
future brings to user's search, like nature language
queries; Users could express their queries in natural
language, not just as keywords. This requires deeper
syntactic and semantic analysis of the queries and the
documents. Allowing the user to orally describe the
information need into a microphone is a more natural
way to interact with a search engine. Intelligent and
adaptive web services; problems which can be tackled
by these agents include: finding and filtering
information, customizing information, and automating
completion of simple tasks or perform some other
service without (the user's) immediate presence and on
some regular schedule, and adaptive web site
automatically improves their organization and
presentation based on user access data. Also
Multimedia Queries, Knowledge Retrieval, Using and
building Arabic language in IR system.
References
[1] Diana Inkpen, "Information Retrieval on the
Internet", 2008
[2] Omar Alonso, Ricardo Baeza-Yates, "Model for
visualizing large answer in WWW retrieval"
[3] Hazem M. El-Bakry, and Nikos Mastorakis, "Fast
Information Retrieval from Web Pages," WSEAS
Transactions on Information Science and
Applications, issue 6, vol. 6, June 2009, pp. 1018-
1036.
[4] Mps Bhatia, Akshi Kumar Khalid, " information
retrieval and machine learning: supporting
technologies for web mining research and practice",
webbology, volume 5, number 2, June, 2008
[5]Mei Kobyashi, Koichi Takeda, "information
retrieval on the web", draft paper, 2000, LINK, last
visit
[6] Mps Bhatia, akshi Kumar Khalid, "a primer on the
web information retrieval paradigm", journal of
theoretical and applied information technology, page
657:662
[7] riccardo Albertoni, Alessio Bertone, Monica
Martino, "semantic web and information
visualization",
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.
1.1.61.6774&rep=rep1&type=pdf , 26/2/2013
[8] A. Spoerri, "How Visual Query Tools Can Support
Users Searching the Internet", Proceedings of Eighth
International Conference on Information
Visualization, IEEE, 14-7-2004, pp. 329-334
[9] Gary Marchionini, "User interface for information
retrieval on the WWW", INFORUM2005: 11th
conference on professional information resources,
prague, may 24-26, 2005
[10] http://en.wikipedia.org/wiki/Search_engine, last
visit: 5/3/2013
[11] Schubert Foo , Douglas Hendry, " Desktop
Search Engine Visualization and Evaluation", D.H.-L.
Goh et al. (Eds.): ICADL 2007, LNCS 4822, pp. 372–
382, 2007, © Springer-Verlag Berlin Heidelberg 2007
[12] Marti Hearst, the book "search user interfaces",
published by Cambridge university press, copy right
2009 by Marti A.Hearst, ISBN 9780521113793
[13] Kotaiah Choudary Ravipati, "Visualization of
Web Search Results in 3D", seminar report
[14]
http://en.wikipedia.org/wiki/Visualization_%28compu
ter_graphics%29, last visit: 8/3/2013
[15] A. G. Sutclife, M.Ennis, J. Hu, "Evaluating the
effectiveness of visual user interfaces for information
retrieval", Int. J. Human-Computer Studies (2000) 53,
pages 741-763
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Figure 1: (how search engine works)
Figure 2: (list view)
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Figure 3: (tree view)
Figure 4: (map view)
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Figure 5: (bubble view)
Figure 6: (title view)
Figure 7: (cloude view)
Table 1: Functionalities provided by some existing tolls to browse the WWW
ISSN 2319 – 832X

Grokker Aduna
AutoFocus
Kartoo Web
Theme
Graphic
al
visualiz
ation
Hierarchical Visualization √
Clustering Visualization √ √ √
Map Based Visualization √ √
Gr
ap
hi
cal
Int
er
act
io
n
Visualization Manipulation √ √ √
Graphical Selection √ √ √ √
Interaction
and
Visualization
Highlighting √
Colored Query Result √
Filter Results
Representation
√ √
Co-Occurring Terms
Interaction/Visualization
√ √ √
Table 2: tools functionalities and how they satisfy seeker needs
database vocabulary
Query
formulation/refi
nement
Information
overload
Query
coordination
Graphical
visualization
Hierarchical
Visualization
√ √
Clustering
Visualization
√ √ √
Map Based
Visualization
√ √ √
Graphical
Interaction
Visualization
Manipulation
√ √
Graphical
Selection
√
Interactionand
Visualization
Highlighting √
Colored Query
Result
√ √
Filter Results
Representation
√ √
Co-Occurring
Terms
Interaction/Visual
ization
√ √
ISSN 2319 – 832X

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