Chapter 1 Introduction to Web Intelligence

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Oct 29, 2013 (3 years and 9 months ago)

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Chapter
1

Introduction to Web Intelligence


Ms.
Malak

Bagais

[Textbook]: Chapter
1

Objectives


By the end of this lecture, student will be able to:



Identify different aspects related to Web Intelligence



redefines the meanings and processes of
business,
commerce,
marketing,
publishing, education,
research, government,
and development
,
as well as
other aspects of our daily life
.


The Web

What’s the difference?

New challenges of the web


Size


Complexity




we need to modify or enhance existing theories and
technologies to deal with the size and complexity of
the web


What is WI?


Web Intelligence (WI) exploits Artificial
Intelligence (AI) and advanced
Information
Technology (IT) on the Web and Internet.



AI

IT

WI

Web Intelligence (WI)


The term WI was conceived in late
1999


A recent sub discipline in computer science, first WI
conference was the Asia
-
Pacific Conference on WI
-
2001

Intelligent Web


The purposes for our interaction with the Web
:


Learning
new knowledge from the Web


Searching for relevant information


Personalized web pages


Learning about individual users

Information Retrieval


As soon as information archives started building, so
did information retrieval techniques.


Catalogues, index, table of contents


Computerized information storage and retrieval from
1950
and
60
’s


Renewed interest after the advent of the Web

Information Retrieval (IR)

Figure
1.1
Timeline of
information and retrieval
(Courtesy of Ned Fielden, San
Francisco State University)

Modern Information Retrieval


Document representation


Query
representation


Retrieval
model


Similarity between document and query


Rank the documents


Performance evaluation of the retrieval process

Semantic Web

Keywords versus Semantics


The traditional IR is limited by keywords


Key phrases can be used to introduce a bit of
semantics


Semantic Web is an emerging
area

Semantic Web


The Semantic Web proposed by Tim Berners
-
Lee, the
developer of the World Wide Web


The Semantic Web is
concerned with the
representation of data on the World Wide Web.


W
3
C, researchers
and industrial
partners

Web Mining

Data Mining Applied to Web


Data mining is
the process
of discovering knowledge
from large amount of data


Used significantly in commercial and scientific
applications


Adjustment
needs to be made for the Web

Data Mining Applied to Web


Clustering
: Finding natural groupings of users or
pages


Classification and prediction
: Determining the
class or behavior of a user or resource


Associations
: Determining which URLs tend to be
requested
together


Sequence Analysis:
study the order in which URLs
tend to be accessed



Web content mining


Applied to primary data on the Web, text and
multimedia documents


Web structure mining


Hyperlink analysis


Web usage mining


Secondary data consisting of user interaction with
the Web


User profiles

Web Mining

Figure
1.2
Web mining classifications (Courtesy of O. Romanko,
2002
)

Web Usage Mining


Study of data generated by
the surfer’s
sessions or
behaviors


Works with the secondary data from user’s
communications with the Web


web logs, proxy
-
server logs, browser logs


A
Web
-
access

log is an inventory of page
-
reference
data


referred to as
clickstream
data, as each entry corresponds to a
mouse click


Cookies

Web Usage Mining

Figure
1.3
High level web usage mining process
(Courtesy of Srivastava
et al.
,
2000
)

Web Usage Mining


Logs can be observed from two angles:


Server: to advance the design of a website.


Client: assessing a client’s sequence of clicks.


Useful for caching of pages


Efficient loading of Web
pages


Helps organizations efficiently market their products
on the Web.


Can supply essential information on how to
restructure a website

Web
Usage Mining

Applications of Web Usage Mining

Figure
1.4
Applications of web usage mining (Courtesy of O.
Romanko
,
2002
;
Courtesy of
Srivastava

et al
.,
2000
)

Web Content Mining

Web Content Mining


Text mining


Traditional information retrieval


Semantic Web


Multimedia


Images


Audio


Video


Web crawlers

Figure
1.5
Architecture of a search engine (Courtesy of O. Romanko,
2002
)

Web Structure Mining

Web Structure
Mining


Finding the model underlying the link structures of
the Web,


classify web pages.


similarity and relationship between various
websites


Algorithms to model web topology


PageRank


HITS


CLEVER


Primarily useful as a technique for computing the rank
of every web
page


Assumption: if
one web page points to another web
page,
then the
former is approving the significance of
the latter.


Web Structure Mining

Why Web Intelligence?


Better keyword and key
-
phrase based
search


Multimedia information retrieval using Web content
mining


Analyze the shopping trends using data mining


Improve access to website by studying Web usage


Improved structure using Web structure mining

Build Better Web Sites Using Intelligent
Technologies


Matching existing resources to a visitor’s interests


Boost the value of
visitors


Enhance the visitor’s experience on the web site


Achieve targeted resource management


Test the significance of content and web site
architecture

Benefits of Intelligent Web