1. Web-Based Information Retrieval
Patrick Alfred Waluchio Ongwen
Knowledge Management Officer
African Research and Resource Forum

2. Web as Agent of Change
 "ICT is not an end in itself or an agent of
change by itself but when incorporated
into a well managed change process it is a
powerful enabler and amplifier."
Bryn Jones,
 Effective use of Web therefore, requires content,
content management, hyperlinks and navigation
tools, retrieval approaches, among others

3. Introduction
 A critical goal of successful information retrieval
on the web is to identify which pages are of high
quality and relevance to user’s query.
 Each search engine index web page,
representing it by a set of weighted keywords
 A crawler (robot or spider) performs traversal of
the web with goal of fetching high quality pages
for indexing and retrieval.

4. Introduction
 Search engines must filter the most
relevant information matching a user’s
query and present retrieved information in
a way a user will understand.
 Hyperlinks provide a valuable source of
information for web retrieval.
 Hypertext and hypermedia enables searching
the web in non-sequential manner

5. Challenges of Web Information
Retrieval
 Management of huge amount of hyperlinked
pages
 Crawling the web to find appropriate web
sites to index
 Accessing documents
 Measuring the quality or authority of available
information

6. Hypertext
 Non-Linear arrangements of textual material is called
hypertext. The term hyper means extension to other
dimensions.
 Converting text into a multidimensional space
 The term was invented by Ted Nelson in 1965
 Hypertext
“non-sequential writing” Nelson, T. 1987. Literary
Machines.
 Non-linear sequences of information (dictionary,
encyclopaedia, newspaper)
 Hypertext are systems to manage collection of
information that can be accessed non-sequentially.

7. Hypertext
 Consists of a network of nodes and logical links
between nodes
 Node refer to chunks of content or web page
 The variety of nodes and links make hypertext a
flexible structure in which information can be
provided by what is stored in nodes and links to
each node.
 Hypertext retrieval systems are the products of
emerging technology that specifies alternative
approach to the retrieval of information from web

8. Hypertext and Non-Linearity
 Allows a user to follow their own path in a
non-sequential manner to access
information
 Hypertext not usually read linearly
 Links encourage branching off
 History and back button permit backtracking
 The immediacy of following links by clicking
creates a different experience from traditional
non-linearity

9. Structure of Hypertext
 In a hypertext system objects in a database called
nodes, are connected to one another by machine-
supported links. Users follow links to access
information.
 Text augmented with links
 Link: pointer to another piece of text in same or different
document
 Hypertext systems can accessed by selecting link to icons
and following links from node to node
 By searching the database for some key word in the
normal way
 Use of browser to view and navigate hypertext

10. Hierarchical Structure

11. Hierarchical Structure
 Hierarchy is the basis of almost all websites
as well as hypertext
 They are orderly and provide ample
navigational freedom
 Users start at the home page, descend the
branch that most interest them, and continue
making further choices as the branch divides

12. Web-like Structures

13. Web-like structures
 Relatively unsystematic and difficult to
navigate
 Mostly used in works of short stories and
fiction in which artistic considerations may
override desire for efficient navigation

14. Multipath Structures

15. Multipath Structures
 Largely linear and to some extent hierarchical
but offers alternative pathways hence
multipath structures

16. Hypertext Component
Hypertext model:
 The run-time layer, which controls the user
interface
 The storage layer, which is a database
containing a network of nodes connected by
links
 The within-component layer, which is the
content structure inside the node.

17. Hypermedia
(Hypermedia = Hypertext + Multimedia)
 Hypermedia integrate text, images, video,
graphics, sound within Web page or node
 Hyper- representation of textual and non-
textual information in a non-sequential
manner.
 Allows embedding bitmapped images (GIF,
JPEG, PNG)

18. History of Hypertext
 1945: Vannevar Bush describes “memex”
(Atlantic Monthly)
 1965: Ted Nelson coins the term “hypertext”
 1985: Peter Brown, University of Kent,
develops first commercially available
hypertext - Guide
 1986-1990: More sophisticated hypertext
systems developed

19. History …
 1991: Tim Berners-Lee builds IP-based
distributed hypertext system at CERN
Develops UDI/URI, HTTP, and HTML…
 1993: Mosaic, first graphical Web browser,
released
 2002: Work begins on Semantic Web

20. Hypertext and Hypermedia- In
Information retrieval
 Browsing – retrieve information by
association
 Follow links, backtrack
 Maintain history, bookmarks
 Searching – retrieve information by content
 Construct indexes of URLs
 Search by keyword/description of page

21. Cont…
 Hypertext and Hypermedia with new standards,
HTML, XHTML, have brought tremendous revolution
in the creation and delivery of content, as well as
access and processing of information.
 Allows users to navigate within or across a range of
documents from several computer networks.
 Allows browsers and other software to interpret and
process information for different purposes
 Search engines use the links among pages to select
information resources from the Internet.
 Google use the link data to rank pages in order of
their relevance to query.

22. Underlying Principles and
Challenges of IR
 Information retrieval is a complex task
 Query-based IR system must be able to accept
a query about any topic and find texts that
contain the specified information of query.
 IR systems are required to operate in real-time,
which demand they should be fast and efficient.
 Most searches are conducted on the natural
language text, which inherently have all the
ambiguities and imprecision.
 The following are some of the challenges IR
systems face in natural language processing:

23. Synonyms
 Synonym occurs when different words of
phrases mean essentially the same thing.
 For example, the words: “finance”, “fund”,
“support”, may be related depending on context
of inquiry.
 Natural language is filled with many words and
phrases that have similar meanings, and it is
often impossible for users to provide all the
words which might be relevant to the query.
 To address this problem, some IR systems
expand the query to include all the synonymous
words for a given word with the help of
thesaurus.

24. Polysemy
 Polysemy occurs when a single word has
more than one meaning. For example, the
word “shot” can refer to following meanings:
 A shooting, in - He shot at a tiger.
 An attempt, in - I took a shot at playing.
 A photograph, in - He took a nice shot

25. Phrases in Information
Retrieval
 Expressions consisting of multiple words often
have a meaning that is substantially different
from the meaning of the individual words.
 The phrase “Artificial Intelligence” is different
from the individual word “Artificial” and
“Intelligence”, and “Operating System” is
different from: “Operating” and “System”.
 One method for phrase-based indexing is to use
proximity measures to specify the acceptable
distance between the words. WITH or NEAR

26. Object Recognition
 Certain types of information require special
procedures to identify them. For example, dates
come in various forms such as: July 3, 2001,
3.7.2001, as well as 7.3.2001 (American
System). (greater and less than logic <>)

27. Semantics and Role- Relationships
 Some information can only be identified through
semantics.
 If a user is interested in finding out the names of
lecturers teaching the courses in the area of:
“Artificial Intelligence”.
 First, the system must be able to know the
courses related to Artificial Intelligence. These
can be AI, Fuzzy logic, Genetic Algorithms,
Neural Networks, Machine learning.
 This should then be linked to lecturers allocated
the courses.

28. Computable Values
 Determining whether information is relevant
some times depends on a specific
calculation.
 Suppose a user is interested in news paper
article about merger in corporation that
occurred after January, 1995.
 The IR system must identify the documents
using search logic operator LESS THAN or
GREATER THAN <>

29. Text Representation Techniques
 The purpose of IR system is to search the text
database for relevant documents in real-time.
 Consequently, the text database is
preprocessed and stored in a structure which
helps in fast searching.
 This preprocessed form is called text
representation

30. Inverted File Approach
 The inverted file approach is used in text representation.
It allows an IR system to quickly determine what
documents contain a given set of words, and how often
each word appears in the document.
 In inverted file system, each database contains two files
 Text file –normal form in which documents appear in a
database and,
 inverted file- which contain all index terms drawn
automatically from the document records.
 Provides indirect file access

31. Using Probability Methods
 All IR systems draw conclusions about the
content of a document by examining source
representation
 IR must base its conclusions about the
document features, such as the present or
absence of particular word or phrases.
 IR system must take into account these
uncertain relationships to determine the strength
of the relevance of a document/s to a particular
request.

32. Relevance feedback
 Relevance feedback is a technique used by
some IR systems to improve performance on
query by asking the user for feedback about
retrieved texts.
 Evaluation forms given to users to seek their
views on performance of information retrieval
systems.

33. CAT TWO- Search Engines
 Compare the assigned search engines and
competently comment on the following:
 Structure of the search engines
 Indexing techniques used
 Information resources offered
 Links between nodes
 Search facilities and retrieval approaches used
 Ease of use by novice and experienced users
 Interface design and display of screen layout

34. Search Engines
 Group 1: Yahoo and Lycos
 Group 2: Google and Alta Vista
 Group 3: Ask Jeeves and Excite
 Group 4: All the Web and HotBot
 Group 5: Web crawler and MSN
 Group 6: Dogpile and EBay