Information Retrieval Systems

UNIT 3 INFORMATION RETRIEVAL

SYSTEMS
Structure

3.0 Objectives
3.1 Introduction
3.2 Theoretical Foundations
3.3 Models of Information Retrieval Systems
3.3.1 Models Based on Input and Output
3.3.2 Models Based on Theories and Tools
3.4 IRS : Design and Operation
3.5 Search Strategy
3.6 Evaluation of IRS
3.7 Summary
3.8 Answers to Self Check Exercises
3.9 Keywords
3.10 References and Further Reading

3.0 OBJECTIVES
After reading this unit, you will be able to :
l understand the definition of information retrieval systems;
l know the theoretical foundation and models of information retrieval systems;
l get yourself acquainted with design and operation of IRS; and
l explain the method of searching information from IRS.

3.1 INTRODUCTION
It was Calvin Mooers who in 1950 coined the term “information retrieval” and
described it as “searching and retrieval of information from storage according to
specific subject.” The word retrieval means to discover and bring to the notice of
the users the documents in which information is embedded. Again B.C. Vickery has
described it as “ retrieval is essentially concerned with the structure of the operation
of the device to select documentary information from the store of information in
response to several questions”
The retrieval systems are usually in a state of continuous gradual revision; data are
added or withdrawn; new index points inserted; syndetic relationship changed. The
development of effective retrieval technique has been the core of IR research for
more than 30 years. Nowadays multimedia indexing and retrieval techniques are
being developed to access image, video and sound database without text descriptions.
47
Types of Information Systems The information retrieval system is certainly not a new concept; it is an integral part
of the communication process, a direct outgrowth of the desire among men to
communicate with eachother.
l The classification of retrieval techniques that has been proposed by Hicholas
Belkin and Bruce Croft are:
Retrieval Technique

Exact Partial
Match Match

Individual Network

Structure Feature Spreading

Cluster Browsing
Based Based Activation
Logic Graph Formal Ad hoc

Probabilistic Vector Fuzzy set

Space

Fig. : Classification of Retrieval Techniques

Belkin and Croft distinguish between exact and partial match techniques. Exact
match techniques are currently in use in most of the conventional IR systems. Queries
are usually formulated using Boolean expression and the search patterns within the
query have to match with exactly the text representation of the document to be
retrieved. Partial match retrieval technique as opposed to exact match technique is
categorised into individual and network. Individual techniques search single document
nodes without considering the document collection as a whole. In the feature-based
techniques, documents are represented by sets of features or index terms. The index
can be either defined manually or be computed automatically. In structure-based
techniques, documents are represented in a more complicated structure than just a
set of index terms as used for the feature based techniques.
In network based methods, the set of all documents and their relationship are used
to find the most relevant documents. With this method, the technique query. In
clustering, most similar documents are clustered together and all documents are
grouped into a cluster hierarchy until a ranked list of lowest level clusters are
produced. Spreading activation is similar to browsing. From the start nodes, other
nodes connected to that node are activated. Activated nodes then propagate or
spread themselves through the network.
Theoretically there is no constraint on the type and structure of the information items
to be stored and retrieved with the information retrieval (IR) system. Until recently
information retrieval systems were limited to searching textural information. Gerard
Salton has defined an information retrieval system as a “system used to store items
of information that need to be processed, searched, retrieved, and disseminated to
various user populations.”
According to Alken Kent , any information retrieval system entails a series of processes
48 or steps, which are as follows:
i) Analysis involving perusal of the record and the selection of point of view (or Information Retrieval Systems

analytics).
ii) Terminology and subject heading control involving establishment of some arbitrary
relationships among, ‘analytic’ in the system.
iii) Recording the results of analysis on a searchable medium.
iv) Storage of records or source documents, involving the physical placement of
the record in some location.
v) Question analysis and development of search strategy involving the expression
of a question or a problem.
vi) Conducting of search involving the manipulation or operation of the search
mechanism in order to identify records from the file.
vii) Delivery of results of search involving physical removal or copying of a record
from files.
Thus, any information retrieval system has three components - input, process and
output. The storing of information is the input component. Generally the search or
retrieval of information from the information retrieval system is through a query
processing system. The information stored in the system is indexed using some
indexing technique using key words. The processing system matches the key words
of the query language with that of the key words under which the information items
have been indexed. The matching results into the response output which may be the
answer to the user in response to his request or search for information.

3.2 THEORETICAL FOUNDATIONS

The development of various techniques to retrieve information has been a major
area of research interest and has been renewed from time to time through greater
emphasis on computerised information retrieval systems. The examples of early
theoretical approaches to are classification theory; linguistic theories in the context
of automatic indexing; psychological approaches and the early structural models of
Fairthrone and others. Any information retrieval system is based on some theory.
Theory is a set of sentences in a formal language with a few powerful axioms, some
special rules of inference and a rich body of true theorems that captures the essential
phenomena and concepts. Taking “theory” in its widest sense, any one setting up a
retrieval system must have some theory relating to the function of the system. In
absence of any general accepted theory, any formulation that appears to deal with
or relate to any part of the storage and retrieval process is potentially a part of the
theory of information retrieval.
Swets regarded the retrieval process as having two stages. In response to a request,
the system first calculates for each items of information the value of search functions.
This function discriminates between relevant and non-relevant information because
its distribution for relevant information is different from that for non- relevant one.
The system then selects those items whose match values are highest or higher than a
certain threshold. The classification of retrieval technique as part of theory is already
discussed in the introduction of this Unit.
As early as 1963 Swets developed an evaluation model based on statistical decision
theory. The first book on the theory concerning information appeared in 1961 49
Types of Information Systems describing the principles of index construction or subject description of documents.
The most important application of a concept from logic was the application of Boolean
lattices to logical combinations of descriptors. Another important development was
Shannon’s information theory to indicate desirable statistical characteristics of index
terms. There have been theoretical approaches to IR from the viewpoint of the
function or functions which the system performs. The performance of a system must
be explicitly stated in any theory. While any retrieval system must be based on some
theory of retrieval, such implicit theories are extremely difficult to extract or analyse.
Even some explicitly formulated theories are formulated in such general terms, with
such loose connection between the theory and system design, that they are difficult
to evaluate. There are theories relating to the relevance feedback and manipulation
of wide terms. The “Weighing function” formation ‘of Robertson and Sparck Jones
can be mentioned. Then concerning the indexing and retrieval effectiveness the
important theoretical contributions are by Marton, Kuhns and Cooper. the
probabilistic and utility theoretic indexing by them is worth while to mention. Saltons’
theory of indexing is another important theoretical development in the field of
information retrieval. Attempts are on in the direction of building an integrated general
theory of information retrieval.

3.3 MODELS OF INFORMATION RETRIEVAL

SYSTEMS
1.3.1 Models Based and Input and Output
Different models of information retrieval system can be recognized, based on input
and output aspects. We can group them into 4 basic models viz.
1) Data Retrieval Model
2) Information Retrieval Model
3) Knowledge Retrieval Model
Data Retrieval Model
A data retrieval model calls for the organizational structure of the content (data)
based on various criteria such as, properties of population, clusters and other entities.
A data retrieval model essentially handles data which can be taken as unprocessed
information. There are a number of economics related data retrieval systems providing
various types of socio-economic data. The census is a data retrieval system. Similarly,
data available from national survey organisations and central statistical organization
can be taken to be a numerical data system. The information retrieval systems based
on data also retrieve information. The expression of information, thus, needs be very
precise. In this context the data retrieval model does a simple model of information
retrieval need specific matching technique, viz., a taxonomic structure of various
entities involved and their properties.
Information Retrieval Model
Information is data processed and oriented to a purpose. It actually combines several
data into a relational structure. Information retrieval is, therefore, a more complex
model. It has to comprehend generally multidimensional relationship. It is not amenable
easily to a taxonomic structure. The representation of information may be based on
50
a relational database structure using some associative mathematics. The expression Information Retrieval Systems

of information need is complex and time consuming. It draws out for a long
conversational or browsing process and the informational retrieval model must
incorporate such facilities of interfaces.
A distinction is made between data retrieval and information retrieval. In practice
the distinction is one of degree rather than kind . Figure 2, shows a spectrum of
various attributes of data retrieval and information retrieval:
Information Retrieval Data Retrieval

Retrieval models
Probabilistic Deterministic
Indexing
Derived from contents Complete items

Matching/Retrieval
Partial or Best match Exact match
Query language
Natural Artificial/structured
Result criteria
Relevance Any match
Query specification
Complete Incomplete
Items wanted
Relevant Matching
Error response
Insensitive Sensitive

Fig. 2: Information Retrieval/Data Retrieval Spectrum

In information retrieval, the underlying model of providing access to information is

probabilistic. It is concerned with such subjective and indeterminate issues as whether
(and to what degree) a document satisfies a user’s need for information i.e. whether
it is relevant for that user’s request. Data retrieval on the other hand is deterministic
with regard to retrieval operations. Most information retrieval systems derive their
index element from the content of the items. The derivation may be simple extraction
(such as extracting key words from a text), inferential extraction or it may be intellectual
analysis and assignment of index items. In data retrieval, the element itself, in its
entirety, is the indexing unit. Obviously this spectrum is not really smooth or continuous
since both types of indexing may be present within the same system.
51
Types of Information Systems In the actual retrieval, the algorithms used for matching the query and the index
elements are based on the particular retrieval model. Information retrieval model
leads us to a class of retrieval algorithms that are probabilistic in nature, and may
involve the actual calculation of probabilities and use of statistical inference methods,
or may take another approach based on another model of the document space
(such as Salton’s vector space model), They attempt to find all of the potential
(partial) matches between query and document and to rank them based on some
measure of “goodness” so that the best matches receive the highest rank . The data
retrieval algorithms are deterministic, and therefore demand an exact match between
the query specification and the contents of the database.
The queries in information retrieval systems are commonly expressed as a natural
language statement of the searcher’s needs for information. These queries are
inherently imprecise and may be ambiguous in many cases. In data retrieval, the
query is usually expressed in some sort of structured query language with precise
syntactic and semantic characteristics. The query types, thus, reflect the underlying
model of the retrieval system.
Knowledge Retrieval Model
Knowledge retrieval models are based on the computational reasoning technique
developed in Artificial Intelligence (AI) research. They are based on the knowledge
of human experts. In order to facilitate decision-making and problem- solving,
intelligent knowledge-based information retrieval models are being designed. The
three major components are :
i) Knowledge base
ii) Inference Engine
iii) User interface
The knowledge retrieval model is the most sophisticated model of information retrieval,

3.3.2 Models Based on Theories and Tools

Based on theories, tools and techniques a number of other models of information
retrieval systems have been developed on contemplated. These are:
i) Linguistic Model
ii) Mathematical Model
iii) Psychological Model
iv) Economic Model
Linguistic Models
In linguistic model of information retrieval, we can study the system from the point of
view of the properties of the language. The various ways of storage of information
are eventually based on natural language. In short, the language used has three
functions:
a) It represents the content of documents and other forms of information.
b) The information problem in represented in terms of language.
c) Language is used in computer processing and also in searching and retrieval of
information.
52
The language works on three base : Information Retrieval Systems

i) Semantic base which conveys meaning from one human being to another.
ii) Syntactic base which helps in the formation of semantics by the use of grammar,
and
iii) The vocabulary which supplies different meanings to terms for the formation of
explosion, expressed in sentences, paragraphs etc.
The logical structure of a language and the taxonomy of the language refers to the
relationship between vocabulary and concepts. The vocabulary generally refers to
the logical structure. The vocabulary control indents thesaurus content and technical
glossary control. The indexing language with control of expression terms provides
the basic model for information retrieval. Use of associative mathematics in search
logic and in search expression formulation provides yet another type of language
control in information retrieval.
Mathematical Models
Mathematical models are essentially based on representative mathematics as well
as associative connections. In particular, cluster analysis and clustering techniques
are used on an experimental basis in automatic abstracting and indexing. Use of set
theory and Boolean logic is a familiar method of mathematical modeling of information.
Concept of similarity measures and choice of variable and the combinational aspect
of clustering tries to provide semantic structure for information represented.
Psychological Model
The psycholinguistics approach to information retrieval led to the study of the formation
of concepts in human mind, the way in which the human thinking process arranges
the ideas and present it at the time of inquiry and the types of retrieval it demands
while searching. The studies of Belkins, Brooks and Oddy on anomalous state of
knowledge provides an interesting insight in relation to the information retrieval
process. Further, studies in information retrieval and artificial intelligence have thrown
significant input bringing in a harmonious coupling of psychological theory with
information retrieval.
The Economic Model
The economic model of information retrieval centres on the measure of cost
effectiveness and cost efficiency of information retrieval. These two criteria are based
on the performance of the information retrieval system in relation to input cost as
well as the number of successful outputs. The concept of provision of multiple access
points being used gives a chance for measurement of information transfer. The several
models of information measurement based on statistical and mathematical techniques
have been used for studies in bibliometrics and scientometrics, providing scope for
correlation for economic benefits. However due to various intangible elements in
information retrieval, which cannot be identified, the economic model does not yet
provide a holistic approach to information retrieval.
User Model in Information Retrieval
In IRS, the user model is used to provide assistance to the user in the query
formulation process. The goal is to express the information requirements in the best
possible way. Clearly the best way is the one that provides the system with enough 53
Types of Information Systems input information to retrieve all relevant documents. Frequently users have a hard
time specifying explicitly what exactly they are looking for. It is the task of the user
model component of the IR system to automatically help and complement the user’s
interests based on their previous search behaviour.
User

Get user’s
preference
(query)
Process User Analyse
System output model user input

Construct
formal query

Information
System

Fig. 3: User Model in Information Retrieval System

Figure 3 illustrate the central position that a user model can assume in information
retrieval system. An IR system enhanced with user modeling techniques will normally
start by getting the user’s preferences. For example, there can be a statement of the
user’s interests as a self-description, or it may be a SQL based query. This input is
subsequently analysed, using the user model, and the user model is updated
accordingly. Then the formal query is constructed and processed, based on the
user’s preferences. Afterwards in close interaction with the user mode, the output is
prepared for presentation to the user and the user model is refreshed. Finally, the
user can evaluate the query and restart the whole cycle again if needed.

3.4 IRS DESIGN AND OPERATION

The overriding principle underlying any IR system is that it be governed by the laws
of the host organization it serves. Some of the important factors to be considered in
the design of IRS are :
i) Specify the user class to be served. Distinguish between direct users and
beneficiaries. If there is more than one user or beneficiary class for the same IR
systems, assign and announce clear cut priorities on the basis of matching the
characteristics of the system with those of the user class.
ii) Specify the uses and problem-class for which the system is intended, and fix
priorities among them, although designing an IR system with a defined problem
54 is a difficult task.
iii) Specify the range of information items which should be acquired, organized and Information Retrieval Systems

stored in the system’s database.

iv) Cross-validating and checking inputs to the database.
v) The system should be cost effective to test, restructure, and to edit the database
for significance, relevance, validity, and redundancy (may be periodically or
when flaw occurs).
vi) The hardware software and other components. It is essential to analyse the
hardware and software requirement of the system.
Design
The design of an IR system involves problems of analysis and problems of synthesis.
The objective of analysing an IR system is to arrange a set of feasible IR systems in
a partial ordering. The IR system should be effective and efficient in its functioning.
An IR system, to be viable, should conform with the basic principles governing the
processing of information in the host institution.

Indexing Mannual
Keyboard Full text
Computer Assisted
Auto indexing
Machine Word Deletion File Structure
Tapes Statistics
Input Dictionary
Text Svntactic Serial Clustered
Character Random
Reader
Abstracting

Extraction

Thesauri
Manual Computer
Assisted Mechanical

Query Language

Cued Response Formal Boolean

Weighting Ranked
Numeric Boolean List
Query Logical Syntactic
Natural Language Set
Selection Parsing

Feedback

Manual
Automatic Output

Fig.: Information Retrieval System Components

55
Types of Information Systems
3.5 SEARCH STRATEGY
Basic Search Techniques
In a bibliographical information retrieval environment, searches can be divided into
two main classes-known item search and unknown item search. A known item
search is what is conducted when the user knows something about the item being
sought. This may be any key, such as author, title, publisher, ISBN, and so on. An
unknown item search is conducted when users are not aware of the existence of any
document that may solve their problems. In other words, users do not know whether
or not such an item exists that can meet their information requirements. There are
different types of searches which are helpful to understand the entire process of
search strategies.
Keyword and Phrase Search
A search can be conducted by entering a single search term or a phrase comprising
more than one term. The keyword search is the simplest form of search facility
offered by a search system. In keyword search mode, the system searches the
inverted file ( the index ) for each keyword/term forming the search expression. The
search terms can be entered through the keyboard or can be selected from an index
or vocabulary control tool, such as subject headings lists or thesauri. Search
expressions containing more than one keyword may require the use of Boolean or
proximity operators.
In a phrase search, the system searches for the entire phrase rather than each individual
key word forming the phrase. Phrase searches can be conducted only in those fields
that are phrase indexed. If the index file comprises only single terms, then phrase
search cannot be conducted, unless proximity operators are used whereby the system
will searche for each constituent keyword in the search expression separately, and
retrieve only those records where the keywords occur consecutively. A search phrase
can simply be entered through the keyboard, or selected from an index file or
vocabulary control tools like subject headings lists and thesauri.
Different search systems provide different facilities for conducting key word and
phrase searches. For example, in a Dialog search one can simply enter a key word
or a phrase preceded by the search command. The user can restrict the search to
one or more fields.
Many bibliographical information retrieval systems provide two types of search
facilities for conducting an unknown item search; keyword search and subject search.
A keywords search allows users to enter one or more key words pertaining to their
query. These keywords can be chosen by the user in any combination depending
upon the requirements, and there are several search operators that can be used to
combine several keywords to formulate a search expression. The search keywords
can appear anywhere, or in one or more chosen fields, in the database records. A
subject search allows the user to submit a subject expression that reflects his or her
information requirement. Such a search is conducted on the subject field that contains
the subject headings assigned by the indexes when the database was created. Thus,
a record will be retrieved only when the user’s subject search expression exactly
matches the subject heading assigned by the indexes. For standardizing the process,
and also helping the user identify the appropriate subject headings, IRS uses certain
56 tools, called vocabulary control tools.
Boolean Search Information Retrieval Systems

This is a search technique that combines search terms according to Boolean logic.
Three types of Boolean search are possible. AND search, OR search and NOT
search.

The AND search allows the user to combine two or more search terms using the
Boolean AND operator. The search will then retrieve all those items that contain all
the constituent terms. For example, the search expression “Internet AND computer”
will retrieve all those records where both the terms occur .The search is restricted
by adding more search terms. The more search term are AND ed, the more restricted,
or specific will be the search and as a result the smaller will be the search output.
Sometimes, a search may produce a blank result if too many search term are AND
ed.

Truncation
Truncation is a facility that enables a search to be conducted for all the different
forms of a word having the same common root . As an example, the truncated word
COMPUT* will retrieve items like COMPUTER, COMPUTING,
COMPUTATION, COMPUTE, etc. A number of different options are available
for truncation viz. right truncation(as in COMPUT* example ), left truncation, and
making of letters in the middle of the word. Left- Turn truncation retrieves all words
having the same characters at the right - hand part e.g.*HYL will retrieve words like
METHYL, ETHYL etc. Similarly middle truncation retrieves all words having the
same characters at the left- and right hand parts. For example, a middle truncated
search term COL * will retrieve both the terms COLOUR AND COLOR.
Proximity Search
This search facility allows the user to specify :
1) Whether two search terms should occur adjacent to each other,
2) Whether one or more words occur in between the search terms,
3) Whether the search term should occur in the same paragraph irrespective of the
intervening words, and so on.
The operators used for proximity searching and their meanings differ from one search
system to another. The various types of proximity search facilities and the
corresponding operators are available in CD-ROM and online database.
Field-specific Search
A search can be conducted on all the fields in a database or it may be restricted to
one or more chosen fields to produce more specific results. Specific fields and
codes vary according to the search systems and database.
Limiting Search
Sometimes the user may want to limit a given search by using certain criteria such as
language, year of publication, type of information source and so on. These are called
limiting searches. Parameters that can be used to limit a search are decided by the
database concerned. Below are two examples of limiting searches in Dialog.

57
Types of Information Systems
Limit Qualifier Example
English - Language document only /ENG SELECT
URBAN (s)CR IME?/ENG
Patents only /PAT S TRANSISTOR?/PAT

Range Search
The range search is very useful with numerical information. It is important in selecting
records within certain data ranges. The following options are usually available for
range searching, though the exact number of operators, their meaning, etc. differ
from one search system to another:
l Greater than (>)
l Less than (<)
l Not equal to (1=or < >)
l Greater than or equal to (>=)
l Less than or equal to (<=)
Search Tools :
Library and information professionals have since been using four types of tools for
organizing information. They are:
1) Classification Schemes
The classification schemes such as, Dewey Decimal Classification (DDC) Universal
Decimal Classification (UDC) Library of Congress Classification (LC), Colon
Classification and so on, are used for classifying documents, organization files and
also for the physical organization of documents in libraries.
2) Catalogue Codes
The catalogue codes, such as Anglo-American Cataloguing Rules, Classified
Catalogue Code, etc., are used to prepare catalogue records of documents, which
provide information to a user about what a given library/information center possesses.
3) Standard Bibliographic Record Formats
Standard record formats such as ISBD and MARC (Machine Readable Cataloguing)
formats are used to prepare machine readable records of bibliographic and other
types of documents.
4) Vocabulary Control Devices
Vocabulary control devices such as thesauri and subject headings lists are used to
standardize the terminology, which can be used both at the time of indexing and
searching records.
All these tools can be used for organizing information in various types of information
systems including digital library systems. However, these are only basic search tools
which may be used and there are many more search techniques available for specific
information retrieval systems.
58
 Information Retrieval Systems
3.6 EVALUATION OF IRS
Any information system exists to provide the seeker of information the document
which bears the information or answers his query: The evaluation is a diagnostic
activity to understand the performance of a system. It reveals the strength as also the
weakness of an information system. It informs about the social benefits that accrue
from the system. It also tells us about the economic aspects of the system, such as
cost various aspects etc. On the basis of a careful evaluation one can thus ways for
improving the system, if required. Evaluation is rightly called an investment for the
future.
Evaluation Methodology
The evaluation programme of an information system involves a number of distinct
steps. Let us understand these steps:
1) The first step is to be clear about the scope of evaluation. That is to say the
purpose of evaluation should be very clearly defined. The scope should be
defined precisely before the designing and execution of an evaluation programme.
2) The second step is the designing of the evaluation programme. The design should
be such so that it suits the objectives and purpose defined earlier. The success
of the evaluation programme depends upon the choice of appropriate design.
3) After deciding about the scope and design of the evaluation programme, the
next step is the execution proper. The execution includes the collection of data,
its organization, analysis and, lastly, the drawing of conclusions.
4) The fourth step is to analyse the conclusion and the interpretation of the results.
5) The fifth and the final step is to modify the information system on the basis of the
result of evaluation as revealed in steps 3 and 4.
Irrespective of the methodology followed, the purpose of evaluation of any information
system is to find out how well the input performs and what measures need be taken
for its improvement. Sometimes, the evaluation of a particular IRS may provide a
clue for the design and development of other systems.
Criteria for Evaluation
The criteria on the basis of which an IRS can be evaluated are:
1) Recall and precision and related factors affecting retrieval efficiency
2) Cost
3) Response time
Recall and Precision
The effectiveness of information retrieval can be measured by the ability of that
system to retrieve the relevant documents and hold back the irrelevant ones in a
given collection in relation to a particular query. The ability to inform about the
retrieval of relevant documents and withhold the irrelevant ones are called recall and
precision powers of the system respectively. Though theoretically 100% recall and
precision is desired in practice it is not possible, as these two factors are inversely
proportional to each other. The system in which these two factors are at the optimum
level will be regarded as the best one and would be preferred for application. 59
Types of Information Systems In response to a query, all the relevant document may not be retrieved in a search,
only a part of them may be retrieved. Similarly all the documents retrieved may not
be relevant, though a number of non-relevant documents also remain as not retrieved.
This can be illustrated in the following formats:

Document Retrieved Non retrieved Total

Relevant a b a+b
Non- relevant c d c+d
Total a+c b+d a+b+c+d

Recall is the retrieval of relevant documents by the system. Recall ratio can defined
as the ratio of the number of relevant items retrieved to the total number of relevant
documents in the system. This can be mathematically represented as :

Number of relevant items retrieved

× 100
Total number of relevant items

a
or. . × 100
a+ b
Suppose there are in all 100 relevant document in a file and the index is able to
retrieve only 75 of them and misses 25, then the recall ratio is 75/75+25×100=75%
Precision Ratio
Precision is the capacity of the system to withhold non-relevant document. Precision
ratio may be defined as the ratio of the relevant retrieved documents to the total
number of documents retrieved from the file. Mathematically it may be represented
as:
Number of relevant items retrieved
× 100
Total number of relevant items

a
or. . × 100
a+ b
Suppose the total number of documents retrieved are 150, out of these 75 are
relevant, then the precision ratio is

75 ×100
or 50%
150
Many a time it is difficult to know the actual number of relevant documents in the
store. Nevertheless, findings of recall and precision are helpful in assuring the quality
of I.R.S.
Besides recall ratio and precision ratio, the other relevant measures which provide
the retrieval efficiency of a system are:
1) Noise ratio.
2) Fallout ratio.
60 3) Novelty ratio.
Noise Ratio Information Retrieval Systems

It is complementary to the precision ratio. It shows the numbers of non- relevant

documents out of the total documents retrieved. Mathematically it can be represented
as:
Total No. of non-relevant document retrieved
× 100
Total No. of document retrieved

The lesser the noise ratio the more efficient a retrieval system will be.
Fall out Ratio
It shows how many non-relevant document, out of the total number of document in
the store have been retrieved by the retrieval system. Mathematically it may be put
as:
Total No. of non-relevant document retrieved
× 100
Total No. of document in store

Novelty Ratio
It is the proportion of nascent or new information items, which the system is able to
bring to the attention of information seekers for the first time. Out of the total number
of relevant document, a small percentage may be of such documents which contain
nascent information. If out of the 100 relevant documents there as 15 such documents
the Novelty Raito will be 15% i.e.

Novelty Ratio = 15 ×100 = 15 %

100

An efficient retrieval system will bring to the attention of the user more of such
documents which provide novel or new or nascent information.
Indexing Exhaustivity
The exhaustivity of a system refers to the accuracy and depth with which the various
concepts contained in the system are covered. Exhaustivity is the property of index
description. The indexing exhaustivity is connected with recall power of the system.
A system having high indexing efficiency possess high recall power.
Cost
Cost is an important factor of IR system evaluation. Cost may relate to initial
expenditure required to develop a system and also other direct charges, concerned
with manpower, material, tools and other initial costs. The cost is a composite factor
which also includes the effort involved on the part of the indexer and the time involved
in the preparation of index and also the search time and search efforts on part of
user. Initial cost can easily be measured but the cost of effort would be matter of
experience and realization. If a particular system is less costly than the system it
better than the other. The case of the use of the system by the user can be related to
this aspect.
Response Time
Response time is another important factor for measuring the efficacy of the system.
Response time should be measured while the users are interacting with the system. 61
Types of Information Systems If a system requires less time to retrieve information it would be economic and
would be better than the other taking a longer time to retrieve the same information.
Self Check Exercise
1) Discuss the various information retrieval techniques.
2) Name the information retrieval models based on tools and techniques.
3) Explain search strategy in brief.
4) Discuss the criteria for evaluation of IRS.
Note: i) Write your answers in the space given below
ii) Check your answer with the answers given at the end of this unit.
...................................................................................................
...................................................................................................

3.7 SUMMARY
The development of effective retrieval techniques has been the core of IR research
for more than 30 years. A number of measures of effectiveness have been proposed.
Effective interfaces for text based information systems are a high priority for users of
these systems. With the increase in the use of the internet, there has been a
corresponding increase in the demand for information retrieval system that can work
in wide area network environments. Search engines like INFOSEEK, LYCOS,
etc., index web pages and provide access to them. Developing databases and
providing search and retrieval access in an integrated manner have been the most
important aspect of developing IRS. The technique for indexing and query
optimisation have been the major issues.

3.8 ANSWERS TO SELF CHECK EXERCISES

1) The information retrieval techniques can be identified into exact match and partial
method:
Retrieval Technique

Exact Partial
Match Match

Individual Network

Structure Feature
Cluster Browsing Spreading
Based Based Activation
Logic Graph Formal Ad hoc

Probabilistic Vector Fuzzy set

62 Space
The exact match techniques are currently in use in most of the conventional IR Information Retrieval Systems

systems. Partial match retrieval technique is opposite to exact match technique.

While in the feature based techniques the information is represented by a set of
features or index terms, in the structure based technique the documents are
represented in the a more complicated structure than just a set of index terms.

2) The information retrieval models are :

i) Linguistic Model

ii) Mathematical Model

iii) Psychological Model

iv) Economic Model

3) There are 5 types of search strategies depending upon the kind of queries:

i) Boolean search

ii) Matching Function

iii) Serial Search

iv) Cluster Based Retrieval

v) Interactive search formulation

Boolean search is a technique that combines search terms according to Boolean

logic (operators). Three types of Boolean search are possible : AND search; OR
search and NOT search. The search is basically through key words or phrases. The
key word search is the simplest form of search facility offered by a search system. In
key word search mode, the system searches the inverted file (index) for each key
word forming a search expression. Search expressions may make use of either
Boolean or proximity operators.

e.g. ‘WATER’ AND ‘POLLUTION’

‘POLLUTION’ AND (WATER OR SEA OR RIVER)

‘POLLUTION’ AND (WATER OR SEA OR RIVER)

AND NOT SEWAGE

One can also go for proximity search and field specific search.

4) The criteria on the basis of which an IRS can be evaluated are :

i) Recall and precision

ii) Fallout

iii) Cost novelty

iv) Response time

63
Types of Information Systems
3.9 KEYWORDS
Boolean Search : Developed by George Boole, It is a search
strategy for formulating search query
expressed in terms of index terms (or key
words), combined by the use of logical
operators AND, OR, and NOT.
Feedback : The mechanism by which a system can modify/
improve its performance of a task by taking
account of past performance.
Information Retrieval Systems : The system to store items of information that
need to be processed, searched, retrieved
and disseminated to various user population.

3.10 REFERENCES AND FURTHER READING

Cleverden C.W. (1972). On the inverse relationship of recall and precision. J of
Documentation. 28(3); 195-201.
Doyle, L.B. (1975). Information retrieval and processing. Los Angels : Melville.
Fairthorne, R.A. (1961) The mathematics of classification, towards information
retrieval. London: Butterworths. 1-10.
Foskett, A.C. (1977). The subject approach to information, London : Clive Bingley.
Information storage and retrieval system (1997). IGNOU course material. MLIS-
03. New Delhi. IGNOU.
Lancarter, F.W. (1968). Information Retrieval systems : Characteristics, Testing
and Evaluation. N.Y. : Wiley.
Mooers, C.N (1963). The educational challenge of information science. In automation
and scientific communication. 26th annual meeting of AD1, 1963.
Oddy, R.N. (1977). Information retrieval through man-machine dialogue. J of
Documentation, 33; 1-4.
Rijsbergen, C.J. Van (1979) Information retrieval. 2nd ed. London: Butterworths,
Reprint ed.-198).
Sparck Jones K (1971). Automatic keyword verification for information studies.
London: Butterworths.
Vickery, B.C. (1970). Techniques of information retrieval. London: Butterworths.

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