ISR Unit I Lecture-1 | Data Retrieval Vs IR | Text Mining And IR Relation | B.E. IT|@yogeshborhade24

Summary

This video delves into the first unit of Information Storage and Retrieval for B Information Technology students, focusing on the basics of Information Retrieval (IR). It explains the concepts of data, information, and retrieval, distinguishing between structured, unstructured, and semi-structured data. The script contrasts data retrieval, which fetches data based on keywords, with information retrieval, which finds documents similar to the user's query. It also touches on text mining's role in extracting meaningful patterns from data and its relationship with IR. Examples like SQL for data retrieval and Google for information retrieval are provided for clarity.

Takeaways

• 📘 The video is an introduction to the first unit of the 'Information Storage and Retrieval' subject for Information Technology students, following the SPBO syllabus 2019 pattern.
• 🔍 The first unit covers three main topics: basic concepts of information retrieval (IR), automatic text analysis, and clustering techniques.
• 📚 Basic concepts of IR include subtopics such as data retrieval, information retrieval, text mining, and the relationship between IR and text mining.
• 🔢 Data is defined as a collection of raw facts and figures, unprocessed and potentially meaningless, whereas information is the processed form of data, organized and meaningful.
• 📊 Data can be categorized into structured, unstructured, and semi-structured types, each with distinct characteristics and uses.
• 🔑 Data retrieval is about fetching data based on keywords in a user's query, often used in databases like SQL.
• 📝 Information retrieval, on the other hand, retrieves information based on the similarity between the query and documents, exemplified by search engines like Google.
• 🚫 Data retrieval systems require precise syntax and do not tolerate errors, which can lead to system failure, while information retrieval systems can tolerate minor errors.
• 📈 Information retrieval systems produce approximate and relevant results, sorted by relevance, unlike data retrieval systems that provide exact results.
• 🔑 Text mining is the process of extracting meaningful information from large sets of data, involving tasks like document classification, clustering, and sentiment analysis.
• 🔍 Text mining aims to discover unknown patterns and information, contrasting with information retrieval which requires the user to have a predefined query or search intent.

Q & A

What is the main focus of the first unit of the Information Storage and Retrieval subject?

The first unit of the Information Storage and Retrieval subject focuses on 'Introduction to Information Retrieval' and covers basic concepts of IR, automatic text analysis, and clustering techniques.

What are the three main topics in Unit 1 of the Information Storage and Retrieval subject?

The three main topics in Unit 1 are basic concepts of information retrieval (IR), automatic text analysis, and clustering techniques.

What is the difference between data and information as discussed in the script?

Data is a collection of raw facts and figures, unprocessed and may not have meaning to everyone. Information, on the other hand, is processed data, organized and more meaningful, adding context and relevance to the raw data.

What are the three types of data mentioned in the script?

The three types of data are structured data, unstructured data, and semi-structured data.

Can you explain structured data with an example?

Structured data has a definite structure model or fixed format and is highly organized. An example of structured data is relational databases like SQL, where data is stored in rows and columns with named tables.

What is unstructured data and how does it differ from structured data?

Unstructured data does not have a standard defined structure or a fixed structure model. It can be in any form, such as text, numbers, audio, video, images, etc. It differs from structured data in that it is irregular and does not follow a fixed format.

How does semi-structured data differ from structured and unstructured data?

Semi-structured data is partially structured and partially unstructured. It may have a certain structure, but not all information collected will have an identical structure, unlike structured data which is fully organized and unstructured data which lacks any structure.

What is the key difference between data retrieval and information retrieval?

Data retrieval focuses on retrieving data based on keywords in the query entered by the user, while information retrieval retrieves information based on the similarity between the query and the document content.

What is the role of a search engine like Google in information retrieval?

A search engine like Google plays a crucial role in information retrieval by indexing documents and providing users with a set of relevant documents based on the entered query, sorted by relevance.

How does text mining relate to information retrieval?

Text mining is the process of extracting meaningful information from chunks of data. Information retrieval, on the other hand, is concerned with finding the most effective ways to deliver this extracted information to users based on their needs.

What are some typical tasks included under text mining?

Typical text mining tasks include document classification, document clustering, building ontology, sentiment analysis, document summarization, and information extraction.

What is the main difference between the approach of text mining and information retrieval when it comes to discovering information?

Text mining attempts to discover unknown patterns and information within data, whereas information retrieval requires the user to know beforehand what they are looking for and focuses on retrieving relevant documents based on the user's query.

Outlines

00:00

📚 Introduction to Information Retrieval

This paragraph introduces the first unit of the Information Storage and Retrieval subject, which is part of the final year Information Technology curriculum for B Information Technology students in semester seven. It follows the SPBO syllabus from 2019 and focuses on the basic concepts of information retrieval (IR). The unit is divided into three main topics: basic concepts of IR, automatic text analysis, and clustering techniques. The paragraph specifically covers the subtopics of data retrieval and information retrieval, and the relationship between text mining and IR. It defines data as a collection of raw facts and figures, information as processed data, and retrieval as the process of accessing data or information. The types of data discussed include structured, unstructured, and semi-structured data, with examples provided for each.

05:01

🔍 Data Retrieval vs. Information Retrieval

This paragraph delves into the differences between data retrieval and information retrieval. Data retrieval is based on keyword matching from user queries and is often associated with structured data and deterministic models, such as SQL databases. It requires precise syntax and produces exact results. On the other hand, information retrieval, exemplified by search engines like Google, is based on the similarity between the query and documents, deals with unstructured data, and uses probabilistic models. It tolerates minor errors and provides approximate, relevant results sorted by relevance. The paragraph highlights the distinct approaches and outcomes of these two retrieval methods.

10:03

📘 Text Mining and Its Relation to Information Retrieval

The final paragraph explores the relationship between text mining and information retrieval. Text mining is described as the process of extracting meaningful information from large sets of data, which is inherently meaningless until processed. Information retrieval, in contrast, focuses on finding the most effective ways to deliver this extracted information to users. The paragraph outlines typical text mining tasks, such as document classification, clustering, ontology building, sentiment analysis, and summarization, and contrasts these with the tasks of information retrieval, which include crawling, parsing, indexing, and distributing documents. It also touches on the differences in the discovery of information patterns between the two fields, with text mining uncovering unknown patterns and information retrieval requiring users to have a predefined idea of what they are searching for.

Keywords

💡Information Retrieval (IR)

Information Retrieval (IR) is the process of finding and retrieving information from a large set of data. It is central to the video's theme as it discusses the fundamental concepts and techniques used in IR. The script mentions IR in the context of comparing it with data retrieval, highlighting its focus on unstructured data and the use of similarity between the query and the document to retrieve information, as exemplified by Google's search engine.

💡Data Retrieval

Data Retrieval is the process of accessing and extracting data based on specific keywords or queries. It is a key concept in the video, which distinguishes it from information retrieval by its reliance on structured data and exact keyword matching. The script uses SQL as an example of a data retrieval system, where queries are formulated to extract precise data sets.

💡Structured Data

Structured Data refers to information that is organized in a specific format or model, like rows and columns in a database. The video explains that structured data is highly organized and follows a fixed structure, making it suitable for data retrieval systems. An example given in the script is relational databases such as SQL, where data is stored in a predefined format.

💡Unstructured Data

Unstructured Data is data that does not have a pre-defined structure or format. The video emphasizes that unstructured data can include various forms like text, numbers, images, and audio, and it is the type of data that information retrieval systems deal with. The script contrasts unstructured data with structured data, noting its irregularity and lack of a fixed model.

💡Semi-Structured Data

Semi-Structured Data is partially organized information that contains some structure but is not completely rigid. The video describes it as a hybrid of structured and unstructured data, with examples such as emails, which have a certain format but also allow for unstructured content in the message body. This concept is important as it represents a middle ground in data organization.

💡Text Mining

Text Mining is the process of extracting meaningful information or patterns from large volumes of text data. The video discusses text mining in relation to IR, highlighting that while text mining focuses on discovering new insights, IR is concerned with retrieving known information effectively. The script lists tasks such as document classification and sentiment analysis as part of text mining.

💡Crawling

Crawling is the process by which search engines discover and update information on web pages. The video explains that crawling is a part of the information retrieval process, where search engines identify new or updated content to be indexed. The script uses the example of a blog page to illustrate how crawling works in updating search engine databases.

💡Indexing

Indexing is the process of organizing and storing data in a way that allows for efficient searching and retrieval. The video mentions indexing in the context of search engines, where the information discovered by crawling is saved to index servers to facilitate faster retrieval of information. Indexing is a critical component of the IR system.

💡Ontology

Ontology, in the context of the video, refers to a formal representation of knowledge as a set of concepts within a domain and the relationships between those concepts. It is mentioned as one of the tasks involved in text mining, where building an ontology helps in organizing and understanding the information extracted from text data.

💡Sentiment Analysis

Sentiment Analysis is a text mining task that involves determining the emotional tone behind words to gain an understanding of the attitudes, opinions, and emotions expressed in a piece of text. The video script lists sentiment analysis as one of the tasks performed by text mining, which helps in understanding the subjective information contained within the text.

💡Information Extraction

Information Extraction is the process of automatically identifying and extracting structured information from unstructured data sources. It is mentioned in the script as another task within text mining, where the goal is to pull out specific pieces of information, such as names, dates, or places, from large text corpora.

Highlights

Introduction to the first unit of Information Storage and Retrieval for B Information Technology semester seven.

Explanation of the syllabus for the first unit, focusing on three main topics: basic concepts of IR, automatic text analysis, and clustering techniques.

Subtopics under basic concepts of IR include data retrieval, information retrieval, and the relationship between text mining and IR.

Data is defined as a collection of raw facts and figures, unprocessed and potentially meaningless.

Information is the processed form of data, organized and more meaningful than raw data.

Retrieval is the process of fetching or accessing data or information.

Data is categorized into structured, unstructured, and semi-structured types.

Structured data has a definite structure model, like relational databases.

Unstructured data lacks a standard structure, such as PDF documents containing various media types.

Semi-structured data is partially structured, like emails with a mix of structured and unstructured content.

Difference between data retrieval and information retrieval, with examples of SQL and Google search engine.

Data retrieval is based on keywords and structured data, while information retrieval focuses on unstructured data and document similarity.

Information retrieval tolerates small errors and provides approximate relevant results, unlike data retrieval which produces exact results.

Text mining is the process of extracting meaningful information from large sets of data.

Text mining tasks include document classification, clustering, ontology building, sentiment analysis, and information extraction.

Information retrieval involves crawling, parsing, and indexing documents for efficient search and retrieval.

Crawling is the process by which search engines discover and update web page information.

Text mining aims to discover unknown patterns, whereas information retrieval requires the user to know what they are looking for.

Invitation for viewers to comment, like, share, and subscribe for more content on the channel.

Transcripts

Hello friends welcome back to the

YouTube channel so we are starting with

the unit 1 of information storage and

retrieval subject which which is of a

final year information technology that

is B Information Technology semester

seven so and this will be according to

the spbo syllabus 2019 pattern so the

first unit is Introduction to

information retrieval so we'll first

look at the syllabus for this unit one

so unit 1 is basically having three main

topics which is basic concepts of fire

then automatic text analysis and

clustering techniques so now this base

basic concepts of ir is having Sub sub

topics

so first is data retrieval and

information retrieval second is text

Mining and IR relation and third one is

the IR system block diagram similarly

these two topics are having their sub

topics under them

so here we have lose idea completion

algorithm and these subtopics and in

clustering techniques we have three

algorithms okay so for this video we

will only cover with the

two subtopics of basic concepts of ir

that is the data retrieval and

information retrieval and second

subtopic is text Mining and IR relation

so before starting with the actual

subtopic that is data rectable and

information retrieval will first

understand what is data information and

retrieval okay so what is data so data

is collection of raw facts and figures

or you can say that data is unprocessed

form

then data is collected from different

sources for different purposes okay so

data is collected from different sources

then data May consist of numbers

characters symbols pictures Etc as this

is the unprocessed form so it may have

this combination that is numbers

character symbols

pictures Etc alpha numeric

then data need not have meaning to

everyone so it is not necessary that

data must have the meaning so data is

mostly meaningless

then it is independent entity okay so

now let's move to the next concept that

is information so information is nothing

but the processed data or processed form

of data is called as information

so this unprocessed data is converted

into the information by processing this

data

so this information is organized and

process form of data

then information is more meaningful than

it okay so as we said that data is

mostly and meaningless

so information adds meaning to that data

so that's why it is said that

information is more meaningful than data

and it depends on data okay

and the third concept is retrieval so

retrieval is nothing but to fetch

something or to access something

okay so now we'll understand the types

of data so basically the data is

categorized into the three types first

is structure data second one is

unstructured data and third one is the

semi-structured data so structured data

from the name itself we understand that

any data which is having a definite

structure model or fixed format and is

highly organized is called as a

structured data in simple words the data

which is follows or which is having a

fixed structure is called as structured

data so for example you can consider the

relational databases such as SQL where

data is organized or stored in the form

of rows and columns with name tables so

in relational database we store the data

in the form of rows and columns and the

records inside that are related to that

particular columns right so every column

that is the attribute has some

information related to it in the form of

Records okay so every columns are the

attributes contains the records and they

are related with each other

okay so this is all about the structured

data now next is the unstructured data

so what is unstructured data so

unstructured data means which does not

have the standard defined structure or a

fixed structure and data model is called

as unstructured data and the data is

irregular because it does not have a

fixed structure so it can be in any form

then it is a combination of text numbers

audio video images post Etc

and if for example we can have the PDF

document so in PDF documents there is

not a fixed structure which we have to

follow so it may contain the images it

may contain the text so this is the

unstructured data

then third is the semi-structured data

so from the name only we can say that it

is semi structured so it means what it

is partially structured and partially

unstructured

so data that may have a certain

structure but not all information

collected will have identical structure

that is partially structured data so

example is email so in emails we can

have a particular structure for the name

then CC then VCC then subject so this

follows some specific structure right

but that is not case in the uh message

text area okay in that we can have the

attachments and the attachments can be

image video audio zip files so that

comes under the semi-structured data so

to make it clear we have the this

diagram so here you can see that

structural data is organized in the form

of rows and columns and if follows a

fixed particular structure

why semi structured data it follows

structure

it follows the structure right it

follows structure

some structure but does not completely

follows the structure did

okay so it is some sort of unstructured

as well as structured data here this is

unstructured data so you can see that it

does not follows any structure and it is

randomly arranged data and most of the

data available on the Internet is in the

form of unstructured data

okay so now we'll move to the first

important topic under the basic concepts

of ir so that is a data retrieval and

information retrieval and will

understand this concept in the form of

this difference between them okay

so first is redirectable from the name

only we can understand that it will

retrieve data right but how it will

retrieve data so it will retrieve data

based on the keywords in the query

entered by the user okay so it will

retrieve data based on the query so

whatever the user will enter the query

it will retrieve data according to it

and in information retrieval it

retrieves information based on the

similarity between the query and the

document so before understanding this we

can understand the example so first

example is the SQL for this data

retrieval and Google search engine is

the example for this information

retrieval so in SQL we type a query

right we give a query and according to

the query we will get the output so this

is nothing but it it drives data based

on the keywords in the query entered by

the user and in information retrieval

with type a text or a sentence inside

the search box which is provided on the

Google search engine and depending upon

the similarity between the text that we

have entered

and in that search box and the document

repository whatever the documents are

stored inside that documentary

repository so it will display that

records which matches with our query

okay now next point is it has defined

structure with respect to semantics okay

so it has defined structure so it

basically it deals with the structured

data

and here information retrieval so it

deals with the unstructured data so it

is ambiguous and does not have a defined

structure okay now next is the there is

no room for errors since it results in

complete system failure okay so in SQL

we have to follow a particular syntax

if our syntax is wrong we'll not get the

output and in some cases it might happen

that it will result in complete system

failure okay so here there is no room

for errors while

in information retrieval small errors

are tolerated and will likely go

unnoticed so here even if you do small

spelling mistake it will be

uh it will be tolerated and you will get

the output okay

now next is the data retrieval system

produces exact results so whatever you

have given the query you will get the

exact results okay but that is not the

case in the information retrievable you

will not get the exact results you will

get the approximator relevant results

okay so even uh suppose if you have

typed something in the search box then

you will not get a particular single

record you will get a set of relevant

documents so that is nothing but the

information retrieval system produces

relevant results

then here displayed results are not

sorted by relevance but in information

predictable resources are sorted by

relevance

now next is the data rate level so data

retrievability deterministic model okay

so as I have said that

it needs to follow some data model so

that model is nothing but the

deterministic model so you can remember

this with

uh indeterministic there is D while and

also in the data retrieval data there is

D so you cannot remember this that in

data retrievable you have the

deterministic model while in information

retrieval we have the probabilistic

model

and example we have already seen SQL is

the example for data retrieval and

Google search engine is the example for

information retrieval so this is all

about the data retrieval and information

retrieval thank you so now we'll move to

the next sub topic that is text Mining

and IR relation so what exactly is the

relation between these two both this two

so first is the mining so mining is the

process of extracting some meaningful

information from a chunks of meaningless

data so basically this mining

extracts information and information is

Meaningful right so that's why it is

said that it extracts meaningful

information from a chunk of meaningless

data so data is meaningless

whereas in information retrieval it is

the study that Ponders about most

effective ways of retrieving that

extracted information to user needs so

mining extracts the information so this

information should be retrieved in a

most effective ways to the user right so

that's what the information retrieval is

all about

next is typical text mining task

includes so there are some tasks which

are

included under this text mining so it

includes document classification

document clustering building ontology

sentiment analysis document

summarization information extraction Etc

so these are the tasks which are

performed by the text mining whereas

information retrieval typically deals

with crawling parsing and indexing

documents and distributing documents so

information retrieval is mostly related

with the retrieval of the documents

of the documents

okay so crawling okay we'll first

understand what is crawling so basically

The Crawling is nothing but

the process by the search engine which

discovers the info updated information

about a page web page so suppose we have

a Blog Page and the information is

updated on that particular website so

that information will be discovered by

this crawling process okay and then

depending upon that it will save that

information on the index servers so

index service which will help the users

to retrieve information at a faster Pace

okay

next is text mining attempts to discover

information in a pattern that is not

known beforehand while information

retrieval or service techniques requires

a user to know beforehand what he or she

is looking for obviously so whatever

information we are trying to search on

the internet we are

knowing beforehanded what we are looking

for okay so that is nothing but we are

no uh we are knowing beforehand what we

are trying to search for in the

information readable but that is not the

case in a text mind

so these are the two topics that we are

we have to cover inside this video and

we have covered it so if you have any

doubt inside these two topics you can

comment and if you like the video

understand the concept you can like the

video share it with your friends and

subscribe the channel for more so that's

it for this video we'll see in the next

video