ISR Unit I Lecture-1 | Data Retrieval Vs IR | Text Mining And IR Relation | B.E. IT|@yogeshborhade24
Summary
TLDRThis 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
- π 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.
- π The first unit covers three main topics: basic concepts of information retrieval (IR), automatic text analysis, and clustering techniques.
- π Basic concepts of IR include subtopics such as data retrieval, information retrieval, text mining, and the relationship between IR and text mining.
- π’ 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.
- π Data can be categorized into structured, unstructured, and semi-structured types, each with distinct characteristics and uses.
- π Data retrieval is about fetching data based on keywords in a user's query, often used in databases like SQL.
- π Information retrieval, on the other hand, retrieves information based on the similarity between the query and documents, exemplified by search engines like Google.
- π« 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.
- π Information retrieval systems produce approximate and relevant results, sorted by relevance, unlike data retrieval systems that provide exact results.
- π Text mining is the process of extracting meaningful information from large sets of data, involving tasks like document classification, clustering, and sentiment analysis.
- π 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
π 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.
π 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.
π 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.
Mindmap
Keywords
π‘Information Retrieval (IR)
π‘Data Retrieval
π‘Structured Data
π‘Unstructured Data
π‘Semi-Structured Data
π‘Text Mining
π‘Crawling
π‘Indexing
π‘Ontology
π‘Sentiment Analysis
π‘Information Extraction
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
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