Question Answer | Information retrieval is the activity of obtaining information

NAME:
STUDENT ID:
COURSE:
TUTOR:

Contents
Question 1...................................................................................................................................................3
Boolean model and vector model...........................................................................................................8
Question 2 IR evaluation.............................................................................................................................9
Bibliography...............................................................................................................................................15

Question 1
 Document 1
Information retrieval is the activity of obtaining information resources relevant to an
information need from a collection of information resources. Searches can be based on full-text
or other content-based indexing.
 Document 2
Information retrieval is finding material of an unstructured nature that satisfies an information
need from within large collections
 Document 3
Information systems is the study of complementary networks of hardware and software that
people and organizations use to collect, filter, process, create, and distribute data.
Based on the documents above;
Step 1: Remove stop words
 Doc 1
Information retrieval activity obtaining information resources relevant information collection
information resources Searches based full-text content-based indexing
 Doc 2
Information retrieval finding material unstructured nature satisfies information within large
collections
 Doc 3
Information systems study complementary networks hardware software people organizations
collect filter process create distribute data
Step2: Apply Porter Stemming algorithm
 Doc 1
Informat retriev activ obtain inform resourc relev inform collect inform resourc Search base full
text content base index
 Doc 2
Informat retriev find materi unstructur natur satisfi inform within larg collect
 Doc 3
Informat system studi complementari network hardwar softwar peopl organ collect filter
process creat distribut data
Step 3: Merge the documents
Merged list

Term Document
activ 1
Base 1
Base 1
collect 2
collect 3
Complementari 3
Content 1
creat 3
data 3
Distribut 3
Filter 3
Find 2
Full 1
Hardwar 3
index 1
Inform 1
Inform 1
Inform 2
Inform 3
Informat 3
Larg 2
Materi 2
Natur 2
Network 3
Obtain 1
Organ 3
Peopl 3
Process 3
Relev 1
Resourc 1
Resourc 1
retriev 1
Retriev 2
Satisfi 2
Search 1
Softwar 3
Studi 3
System 3
Text 1
Unstructur 2
Within 2

Merged list with within-document frequencies
Term Frequency Document
activ 1 1
Base 2 1
collect 1 2
collect 1 3
Complementari 1 3
Content 1 1
creat 1 3
data 1 3
Distribut 1 3
Filter 1 3
Find 1 2
Full 1 1
Hardwar 1 3
index 1 1
Inform 2 1
Inform 1 2
Inform 2 3
Larg 1 2
Materi 1 2
Natur 1 2
Network 1 3
Obtain 1 1
Organ 1 3
Peopl 1 3
Process 1 3
Relev 1 1
Resourc 2 1
Retriev 1 1
Retriev 1 2
Satisfi 1 2
Search 1 1
Softwar 1 3
Studi 1 3
System 1 3
Text 1 1
Unstructur 1 2
Within 1 2
Step 4: Create Posting file

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
Term Frequency Posting
Activ 1 1
Base 2 1
Collect 1 2
Complementari 1 3
Content 1 3
Data 1 3
Distribut 1 3
Filter 1 3
Find 1 2
Full 1 1
Hardwar 1 3
Index 1 1
Inform 5 1
3
3 2

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
Larg 1 2
Materi 1 2
Natur 1 2
Network 1 3
Obtain 1 1
Organ 1 3
Peopl 1 3
Process 1 3
relev 1 1
Resourc 1 2
Retriev 1 1
Satisfi 1 2
Search 1 1
Softwar 1 3
2
Studi 1 3

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
Testing the posting file
By using key words system, information and index a test can be done using a search engine like Google
to determine whether the results of the documents retrieved by the search engine matches the
documents used to create the posting file.
Boolean model and vector model
a. Boolean Model queries
1) Network AND People AND NOT search
This query returns Doc3 only.
2) Process AND (retrieve OR retreive)
This query returns Doc1, Doc2 and Doc3
3) Information AND Data
This query returns Doc1, Doc2 and Doc3
b. Vector Model
Query Q= (Information, system, index)
Doc 1
D1= <3, 1, 0>
Q= <1, 1, 1>
σ ( D1 , Q)= 3 x 1+ 1 x 1+ 0 x 1
√32 +12 +02 √12+12 +12 = 4
√7 √3 = 1.15
Doc 2
D2= <2, 0, 0>
Q = <1, 1, 1>
σ ( D2 , Q)= 2 x 1+ 0 x 1+0 x 1
√22 +02 +02 √12+ 12+12 = 2
√4 √3 = 0.76
System 1 3
Text 1 1
Unstructur 1 2
Within 1 2

Document 3
D= <1, 1, 0>
Q= <1, 1, 1>
σ ( D3 , Q)= 1 x 1+ 1 x 1+ 0 x 1
√12 +12 +02 √12+12 +12 = 2
√2 √3 = 1.07
Boolean Model and Vector Model comparison
In information retrieval both Boolean model and vector model are used to show documents that are
retrieved based on a certain query. Boolean model shows the documents that are returned but does not
show the order in which the documents will be retrieved but vector model shows the documents that
will be retrieved and the order in which the documents will be retrieved from the first document to the
last document.
Question 2 IR evaluation
Search engines
 Bing
 Yahoo
Target
Target 4: Obtain Oracle SQL Tutorial
Queries
 Query 1= Oracle SQL Tutorial
 Query 2= Oracle SQL notes
Bing

Query 1 Query 2
Precision Recall Precison Recall
R 1 0.0714 R 1 0.071
R 1 0.143 R 1 0.143
1 0.214 0.667 0.143
R 1 0.286 0.75 0.214
R 1 0.357 R 0.8 0.289
R 0.833 0.429 0.667 0.289
0.714 0.429 0.571 0.289
0.625 0.429 R 0.625 0.357
R 0.667 0.5 R 0.667 0.429
0.6 0.5 0.6 0.429
0.636 0.571 0.636 0.5
R 0.667 0.643 R 0.667 0.571
0.692 0.714 R 0.615 0.571
0.643 0.714 0.571 0.571
0.6 0.714 R 0.6 0.643
0.5625 0.714 0.5625 0.643
0.529 0.714 0.529 0.643
0.556 0.786 0.556 0.714
R 0.526 0.786 0.526 0.714
R 0.55 0.857 R 0.55 0.786
Interpolation Interpolation
Precision precision Average Precision
0 1 0 1 0 1
0.1 1 0.1 1 0.1 1
0.2 1 0.2 0.8 0.2 0.9
0.3 1 0.3 0.625 0.3 0.8125
0.4 0.833 0.4 0.667 0.4 0.75
0.5 0.667 0.5 0.667 0.5 0.667
0.6 0.667 0.6 0.615 0.6 0.641
0.7 0.526 0.7 0.6 0.7 0.563
0.8 0.55 0.8 0.55 0.8 0.55
0.9 0 0.9 0 0.9 0
1 0 1 0 1 0

Figure 1: Bing precision against recall plotted graph
Yahoo search engine

Query 1 Query 2
Precision Recall Precison Recall
R 1 0.0714 R 1 0.071
R 1 0.143 R 1 0.143
1 0.214 0.667 0.143
R 1 0.286 0.5 0.143
R 1 0.357 R 0.6 0.214
R 0.833 0.429 0.5 0.214
0.714 0.429 0.429 0.214
0.625 0.429 0.375 0.214
R 0.667 0.5 0.444 0.286
0.6 0.5 0.4 0.286
0.636 0.571 R 0.455 0.358
R 0.667 0.643 0.417 0.358
0.692 0.714 R 0.462 0.429
0.643 0.714 0.4 0.429
0.6 0.714 R 0.4375 0.5
0.5625 0.714 R 0.412 0.5
0.529 0.714 0.389 0.5
0.556 0.786 0.368 0.5
R 0.526 0.786 0.4 0.571
R 0.55 0.857
Interpolation Interpolation
Precision precision Average Precision
0 1 0 1 0 1
0.1 1 0.1 1 0.1 1
0.2 1 0.2 0.6 0.2 0.8
0.3 1 0.3 0.455 0.3 0.7275
0.4 0.833 0.4 0.462 0.4 0.6475
0.5 0.667 0.5 0.4375 0.5 0.55225
0.6 0.667 0.6 0.412 0.6 0.5395
0.7 0.526 0.7 0 0.7 0.263
0.8 0.55 0.8 0 0.8 0.275
0.9 0 0.9 0 0.9 0
1 0 1 0 1 0

Figure 2: Yahoo Precision against recall plotted graph
Average Comparison

Bing Yahoo
Average Precision Average Precision
0 1 0 1
0.1 1 0.1 1
0.2 0.9 0.2 0.8
0.3 0.8125 0.3 0.7275
0.4 0.75 0.4 0.6475
0.5 0.667 0.5 0.55225
0.6 0.641 0.6 0.5395
0.7 0.563 0.7 0.263
0.8 0.55 0.8 0.275
0.9 0 0.9 0
1 0 1 0
Figure 3: Bing vs Yahoo average precision vs recall plotted graph
A comparison can be done to determine the superior search engine between Bing and Yahoo based on
the average precision and recall graph shown above. Based on the data reflected on the graph Bing
performs better than yahoo because it has a higher precision and recall. Recall is the number of true

correct results over the total results and Bing retrieves more correct than Yahoo results for both queries.
Precision is the number of true positives over the relevant results thus Bing retrieves the most correct
documents for both queries as compared to Yahoo thus making Bing better than Yahoo for obtaining
oracle SQL tutorial.
Bibliography
Koehrsen, W. (2018). Beyond Accuracy: Precision and Recall – Towards Data Science.
[online] Towards Data Science. Available at: https://towardsdatascience.com/beyond-
accuracy-precision-and-recall-3da06bea9f6c [Accessed 26 Jan. 2019].
Mikulski, B. (2018). Precision vs. recall - explanation – Bartosz Mikulski. [online] Bartosz
Mikulski. Available at: https://mikulskibartosz.name/precision-vs-recall-explanation-
aada1ec393ec [Accessed 24 Jan. 2019].