Information Retrieval Concepts Exercise

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timer Asked: Dec 26th, 2017
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Question description

hello
i will attach the file which consist a 4 questions in Knoweldge managment and Information retrieval
any one can help ?
thanks


Tool(s)/Software MS Word, Calculator, EXCEL sample data files. Description Read the below questions carefully and apply KM concepts accordingly. Tasks/Assignments(s) [IR USER INTERFACES FOR SEARCH] Q.1. Apply and provide two examples of CLASSIC MODEL and 2 example of DYNAMIC MODEL of User Search? [IR MODELING] Q.2.1. Provide a REAL-LIFE example of how to apply NEURAL NETWORK? Q.2.2. Provide a REAL-LIFE example of how to apply TERM WEIGHT? [RETRIEVAL EVALUATION] Q.3. Provide a REAL-LIFE example of how to apply SPEARMAN’S RANKING CORRELATION Model? [INDEXING & SEARCHING] Q. 4. Provide a REAL-LIFE example of how to apply FULL INVERTED INDEX? .

Tutor Answer

HKPJ
School: Cornell University

Attached.

Tool(s)/Software
MS Word, Calculator, EXCEL sample data files.
Description
Read the below questions carefully and apply KM concepts accordingly.

Tasks/Assignments(s)
[IR USER INTERFACES FOR SEARCH]
Q.1. Apply and provide two examples of CLASSIC MODEL and 2 example of DYNAMIC
MODEL of User Search?
[IR MODELING]
Q.2.1. Provide a REAL-LIFE example of how to apply NEURAL NETWORK?
Q.2.2. Provide a REAL-LIFE example of how to apply TERM WEIGHT?
[RETRIEVAL EVALUATION]
Q.3. Provide a REAL-LIFE example of how to apply SPEARMAN’S RANKING
CORRELATION Model?
[INDEXING & SEARCHING]
Q. 4. Provide a REAL-LIFE example of how to apply FULL INVERTED INDEX?
.


Running head: KNOWLEDGE MANAGEMENT AND INFORMATION RETRIEVAL

Knowledge Management and Information Retrieval
You’re Name
Professors Name
Course Title
Submission Date

Knowledge Management and Information Retrieval

1

Table of Contents
IR User Interfaces for Search .......................................................................................................... 2
Classic and Dynamic Modeling .................................................................................................. 2
IR Modeling: Neural Networks & Term Weight ........................................................................ 3
Retrieval Evaluation: Spearman’s Ranking Correlation ............................................................. 4
Indexing and Searching: Full Inverted Index.............................................................................. 4
Conclusion ...................................................................................................................................... 5
References ....................................................................................................................................... 6
Appendix ......................................................................................................................................... 7
Classic and Dynamic Model ....................................................................................................... 7
Spearman’s Ranking Correlation ................................................................................................ 7
Full Inverted Index ...................................................................................................................... 8

Knowledge Management and Information Retrieval

2

IR User Interfaces for Search
Information retrieval is a process by which a query generated through the user interfaces
to search retrieving related information. The information retrieval process utilizes attributes
within the search query to display results for obtaining information. The collected data may
contain a ranking correlation while summarizing the document. Once the user inputs their
question, the results demonstrate documents illustrating data or a graphical representation. Users
have the option of retrieving documented data through a full-text or content-based indexing of
either a subject or an object.
Classic and Dynamic Modeling1
In a classic or dynamic perspective, the user query present standard results displaying
relational or corresponding data as a critical part of information retrieval. Primary examples
involving classic and dynamic modeling of document spacing is defined the vocabulary query
associated with users, hardware, and software. The classical model identifies a set of documents
such as # visited resources, # announcements view, # Discussion, parent answering purvey, #
Unread discussion, class and participation mark. If a user queries some discussions, the system
will display retrieved information relating to the probabilities of participation. Another example
would include a set of documents that have a ranking profile regarding the number of discussions
per user.
The dynamic model (Hui Yang, 2017, p. 1) discuss information retrieval processing
through correlation between subjects, subjects agreement, and specific descriptors. A primary
example of dynamic modeling includes changing data, users, systems and information filtering
complexities. The dynamic query suggests that the users interface displays category information

1

Classic and Dynamic Model

Knowledge Management and Information Retrieval

3

to select several sources deriving from the documentation list within the database. Once the user
enters their search query, the results will display a list of documents that consistently change
when data usages added or removed from the query process.
IR Modeling: Neural Networks & Term Weight2
The neural network information retrieval represents language queries that return
information that may or may not useful correspond with the user's query. Neural networking
models and the information retrieval process is a query response that displays data in a
parametric and zone indexes while viewing each document in a vectoring weigh. Neural
networks are a concept encompassing information retrieval through two distinct and different
neural and non-neural approaches. The neural network information retrieval process includes
modeling user behaviors including rank and order display resulting in the generation of a
response related to the text matching. A real-life example would consist of a user seeking
information about the # unread discussion, which associated with the number of conversations
about participation results for a visited resource.
Term weight (Mori, 2017) is a vector space model that integrates information retrieval
processing through ratio and scoring displays. Referred to and known as vector space scoring.
Term weight involves information retrieval processing designed to obtain information and
documentation resources relevant to a user’s query. Scoring and vector spacing is a process that
utilizes a hierarchical clustering of distributed vocabulary words and calculates higher weights
for rendering results and information displays. Using the provided dataset, information retrieval
involving the scoring model extracts sentences through the inverse documentation frequency.

2

Neural Networks & Term Weight

Knowledge Management and Information Retrieval

4

Retrieval Evaluation: Spearman’s Ranking Correlation
Spearman’s ranking correlation is the ranking process performed by the IR systems,
which identifies and recognizes a collection of documents. One example of this process is the
retrieval of information that is assigned a numerical value or scores structured through the
ranking by probability. The database document ranking in a distributed retrieval process, which
categories of ranking retrieval depend on the likelihood of classification. For this example, a
real-world live situation would require a short query that retrieves information based on
#VisITedResources ranks between lower minimal and maximum ranges.3
Indexing and Searching: Full Inverted Index4
Full inverted index (Stefan Büttcher, 2010, p. 19) structured as a postings list consisting
of categorized document id and a specified payload. The process includes information retrieval
of documents through short query comprising of a few terms and access to the records. The full
inverted index utilizes query terms, crawling techniques and retrieval indexing. A real-life
example includes a schema reversed and mapped about the number of occurrences, which, in this
case would involve querying participation marks. Another example of a real-world situation
consists of a distributed dataset in which one short query would produce multiple results
depending o the weight and range of sentencing structures.

3
4

Spearman’s Ranking Correlation Model
Full Inverted Index

Knowledge Management and Information Retrieval

5

Conclusion
The information retrieval (IR) process is a developing and continuous process containing
differentiating stages to produce results associated with a user’s query. The process is designed
to interpret users input and gather resources with associated information. The information
retrieval process evaluates results by locating relevant information within the documents
publications and applying valuable information to a problematic issue of concern. For example,
within the dataset provided, users could retrieve data based on a vocabulary subject or a binary
object. Depending on the structuring of the documents retrieval process, users may also find
probability, ranges and differencing scores within a list that generates sequentially or
numerically.

Knowledge Management and Information Retrieval

References
Hui Yang, M. S. (1st January 2017 p.). Dynamic Information Retrieval Modeling. Ebsco Host, с. 1.
Mori, T. (2017). Information Gain Ratio as Term Weight. Entertainment and Information Science, 1-7.
Stefan Büttcher, C. L. (2010). Information Retrieval: Implementing and Evaluating Search Engines
Inverted Indexing for Text. Cambridge, MA: Massachusetts Institute of Technology.

6

Knowledge Management and Information Retrieval

7

Appendix
Classic and Dynamic Model
#VisITedReso #Announcemen
urces
tsView
80
2
80
95
85
85
75
50
75
50
91
98
96
89
87
86
88
60
90
86
87
82
61
42
95
70
91
62
82
82
94
72
57
74
87
62
65
62

#
Discussi
on
70
90
70
70
70
40
84
84
31
71
86
94
70
53
70
80
89
81
53

ParentAnswering
Survey
No
No
No
Yes
Yes
Yes
Yes
No
No
Yes
No
Yes
Yes
Yes
Yes
No
Yes
No
Yes

# Unread
discussion
Under-7
Under-7
Under-7
Under-7
Under-7
Under-7
Under-7
Under-7
Under-7
Under-7
Under-7
Under-7
Under-7
Under-7
Under-7
Under-7
Above-7
Under-7
Under-7

Cla
ss
H
H
M
H
H
H
H
H
H
H
H
H
H
M
H
H
M
M
H

participation
Mark
100
100
100
100
100
100
99
98
98
98
97
96
96
95
95
95
95
95
92

ParentAnswering
Survey
No
No
No
No
Yes
No
No
No
Yes
Yes
No
No
No

# Unread
discussion
Above-7
Above-7
Under-7
Above-7
Above-7
Above-7
Above-7
Above-7
Above-7
Above-7
Under-7
Under-7
Above-7

Cla
ss
L
L
L
L
L
L
L
L
L
L
L
M
L

participation
Mark
2
0
12
1
0
5
10
5
0
5
11
6
11

Spearman’s Ranking Correlation
#
#VisITedReso #Announcemen Discussi
urces
tsView
on
0
2
50
0
0
4
0
6
13
0
1
12
0
5
80
0
1
8
0
2
41
1
0
11
2
3
70
2
6
5
2
2
8
2
38
12
2
0
50

Knowledge Management and Information Retrieval
2
2
2
2
2
3

13
29
29
29
9
11

53
33
23
23
49
9

8

No
No
No
No
No
No

Above-7
Above-7
Under-7
Above-7
Above-7
Above-7

L
L
M
L
L
L

ParentAnswering
Survey
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes

# Unread
discussion
Under-7
Above-7
Under-7
Under-7
Under-7
Under-7
Under-7
Under-7
Under-7
Under-7
Under-7
Above-7
Under-7
Under-7
Under-7
Under-7
Under-7
Under-7
Under-7

Cla
ss
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H

10
35
35
35
11
13

Full Inverted Index
#
#VisITedReso #Announcemen Discussi
urces
tsView
on
88
30
80
70
44
60
50
40
99
80
50
70
4
39
90
80
40
88
70
19
75
90
70
80
75
23
80
69
35
30
90
49
55
70
50
10
12
40
50
90
55
19
70
19
15
89
40
40
80
2
70
60
11
75
92
50
7

participation
Mark
50
62
70
35
70
13
49
80
65
70
80
50
70
80
50
55
100
14
70


gender
M
M
M
M
M
F
M
M
F
F
M
M
M
M
F
F
M
M
F
M
F
F
M
M
M
M
M
M
M
F
F
M
F
M
M
M
M
F
M
F
F
M
M
F
F
M

GradeID
G-04
G-04
G-04
G-04
G-04
G-04
G-07
G-07
G-07
G-07
G-07
G-07
G-04
G-08
G-08
G-06
G-07
G-07
G-07
G-07
G-07
G-07
G-07
G-07
G-07
G-07
G-07
G-08
G-08
G-08
G-08
G-07
G-07
G-05
G-07
G-07
G-07
G-07
G-06
G-07
G-07
G-07
G-09
G-09
G-09
G-07

Topic
IT
IT
IT
IT
IT
IT
Math
Math
Math
Math
Math
Math
Math
Math
Math
Math
Math
Math
Math
Math
Math
Math
Math
Math
Math
Math
Math
Math
Math
Math
Math
IT
IT
English
IT
English
IT
IT
IT
IT
IT
IT
IT
IT
IT
Quran

Semester participation Mark. #VisITedResources
F
0.75
16
F
1.00
20
F
0.50
7
F
1.50
25
F
2.00
50
F
2.10
30
F
1.75
12
F
2.50
10
F
0.60
21
F
3.50
80
F
2.50
88
F
0.95
6
F
0.25
1
F
1.00
14
F
3.10
70
F
1.50
40
F
1.80
30
F
2.75
13
F
3.45
15
F
3.50
50
F
3.00
60
F
0.50
12
F
0.75
21
F
0.10
0
F
0.00
2
F
0.40
7
F
0.95
19
F
1.25
15
F
3.75
85
F
1.50
90
F
1.75
80
F
0.20
5
F
0.10
19
F
0.40
22
F
0.60
11
F
0.50
12
F
0.40
6
F
2.25
54
F
0.00
0
F
2.50
90
F
0.70
13
F
0.95
20
F
0.50
12
F
1.50
35
F
1.65
33
F
1.00
12

M
F
F
F
F
M
F
F
M
M
M
M
M
F
F
M
M
M
M
M
F
F
F
M
M
M
M
F
M
M
M
M
M
F
M
M
M
M
M
M
M
M
F
M
M
M
F

G-05
G-12
G-12
G-12
G-12
G-12
G-12
G-11
G-12
G-07
G-08
G-07
G-07
G-07
G-05
G-10
G-10
G-10
G-10
G-12
G-12
G-12
G-12
G-07
G-07
G-07
G-07
G-07
G-07
G-07
G-09
G-09
G-11
G-07
G-07
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02

English
English
English
English
English
English
English
English
English
Math
Math
Math
Math
Math
English
IT
IT
IT
IT
English
English
English
IT
IT
IT
IT
Math
Math
Math
Math
Math
IT
IT
Math
Math
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT

F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F

0.35
3.50
0.65
1.45
1.00
1.95
2.75
2.45
0.60
0.80
0.95
0.25
1.40
1.35
1.05
2.50
4.00
0.85
0.00
0.65
1.25
3.25
3.50
1.95
1.10
1.45
0.55
0.95
0.60
2.50
0.75
1.00
0.65
4.00
0.40
0.40
0.35
0.35
2.50
0.05
3.50
0.95
0.15
0.25
0.20
4.00
2.50

10
4
80
39
14
15
90
70
50
14
5
2
60
22
10
70
90
13
5
5
10
75
69
40
30
22
2
30
0
90
70
80
3
90
15
25
5
4
70
0
12
70
12
20
8
90
70

M
F
F
F
F
F
M
M
F
F
M
M
F
F
M
M
M
F
M
M
M
M
M
F
F
F
M
M
M
F
F
M
M
F
M
M
M
F
M
M
M
M
F
F
F
F
M

G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-07
G-07
G-11
G-11
G-07
G-07

IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT

F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
S
S
S
S
S
F
F
F

2.75
4.00
5.00
0.70
0.30
0.50
2.50
2.50
3.50
0.10
0.05
0.00
0.00
0.60
3.50
0.35
4.50
3.50
3.85
0.10
1.25
0.55
0.00
3.85
1.25
1.20
3.00
1.05
0.00
3.30
3.50
0.00
0.60
0.10
2.75
0.60
3.50
0.35
4.00
0.00
0.60
4.00
3.50
3.50
3.00
5.00
5.00

89
44
80
60
2
3
7
90
92
6
7
12
0
26
90
12
70
88
80
5
27
2
8
80
29
35
60
12
4
90
98
6
30
9
33
10
90
9
42
3
60
80
80
80
80
80
85

M
M
M
M
M
M
F
F
F
F
M
M
F
M
M
M
F
M
M
F
F
M
F
F
M
F
M
M
M
M
M
M
M
M
F
M
M
F
F
M
F
M
M
F
M
M
M

G-07
G-07
G-07
G-07
G-07
G-07
G-07
G-07
G-07
G-07
G-11
G-11
G-11
G-11
G-11
G-11
G-11
G-11
G-07
G-07
G-07
G-07
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-02
G-08
G-08
G-08
G-08
G-08

IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT

F
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S

0.50
0.95
0.50
4.00
0.50
3.50
5.00
0.50
3.00
5.00
4.00
1.15
5.00
0.50
3.50
3.50
3.50
3.50
1.10
3.10
4.10
3.60
3.50
3.00
2.75
3.60
2.55
4.00
3.00
1.50
2.00
3.00
1.00
1.00
2.50
0.50
3.00
0.75
4.00
2.00
3.00
2.50
4.25
1.25
0.50
4.35
4.25

60
65
75
90
10
75
75
79
55
75
80
63
91
51
50
58
50
50
51
68
89
80
82
82
72
65
82
92
52
12
62
52
22
52
62
2
52
52
42
51
70
62
75
15
35
65
15

F
M
F
M
M
M
M
M
M
M
M
M
M
M
F
M
F
M
F
M
M
M
M
M
M
M
M
M
M
M
F
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
F

G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-08
G-11
G-08
G-08
G-08
G-07
G-07

IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT
IT

S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
F
S

4.00
3.75
4.25
1.15
0.75
4.75
4.05
2.65
0.75
4.60
4.15
1.35
2.25
0.75
2.25
1.25
1.10
1.45
3.60
3.35
0.85
1.35
3.50
1.35
0.85
4.35
0.35
0.85
0.25
1.35
4.35
4.80
2.85
3.85
4.00
3.10
3.60
4.35
3.60
0.10
0.25
3.65
0.25
2.55
0.45
0.95
1.60

71
71
66
25
25
91
75
75
43
65
75
15
95
90
58
5
51
10
51
31
21
41
81
90
61
81
61
50
21
41
88
61
51
69
51
61
83
81
90
11
3
84
17
42
7
72
80

M
M
M
M
F
M
M
M
M
M
M
F
F
F
F
M
M
M
F
M
M
M
F
F
F
M
M
M
F
M
F
F
M
M
M
M
F
F
M
M
F
F
M
M
M
M
M

G-07
G-07
G-07
G-07
G-07
G-07
G-07
G-07
G-07
G-07
G-07
G-06
G-06
G-06
G-06
G-04
G-04
G-04
G-04
G-04
G-04
G-04
G-04
G-04
G-04
G-04
G-04
G-04
G-04
G-04
G-06
G-06
G-06
G-06
G-06
G-06
G-06
G-06
G-06
G-06
G-06
G-06
G-06
G-06
G-06
G-06
...

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Anonymous
Top quality work from this guy! I'll be back!

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