Case retrieval nets as a model for building flexible information systems:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin
Akad. Verl.-Ges. Aka
2000
|
| Schriftenreihe: | Dissertationen zur künstlichen Intelligenz
236 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Zugl.: Berlin, Humboldt-Univ., Diss., 1999 |
| Beschreibung: | XV, 200 S. Ill., graph. Darst. |
| ISBN: | 3898382362 |
Internformat
MARC
| LEADER | 00000nam a22000008cb4500 | ||
|---|---|---|---|
| 001 | BV013210119 | ||
| 003 | DE-604 | ||
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| 040 | |a DE-604 |b ger |e rakddb | ||
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| 084 | |a ST 273 |0 (DE-625)143640: |2 rvk | ||
| 084 | |a DAT 600d |2 stub | ||
| 100 | 1 | |a Lenz, Mario |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Case retrieval nets as a model for building flexible information systems |c Mario Lenz |
| 264 | 1 | |a Berlin |b Akad. Verl.-Ges. Aka |c 2000 | |
| 300 | |a XV, 200 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Dissertationen zur künstlichen Intelligenz |v 236 | |
| 500 | |a Zugl.: Berlin, Humboldt-Univ., Diss., 1999 | ||
| 650 | 0 | 7 | |a Künstliche Intelligenz |0 (DE-588)4033447-8 |2 gnd |9 rswk-swf |
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| 655 | 7 | |0 (DE-588)4113937-9 |a Hochschulschrift |2 gnd-content | |
| 689 | 0 | 0 | |a Informationssystem |0 (DE-588)4072806-7 |D s |
| 689 | 0 | 1 | |a Künstliche Intelligenz |0 (DE-588)4033447-8 |D s |
| 689 | 0 | |5 DE-604 | |
| 830 | 0 | |a Dissertationen zur künstlichen Intelligenz |v 236 |w (DE-604)BV005345280 |9 236 | |
| 856 | 4 | 2 | |m DNB Datenaustausch |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009000573&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-009000573 | |
Datensatz im Suchindex
| _version_ | 1807504655659302912 |
|---|---|
| adam_text |
CONTENTS
I
BACKGROUND
1
1
INTRODUCTION
3
1.1
THESIS
OVERVIEW
.
4
1.1.1
BACKGROUND
.
4
1.1.2
FOCUS
.
6
1.1.3
OBJECTIVES
.
7
1.1.4
RESULTS
.
8
1.1.5
APPLICATIONS
.
8
1.2
PREREQUISITES
.
10
1.3
GUIDE
TO
THE
THESIS
.
10
2
A
BRIEF
INTRODUCTION
TO
CBR
13
2.1
ORIGINS
OF
CBR
.
13
2.1.1
NORMATIVE
APPROACHES
IN
AI
.
13
2.1.2
HISTORIC
ROOTS
OF
CBR
.
14
2.1.3
THE
HISTORY
OF
CBR
IN
EUROPE
.
15
2.2
BASIC
CONCEPTS
OF
CBR
.
17
2.2.1
THE
GENERAL
IDEA
OF
CBR
.
17
2.2.2
CASES
AS
PROBLEMS
AND
SOLUTIONS
.
17
2.2.3
SIMILARITY
OF
CASES
.
18
2.2.4
A
GENERAL
PROCESS
MODEL
OF
CBR
.
18
2.2.5
KNOWLEDGE
CONTAINERS
.
19
2.2.6
TYPES
OF
CBR
SYSTEMS
.
20
3
INFORMATION
COMPLETION
25
3.1
CASE
=
PROBLEM
+
SOLUTION
REVISITED
.
25
3.1.1
THE
TRADITIONAL
VIEW
ON
CASES
.
25
3.1.2
CONSEQUENCES
FOR
APPLICATIONS
OF
CBR
.
26
3.1.3
CONSEQUENCES
FOR
DECISION
SUPPORT
PROCESSES
.
27
3.2
REPRESENTATION
FOR
INFORMATION
COMPLETION
.
28
3.2.1
INFORMATION
ENTITIES
.
28
3.2.2
CASES
AND
QUERIES
AS
SETS
OF
IES
.
29
3.2.3
TYPES
OF
IES
.
29
3.2.4
REPRESENTATION
OF
RELATIONSHIPS
BY
IES
.
30
3.3
INFORMATION
COMPLETION
PROCESSES
.
30
XII
CONTENTS
II
THEORY
OF
CASE
RETRIEVAL
NETS
33
4
STARTING
POINTS
FOR
CRNS
35
4.1
DISTRIBUTED
REPRESENTATIONS
.
35
4.2
RETRIEVAL
WITHOUT
SEARCH?
.
36
4.2.1
REMINDINGS:
SEARCH
VERSUS
RECONSTRUCTION
.
36
4.2.2
CASE
RETRIEVAL
BY
SEARCHING
.
37
4.2.3
CASE
RETRIEVAL
BY
ASSOCIATION
.
38
4.3
OBJECTS,
CASES,
AND
INDEXES
.
39
5
BASIC
CASE
RETRIEVAL
NETS
41
5.1
BASIC
IDEAS
OF
CASE
RETRIEVAL
NETS
.
41
5.1.1
BITS
AND
PIECES
.
41
5.1.2
AN
ILLUSTRATION
.
42
5.2
FORMAL
MODEL
OF
BASIC
CASE
RETRIEVAL
NETS
.
43
5.2.1
THE
BCRN
MODEL
.
43
5.2.2
SIMILARITY
PROPAGATION
.
43
5.3
ADVANTAGES
OF
BCRNS
.
44
5.3.1
COMPLETENESS
AND
CORRECTNESS
.
44
5.3.2
EFFICIENCY
.
48
5.3.3
FLEXIBILITY
.
51
5.3.4
SIMILARITY
AS
ACCEPTANCE
.
52
5.4
LIMITATIONS
.
56
5.4.1
RETRIEVAL
BASED
ON
ADAPTABILITY
.
56
5.4.2
STRUCTURAL
SIMILARITIES
.
56
5.5
MINOR
EXTENSIONS
OF
BCRNS
.
56
5.5.1
DYNAMIC
WEIGHTING
.
56
5.5.2
COMPUTATIONAL
NODES
.
58
6
EXTENDED
MODELS
OF
CRNS
61
6.1
CONCEPTUAL
CASE
RETRIEVAL
NETS
.
61
6.1.1
THE
CCRN
MODEL
.
61
6.1.2
RETRIEVAL
IN
CCRNS
.
63
6.1.3
AN
ILLUSTRATION
.
64
6.1.4
TRANSLATION
TO
BCRNS
.
64
6.2
MICROFEATURE
CASE
RETRIEVAL
NETS
.
68
6.2.1
MODEL-BASED
SIMILARITY
ASSESSMENT
.
68
6.2.2
CONTEXT-DEPENDENT
SIMILARITY
ASSESSMENT
.
69
6.2.3
LEARNING
IN
MFCRNS
.
70
6.3
OBJECT-DIRECTED
CRNS
.
70
6.3.1
THE
OCRN
MODEL
.
70
6.3.2
RETRIEVAL
IN
OCRNS
.
71
6.4
LAZY
PROPAGATION
OF
SIMILARITY
.
72
6.4.1
AN
EXAMPLE
DOMAIN
.
74
6.4.2
HEURISTIC
RESTRICTIONS
.
75
6.4.3
THE
IDEA
OF
LAZY
PROPAGATION
.
75
6.4.4
FORMAL
REQUIREMENTS
.
77
6.4.5
IMPROVEMENT
1:
AVOIDING
A-ERRORS
.
80
6.4.6
IMPROVEMENT
2:
REDUCING
/3-ERRORS
.
82
6.4.7
BENEFITS
FOR
HUGE
CASE
BASES
.
84
6.4.8
NOTES
ON
IMPLEMENTATION
.
85
CONTENTS
XIII
III
CRNS
FOR
BUILDING
FLEXIBLE
INFORMATION
SYSTEMS
87
7
GENERAL
FRAMEWORK
89
7.1
TYPES
OF
KNOWLEDGE
TO
REASON
UPON
.
89
7.1.1
CASE
BASES
AS
VIEW
ON
DATA
.
89
7.1.2
APPLICABILITY
.
91
7.1.3
THE
KNOWLEDGE
TRIANGLE
REVISITED
.
91
7.2
GENERAL
COMPONENTS
.
91
7.2.1
RETRIEVAL
SERVER
.
92
7.2.2
GRAPHICAL
USER
INTERFACE
.
92
7.2.3
UPDATE
MODULE
.
93
7.2.4
DATA
SERVER
.
93
7.3
BUILDING
A
CRN-BASED
INFORMATION
SYSTEM
.
94
7.3.1
DOMAIN
ANALYSIS
.
94
7.3.2
SYSTEM
SPECIFICATION
.
96
7.3.3
SYSTEM
IMPLEMENTATION
.
98
7.3.4
SYSTEM
MAINTENANCE
.
98
8
CBR
FOR
PRODUCT
SELECTION
AND
EVALUATION
101
8.1
BASIC
IDEAS
OF
E-COMMERCE
.
101
8.1.1
REQUIREMENTS
FOR
E-COMMERCE
APPLICATIONS
.
102
8.1.2
INTELLIGENT
SUPPORT
FOR
E-COMMERCE
APPLICATIONS
.
102
8.2
THE
V
IRTUAL
T
RAVEL
A
GENCY
.
103
8.2.1
DESCRIPTION
OF
THE
DOMAIN
.
103
8.2.2
MOTIVATION
FOR
A
CBR
APPROACH
.
105
8.2.3
ADVANTAGES
OF
CRNS
.
105
8.2.4
DOMAIN
ANALYSIS
.
106
8.2.5
SYSTEM
SPECIFICATION
.
108
8.2.6
SYSTEM
IMPLEMENTATION
.
108
/
8.2.7
SYSTEM
MAINTENANCE
.
.ILL
8.2.8
CURRENT
STATE
OF
THE
V
IRTUAL
T
RAVEL
A
GENCY
.
ILL
8.2.9
EVALUATION
.
112
8.3
RELATED
PROJECTS
.
113
8.3.1
REAL
ESTATE
ASSESSMENT
.
113
8.3.2
CBR-S
ELLS
.
117
9
CBR
FOR
KNOWLEDGE
MANAGEMENT
121
9.1
BASIC
IDEAS
OF
KNOWLEDGE
MANAGEMENT
.
121
9.1.1
ASPECTS
OF
KNOWLEDGE
MANAGEMENT
.
122
9.1.2
THE
KNOWLEDGE
CONTAINED
IN
DOCUMENTS
.
123
9.2
TEXTUAL
CBR
FOR
KNOWLEDGE
MANAGEMENT
.
124
9.2.1
METHODOLOGICAL
DIFFERENCES
TO
OTHER
AREAS
.
124
9.2.2
KNOWLEDGE
CONTAINERS
FOR
TEXTUAL
CBR
.
125
9.2.3
KNOWLEDGE
REPRESENTATION
VIA
KNOWLEDGE
LAYERS
.
126
9.2.4
KNOWLEDGE
SOURCES
.
127
9.2.5
THE
HOTLINE
SCENARIO
.
130
9.2.6
THE
CBR-A
NSWERS
SYSTEM
.
131
9.3
THE
SIMATIC
K
NOWLEDGE
M
ANAGER
.
131
9.3.1
DESCRIPTION
OF
THE
DOMAIN
.
131
9.3.2
MOTIVATION
FOR
A
TEXTUAL
CBR
APPROACH
.
132
9.3.3
ADVANTAGES
OF
CRNS
.
132
9.3.4
DOMAIN
ANALYSIS
.
133
XIV
CONTENTS
9.3.5
SYSTEM
SPECIFICATION
.
134
9.3.6
SYSTEM
IMPLEMENTATION
.
136
9.3.7
SYSTEM
MAINTENANCE
.
138
9.3.8
CURRENT
STATE
OF
THE
SKM
.
138
9.4
EVALUATION
OF
TEXTUAL
CBR
.
138
9.4.1
EVALUATION
METHODOLOGY
.
139
9.5
RELATED
PROJECTS
.
144
9.5.1
FA
LL
Q
.
144
9.5.2
THE
EXPERIENCEBOOK
.
146
IV
DISCUSSION
149
10
RELATED
WORK
151
10.1
OTHER
RETRIEVAL
METHODS
.
151
10.1.1
LINEAR
SEARCH
.
151
10.1.2
INDEXING
TECHNIQUES
FOR
CASE
RETRIEVAL
.
152
10.1.3
RELATIONAL
RETRIEVAL
.
152
10.1.4
HIERARCHICAL
MEMORY
STRUCTURES
.
152
10.1.5
FCD-TREES
.
153
10.1.6
F
ISH
&
S
HRINK
.
153
10.1.7
C
RASH
.
155
10.2
TEXTUAL
CBR
AND
INFORMATION
EXTRACTION
.
156
10.2.1
FAQF
INDER
.
157
10.2.2
S
PIRE
.
157
10.2.3
AUTOMATIC
INDEX
ASSIGNMENT
.
158
10.2.4
INFORMATION
EXTRACTION
FOR
DOCUMENT
ANALYSIS
.
158
10.2.5
INFORMATION
EXTRACTION
FOR
KNOWLEDGE
ACQUISITION
.
159
10.3
KNOWLEDGE
MANAGEMENT
.
159
10.3.1
ORGANIZATIONAL
MEMORIES
.
159
10.3.2
THE
USE
OF
ONTOLOGIES
.
160
10.3.3
TEXTUAL
KNOWLEDGE
MANAGEMENT
.
160
10.4
COGNITIVE
PSYCHOLOGY
.
160
10.4.1
PARALLEL
DISTRIBUTED
PROCESSING
.
160
10.4.2
MARKER
PASSING
ALGORITHMS
.
161
10.4.3
SPREADING
ACTIVATION
THEORIES:
A
CT
*
.
162
10.4.4
KNOWLEDGE-DIRECTED
SPREADING
ACTIVATION
.
163
10.4.5
R
OBIN
/
R
EMIND
.
163
10.4.6
ANALOG
RETRIEVAL
.
164
10.5
MISCELLANEOUS
.
165
10.5.1
CONSYDERR
.
165
10.5.2
VAGUE
INFORMATION
IN
DATABASES
.
166
10.5.3
DECISION-THEORETIC
APPROACHES
.
167
11
SUMMARY
AND
OUTLOOK
171
11.1
CONCLUSIONS
.
171
11.1.1
ADVANTAGES
.
172
11.1.2
LIMITATIONS
.
172
11.1.3
APPLICATIONS
.
173
11.2
OUTLOOK
.
174
11.2.1
EXTENSIONS
OF
THE
THEORETICAL
FRAMEWORK
.
174
11.2.2
EXTENSIONS
FOR
PRACTICAL
APPLICATIONS
.
175
CONTENTS
XV
V
APPENDICES
177
REFERENCES
179
INDEX
191
A
EVALUATION
DATA
195
A.L
EVALUATION
QUERIES
.
195
A.2
PRECISION-RECALL
TABLES
FOR
THE
DIFFERENT
METHODS
OF
NORMALIZATION
.
197
A.3
PRECISION-RECALL
TABLES
FOR
THE
ABLATION
STUDY
.
198
A.4
EVALUATION
QUERIES
.
198 |
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| author | Lenz, Mario |
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| indexdate | 2024-08-16T01:12:45Z |
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| language | English |
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| oclc_num | 48478498 |
| open_access_boolean | |
| owner | DE-91G DE-BY-TUM DE-29T DE-20 DE-11 |
| owner_facet | DE-91G DE-BY-TUM DE-29T DE-20 DE-11 |
| physical | XV, 200 S. Ill., graph. Darst. |
| publishDate | 2000 |
| publishDateSearch | 2000 |
| publishDateSort | 2000 |
| publisher | Akad. Verl.-Ges. Aka |
| record_format | marc |
| series | Dissertationen zur künstlichen Intelligenz |
| series2 | Dissertationen zur künstlichen Intelligenz |
| spelling | Lenz, Mario Verfasser aut Case retrieval nets as a model for building flexible information systems Mario Lenz Berlin Akad. Verl.-Ges. Aka 2000 XV, 200 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Dissertationen zur künstlichen Intelligenz 236 Zugl.: Berlin, Humboldt-Univ., Diss., 1999 Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Informationssystem (DE-588)4072806-7 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Informationssystem (DE-588)4072806-7 s Künstliche Intelligenz (DE-588)4033447-8 s DE-604 Dissertationen zur künstlichen Intelligenz 236 (DE-604)BV005345280 236 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009000573&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Lenz, Mario Case retrieval nets as a model for building flexible information systems Dissertationen zur künstlichen Intelligenz Künstliche Intelligenz (DE-588)4033447-8 gnd Informationssystem (DE-588)4072806-7 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4072806-7 (DE-588)4113937-9 |
| title | Case retrieval nets as a model for building flexible information systems |
| title_auth | Case retrieval nets as a model for building flexible information systems |
| title_exact_search | Case retrieval nets as a model for building flexible information systems |
| title_full | Case retrieval nets as a model for building flexible information systems Mario Lenz |
| title_fullStr | Case retrieval nets as a model for building flexible information systems Mario Lenz |
| title_full_unstemmed | Case retrieval nets as a model for building flexible information systems Mario Lenz |
| title_short | Case retrieval nets as a model for building flexible information systems |
| title_sort | case retrieval nets as a model for building flexible information systems |
| topic | Künstliche Intelligenz (DE-588)4033447-8 gnd Informationssystem (DE-588)4072806-7 gnd |
| topic_facet | Künstliche Intelligenz Informationssystem Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009000573&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV005345280 |
| work_keys_str_mv | AT lenzmario caseretrievalnetsasamodelforbuildingflexibleinformationsystems |