The knowledge graph cookbook: recipes that work
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Vienna
edition mono/monochrom
2020
|
| Edition: | 1st edition |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | 256 Seiten Illustrationen 24 cm x 16 cm |
| ISBN: | 9783902796707 3902796707 |
Staff View
MARC
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| 100 | 1 | |a Blumauer, Andreas |e Verfasser |0 (DE-588)133257053 |4 aut | |
| 245 | 1 | 0 | |a The knowledge graph cookbook |b recipes that work |c Andreas Blumauer and Helmut Nagy |
| 250 | |a 1st edition | ||
| 264 | 1 | |a Vienna |b edition mono/monochrom |c 2020 | |
| 300 | |a 256 Seiten |b Illustrationen |c 24 cm x 16 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
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| 653 | |a Semantic Web | ||
| 653 | |a Semantic AI | ||
| 653 | |a Knowledge management | ||
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| 689 | 0 | 1 | |a Wissensmanagement |0 (DE-588)4561842-2 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Nagy, Helmut |e Verfasser |0 (DE-588)1219222429 |4 aut | |
| 710 | 2 | |a edition mono/monochrom |0 (DE-588)1065136749 |4 pbl | |
| 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=032310147&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-032310147 | ||
Record in the Search Index
| _version_ | 1804181772100960256 |
|---|---|
| adam_text | TABLE
OF
CONTENTS
WHY
THIS
BOOK?
-
EXECUTIVE
SUMMARY
13
WHY
WE
WROTE
THIS
BOOK-ABOUT
THE
AUTHORS
19
ANDREAS
21
HELMUT
24
PART
1:
INTRODUCTION
TO
KNOWLEDGE
GRAPHS
WHY
KNOWLEDGE
GRAPHS?
27
29
A
BRIEF
HISTORY
OF
KNOWLEDGE
GRAPHS
32
FAST
FORWARD
32
SEMANTIC
WEB
33
LABELED
PROPERTY
GRAPHS
35
CORE
CONCEPTS
36
METADATA:
BE
FAIR
37
CONTEXT
OVERCOMES
AMBIGUITY
39
DATA
FABRIC
INSTEAD
OF
DATA
SILO
41
KNOWLEDGE
ORGANIZATION:
MAKE
SEMANTICS
EXPLICIT
43
KNOWLEDGE
MANAGEMENT
-
BETTER
WITH
KNOWLEDGE
GRAPHS
45
KNOWLEDGE
GRAPHS
ARE
NOT
JUST
FOR
VISUALIZATION
48
THINGS,
NOT
STRINGS
50
MACHINE
LEARNING
AND
ARTIFICIAL
INTELLIGENCE:
MAKE
IT
EXPLAINABLE
52
APPLICATION
SCENARIOS
55
ORCHESTRATING
KNOWLEDGE
WORKFLOWS
IN
COLLABORATIVE
ENVIRONMENTS
56
UNIFY
UNSTRUCTURED
AND
STRUCTURED
DATA
IN
A
SEMANTIC
DATA
CATALOG
59
CONNECTING
THE
DOTS:
SEARCH
AND
ANALYTICS
WITH
KNOWLEDGE
GRAPHS
61
SEMANTIC
SEARCH
62
DRUG
DISCOVERY
64
FRAUD
DETECTION
65
DIGITAL
TWINS
AND
WEB
OF
THINGS
65
5
DEEP
TEXT
ANALYTICS
(DTA)
66
CONTRACT
INTELLIGENCE
68
AUTOMATED
UNDERSTANDING
OF
TECHNICAL
DOCUMENTATION
69
INTELLIGENT
ROBOTIC
PROCESS
AUTOMATION
70
EXCELLENT
CUSTOMER
EXPERIENCE
71
CUSTOMER
360
71
RECOMMENDER
SYSTEMS
72
CONVERSATIONAL
AL
74
SEARCH
ENGINE
OPTIMIZATION
(SEO)
74
PART
2:
SETTING
THE
STAGE
INTRODUCING
KNOWLEDGE
GRAPHS
INTO
ORGANIZATIONS
77
79
WHEN
DO
YOU
KNOW
THAT
YOU
NEED
A
KNOWLEDGE
GRAPH?
80
ASSESSING
THE
SEMANTIC
MATURITY
LEVEL
OF
AN
ORGANIZATION
81
ORGANIZATIONAL
ASPECTS
81
TECHNICAL
ASPECTS
82
EMBEDDING
KNOWLEDGE
GRAPH
BUILDING
IN
A
CHANGE
MANAGEMENT
PROCESSES
83
KNOWLEDGE
GRAPH
GOVERNANCE
86
PERSONAS:
TOO
MANY
COOKS?
88
CHIEF
INFORMATION
OFFICER
(CIO)
89
CHIEF
DATA
OFFICER
(CDO)
/
DATA
&
ANALYTICS
LEADERS
90
AL
ARCHITECT
91
DATA/LNFORMATION
ARCHITECT
93
DATA
ENGINEER
94
ML
ENGINEER
(MLOPS)
95
KNOWLEDGE
ENGINEER
/
METADATA
SPECIALIST
95
SUBJECT
MATTER
EXPERT
(SME,
DOMAIN
EXPERT)
95
DATA
SCIENTIST
/
DATA
ANALYST
96
BUSINESS
USER
/
CUSTOMER
/
CITIZEN
96
SETTING
UP
AN
ENTERPRISE
KNOWLEDGE
GRAPH
PROJECT
97
CIRCUMVENT
KNOWLEDGE
ACQUISITION
BOTTLENECKS
100
HOW
TO
MEASURE
THE
ECONOMIC
IMPACT
OF
AN
ENTERPRISE
KNOWLEDGE
GRAPH
101
6
PART
3:
MAKE
KNOWLEDGE
GRAPHS
WORK
105
THE
ANATOMY
OF
A
KNOWLEDGE
GRAPH
107
BASIC
PRINCIPLES
OF
SEMANTIC
KNOWLEDGE
MODELING
110
BASIC
INGREDIENTS
OF
KNOWLEDGE
GRAPHS
112
URIS
AND
TRIPLES
112
RDF
TRIPLES
AND
SERIALIZATION
114
KNOWLEDGE
ORGANIZATION
SYSTEMS
114
TAXONOMIES
AND
THESAURI
115
ONTOLOGIES
119
REUSING
EXISTING
KNOWLEDGE
MODELS
AND
GRAPHS
122
WORLD
KNOWLEDGE
GRAPHS
122
DOMAIN
KNOWLEDGE
GRAPHS
123
BUSINESS
AND
FINANCE
124
PHARMA
AND
MEDICINE
126
CULTURAL
HERITAGE
127
SUSTAINABLE
DEVELOPMENT
129
GEOGRAPHIC
INFORMATION
129
METHODOLOGIES
131
CARD
SORTING
131
TAXONOMY
MANAGEMENT
133
TAXONOMY
GOVERNANCE
133
PROCESS
MODEL
134
ONTOLOGY
MANAGEMENT
136
RDFIZATION:
TRANSFORMING
STRUCTURED
DATA
INTO
RDF
138
TEXT
MINING:
TRANSFORMING
UNSTRUCTURED
DATA
INTO
RDF
140
ENTITY
EXTRACTION
140
TEXT
CLASSIFICATION
142
FACT
EXTRACTION
143
ENTITY
LINKING
AND
DATA
FUSION
143
QUERYING
KNOWLEDGE
GRAPHS
145
VALIDATING
DATA
BASED
ON
CONSTRAINTS
146
REASONING
OVER
GRAPHS
148
7
HOW
TO
MEASURE
THE
QUALITY
OF
AN
ENTERPRISE
KNOWLEDGE
GRAPH
150
KNOWLEDGE
GRAPH
LIFE
CYCLE
152
EXPERT
LOOP
153
AUTOMATION
LOOP
154
USER
LOOP
154
GOOD
PRACTICES
BASED
ON
REAL-WORLD
USE
CASES
156
START
SMALL
AND
GROW
156
GET
TO
KNOW
YOUR
DATA
157
NOT
INVENTED
HERE!
IS
NOT
A
GOOD
PRACTICE
158
URI
PATTERNS:
PUT
YOUR
KNOWLEDGE
GRAPH
ON
A
SOLID
FOUNDATION
159
PART
4:
SYSTEM
ARCHITECTURE
AND
TECHNOLOGIES
163
ELEMENTS
OF
AN
ENTERPRISE
KNOWLEDGE
GRAPH
ARCHITECTURE
165
INTEGRATION
SCENARIOS
IN
AN
ENTERPRISE
SYSTEMS
ARCHITECTURE
167
SINGLE
SOURCE
INTEGRATION
167
MULTI-SOURCE
INTEGRATION
168
FULL
INTEGRATION
WITH
AN
ESA
169
KNOWLEDGE
GRAPH
AS
A
SERVICE
170
KNOWLEDGE
GRAPH
INGESTION
SERVICES
171
KNOWLEDGE
GRAPH
ENRICHMENT
SERVICES
171
KNOWLEDGE
GRAPH
CONSUMPTION
SERVICES
172
KNOWLEDGE
GRAPH
ORCHESTRATION
SERVICES
173
A
SEMANTIC
DATA
CATALOG
ARCHITECTURE
175
GRAPH
DATABASES
177
PART
5:
EXPERT
S
OPINIONS
181
INTERVIEWS
183
JANS
AASMAN
183
AARON
BRADLEY
186
YANKO
IVANOV
192
8
BRYON
JACOB
196
ATANAS
KIRYAKOV
199
MARK
KITSON
203
LUTZ
KRUEGER
207
JOE
PAIRMAN
209
IAN
PIPER
213
BORIS
SHALUMOV
219
MICHAEL
J.
SULLIVAN
221
PART
6:
THE
FUTURE
OF
KNOWLEDGE
GRAPHS
225
AL
AND
KNOWLEDGE
TECHNOLOGIES
IN
A
POST-CORONA
SOCIETY
227
SELF-SERVICING
BASED
ON
EXPLAINABLE
AL
228
FIGHT
FAKE
NEWS
AND
HATE
SPEECH
229
HR
AT
THE
HEART
OF
LEARNING
ORGANIZATIONS
229
REBIRTH
OF
LINKED
OPEN
(GOVERNMENT)
DATA
231
THE
BEGINNING
OF
A
NEW
AL
ERA
232
NEW
ROLES:
THE
RISE
OF
THE
KNOWLEDGE
SCIENTIST
234
UPCOMING
NEW
GRAPH
STANDARDS
238
ADDENDUM:
FAQS
AND
GLOSSARY
241
FAQS
243
WHY
DO
YOU
THINK
I
SHOULD
BE
INTERESTED
IN
KNOWLEDGE
GRAPHS?
243
HOW
CAN
I
MEASURE
THE
BUSINESS
VALUE
OF
KNOWLEDGE
GRAPHS?
243
ARE
KNOWLEDGE
GRAPHS
CREATED
PRIMARILY
FOR
DATA
VISUALIZATION
AND
ANALYTICS?
244
DO
I
HAVE
TO
CREATE
A
KNOWLEDGE
GRAPH
BY
HAND
OR
CAN
THIS
BE
AUTOMATED?
244
WHERE
CAN
I
DOWNLOAD
OR
PURCHASE
KNOWLEDGE
GRAPHS?
245
WHO
IN
OUR
ORGANIZATION
WILL
BE
WORKING
ON
KNOWLEDGE
GRAPHS?
245
HOW
ARE
KNOWLEDGE
GRAPHS
RELATED
TO
ARTIFICIAL
INTELLIGENCE?
246
WHICH
TOOLS
DO
I
NEED
TO
CREATE
AND
RUN
A
KNOWLEDGE
GRAPH?
246
9
WHAT S
THE
DIFFERENCE
BETWEEN
A
TAXONOMY
AND
AN
ONTOLOGY?
247
WHAT S
THE
DIFFERENCE
BETWEEN
THE
SEMANTIC
WEB,
LINKED
DATA
AND
KNOWLEDGE
GRAPHS?
247
ARE
GRAPH
DATABASES
THE
SAME
AS
KNOWLEDGE
GRAPHS?
247
GLOSSARY
248
AUTOML
248
BUSINESS
GLOSSARY
248
ENTERPRISE
KNOWLEDGE
GRAPH
(EKG)
249
HUMAN-IN-THE-LOOP
(HITL)
249
INFERENCE
AND
REASONING
250
INFORMATION
RETRIEVAL
(IR)
250
KNOWLEDGE
DOMAIN
250
KNOW
YOUR
CUSTOMER
(KYC)
251
NAMED
GRAPHS
251
NATURAL
LANGUAGE
PROCESSING
(NLP)
251
OPEN-WORLD
ASSUMPTION
(OWA)
252
PRECISION
AND
RECALL
(F1
SCORE)
252
SEMANTIC
AL
253
SEMANTIC
FOOTPRINT
253
SEMANTIC
LAYER
254
IO
|
| adam_txt |
TABLE
OF
CONTENTS
WHY
THIS
BOOK?
-
EXECUTIVE
SUMMARY
13
WHY
WE
WROTE
THIS
BOOK-ABOUT
THE
AUTHORS
19
ANDREAS
21
HELMUT
24
PART
1:
INTRODUCTION
TO
KNOWLEDGE
GRAPHS
WHY
KNOWLEDGE
GRAPHS?
27
29
A
BRIEF
HISTORY
OF
KNOWLEDGE
GRAPHS
32
FAST
FORWARD
32
SEMANTIC
WEB
33
LABELED
PROPERTY
GRAPHS
35
CORE
CONCEPTS
36
METADATA:
BE
FAIR
37
CONTEXT
OVERCOMES
AMBIGUITY
39
DATA
FABRIC
INSTEAD
OF
DATA
SILO
41
KNOWLEDGE
ORGANIZATION:
MAKE
SEMANTICS
EXPLICIT
43
KNOWLEDGE
MANAGEMENT
-
BETTER
WITH
KNOWLEDGE
GRAPHS
45
KNOWLEDGE
GRAPHS
ARE
NOT
JUST
FOR
VISUALIZATION
48
THINGS,
NOT
STRINGS
50
MACHINE
LEARNING
AND
ARTIFICIAL
INTELLIGENCE:
MAKE
IT
EXPLAINABLE
52
APPLICATION
SCENARIOS
55
ORCHESTRATING
KNOWLEDGE
WORKFLOWS
IN
COLLABORATIVE
ENVIRONMENTS
56
UNIFY
UNSTRUCTURED
AND
STRUCTURED
DATA
IN
A
SEMANTIC
DATA
CATALOG
59
CONNECTING
THE
DOTS:
SEARCH
AND
ANALYTICS
WITH
KNOWLEDGE
GRAPHS
61
SEMANTIC
SEARCH
62
DRUG
DISCOVERY
64
FRAUD
DETECTION
65
DIGITAL
TWINS
AND
WEB
OF
THINGS
65
5
DEEP
TEXT
ANALYTICS
(DTA)
66
CONTRACT
INTELLIGENCE
68
AUTOMATED
UNDERSTANDING
OF
TECHNICAL
DOCUMENTATION
69
INTELLIGENT
ROBOTIC
PROCESS
AUTOMATION
70
EXCELLENT
CUSTOMER
EXPERIENCE
71
CUSTOMER
360
71
RECOMMENDER
SYSTEMS
72
CONVERSATIONAL
AL
74
SEARCH
ENGINE
OPTIMIZATION
(SEO)
74
PART
2:
SETTING
THE
STAGE
INTRODUCING
KNOWLEDGE
GRAPHS
INTO
ORGANIZATIONS
77
79
WHEN
DO
YOU
KNOW
THAT
YOU
NEED
A
KNOWLEDGE
GRAPH?
80
ASSESSING
THE
SEMANTIC
MATURITY
LEVEL
OF
AN
ORGANIZATION
81
ORGANIZATIONAL
ASPECTS
81
TECHNICAL
ASPECTS
82
EMBEDDING
KNOWLEDGE
GRAPH
BUILDING
IN
A
CHANGE
MANAGEMENT
PROCESSES
83
KNOWLEDGE
GRAPH
GOVERNANCE
86
PERSONAS:
TOO
MANY
COOKS?
88
CHIEF
INFORMATION
OFFICER
(CIO)
89
CHIEF
DATA
OFFICER
(CDO)
/
DATA
&
ANALYTICS
LEADERS
90
AL
ARCHITECT
91
DATA/LNFORMATION
ARCHITECT
93
DATA
ENGINEER
94
ML
ENGINEER
(MLOPS)
95
KNOWLEDGE
ENGINEER
/
METADATA
SPECIALIST
95
SUBJECT
MATTER
EXPERT
(SME,
DOMAIN
EXPERT)
95
DATA
SCIENTIST
/
DATA
ANALYST
96
BUSINESS
USER
/
CUSTOMER
/
CITIZEN
96
SETTING
UP
AN
ENTERPRISE
KNOWLEDGE
GRAPH
PROJECT
97
CIRCUMVENT
KNOWLEDGE
ACQUISITION
BOTTLENECKS
100
HOW
TO
MEASURE
THE
ECONOMIC
IMPACT
OF
AN
ENTERPRISE
KNOWLEDGE
GRAPH
101
6
PART
3:
MAKE
KNOWLEDGE
GRAPHS
WORK
105
THE
ANATOMY
OF
A
KNOWLEDGE
GRAPH
107
BASIC
PRINCIPLES
OF
SEMANTIC
KNOWLEDGE
MODELING
110
BASIC
INGREDIENTS
OF
KNOWLEDGE
GRAPHS
112
URIS
AND
TRIPLES
112
RDF
TRIPLES
AND
SERIALIZATION
114
KNOWLEDGE
ORGANIZATION
SYSTEMS
114
TAXONOMIES
AND
THESAURI
115
ONTOLOGIES
119
REUSING
EXISTING
KNOWLEDGE
MODELS
AND
GRAPHS
122
WORLD
KNOWLEDGE
GRAPHS
122
DOMAIN
KNOWLEDGE
GRAPHS
123
BUSINESS
AND
FINANCE
124
PHARMA
AND
MEDICINE
126
CULTURAL
HERITAGE
127
SUSTAINABLE
DEVELOPMENT
129
GEOGRAPHIC
INFORMATION
129
METHODOLOGIES
131
CARD
SORTING
131
TAXONOMY
MANAGEMENT
133
TAXONOMY
GOVERNANCE
133
PROCESS
MODEL
134
ONTOLOGY
MANAGEMENT
136
RDFIZATION:
TRANSFORMING
STRUCTURED
DATA
INTO
RDF
138
TEXT
MINING:
TRANSFORMING
UNSTRUCTURED
DATA
INTO
RDF
140
ENTITY
EXTRACTION
140
TEXT
CLASSIFICATION
142
FACT
EXTRACTION
143
ENTITY
LINKING
AND
DATA
FUSION
143
QUERYING
KNOWLEDGE
GRAPHS
145
VALIDATING
DATA
BASED
ON
CONSTRAINTS
146
REASONING
OVER
GRAPHS
148
7
HOW
TO
MEASURE
THE
QUALITY
OF
AN
ENTERPRISE
KNOWLEDGE
GRAPH
150
KNOWLEDGE
GRAPH
LIFE
CYCLE
152
EXPERT
LOOP
153
AUTOMATION
LOOP
154
USER
LOOP
154
GOOD
PRACTICES
BASED
ON
REAL-WORLD
USE
CASES
156
START
SMALL
AND
GROW
156
GET
TO
KNOW
YOUR
DATA
157
"NOT
INVENTED
HERE!"
IS
NOT
A
GOOD
PRACTICE
158
URI
PATTERNS:
PUT
YOUR
KNOWLEDGE
GRAPH
ON
A
SOLID
FOUNDATION
159
PART
4:
SYSTEM
ARCHITECTURE
AND
TECHNOLOGIES
163
ELEMENTS
OF
AN
ENTERPRISE
KNOWLEDGE
GRAPH
ARCHITECTURE
165
INTEGRATION
SCENARIOS
IN
AN
ENTERPRISE
SYSTEMS
ARCHITECTURE
167
SINGLE
SOURCE
INTEGRATION
167
MULTI-SOURCE
INTEGRATION
168
FULL
INTEGRATION
WITH
AN
ESA
169
KNOWLEDGE
GRAPH
AS
A
SERVICE
170
KNOWLEDGE
GRAPH
INGESTION
SERVICES
171
KNOWLEDGE
GRAPH
ENRICHMENT
SERVICES
171
KNOWLEDGE
GRAPH
CONSUMPTION
SERVICES
172
KNOWLEDGE
GRAPH
ORCHESTRATION
SERVICES
173
A
SEMANTIC
DATA
CATALOG
ARCHITECTURE
175
GRAPH
DATABASES
177
PART
5:
EXPERT
'
S
OPINIONS
181
INTERVIEWS
183
JANS
AASMAN
183
AARON
BRADLEY
186
YANKO
IVANOV
192
8
BRYON
JACOB
196
ATANAS
KIRYAKOV
199
MARK
KITSON
203
LUTZ
KRUEGER
207
JOE
PAIRMAN
209
IAN
PIPER
213
BORIS
SHALUMOV
219
MICHAEL
J.
SULLIVAN
221
PART
6:
THE
FUTURE
OF
KNOWLEDGE
GRAPHS
225
AL
AND
KNOWLEDGE
TECHNOLOGIES
IN
A
POST-CORONA
SOCIETY
227
SELF-SERVICING
BASED
ON
EXPLAINABLE
AL
228
FIGHT
FAKE
NEWS
AND
HATE
SPEECH
229
HR
AT
THE
HEART
OF
LEARNING
ORGANIZATIONS
229
REBIRTH
OF
LINKED
OPEN
(GOVERNMENT)
DATA
231
THE
BEGINNING
OF
A
NEW
AL
ERA
232
NEW
ROLES:
THE
RISE
OF
THE
KNOWLEDGE
SCIENTIST
234
UPCOMING
NEW
GRAPH
STANDARDS
238
ADDENDUM:
FAQS
AND
GLOSSARY
241
FAQS
243
WHY
DO
YOU
THINK
I
SHOULD
BE
INTERESTED
IN
KNOWLEDGE
GRAPHS?
243
HOW
CAN
I
MEASURE
THE
BUSINESS
VALUE
OF
KNOWLEDGE
GRAPHS?
243
ARE
KNOWLEDGE
GRAPHS
CREATED
PRIMARILY
FOR
DATA
VISUALIZATION
AND
ANALYTICS?
244
DO
I
HAVE
TO
CREATE
A
KNOWLEDGE
GRAPH
BY
HAND
OR
CAN
THIS
BE
AUTOMATED?
244
WHERE
CAN
I
DOWNLOAD
OR
PURCHASE
KNOWLEDGE
GRAPHS?
245
WHO
IN
OUR
ORGANIZATION
WILL
BE
WORKING
ON
KNOWLEDGE
GRAPHS?
245
HOW
ARE
KNOWLEDGE
GRAPHS
RELATED
TO
ARTIFICIAL
INTELLIGENCE?
246
WHICH
TOOLS
DO
I
NEED
TO
CREATE
AND
RUN
A
KNOWLEDGE
GRAPH?
246
9
WHAT'S
THE
DIFFERENCE
BETWEEN
A
TAXONOMY
AND
AN
ONTOLOGY?
247
WHAT'S
THE
DIFFERENCE
BETWEEN
THE
SEMANTIC
WEB,
LINKED
DATA
AND
KNOWLEDGE
GRAPHS?
247
ARE
GRAPH
DATABASES
THE
SAME
AS
KNOWLEDGE
GRAPHS?
247
GLOSSARY
248
AUTOML
248
BUSINESS
GLOSSARY
248
ENTERPRISE
KNOWLEDGE
GRAPH
(EKG)
249
HUMAN-IN-THE-LOOP
(HITL)
249
INFERENCE
AND
REASONING
250
INFORMATION
RETRIEVAL
(IR)
250
KNOWLEDGE
DOMAIN
250
KNOW
YOUR
CUSTOMER
(KYC)
251
NAMED
GRAPHS
251
NATURAL
LANGUAGE
PROCESSING
(NLP)
251
OPEN-WORLD
ASSUMPTION
(OWA)
252
PRECISION
AND
RECALL
(F1
SCORE)
252
SEMANTIC
AL
253
SEMANTIC
FOOTPRINT
253
SEMANTIC
LAYER
254
IO |
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| any_adam_object_boolean | 1 |
| author | Blumauer, Andreas Nagy, Helmut |
| author_GND | (DE-588)133257053 (DE-588)1219222429 |
| author_facet | Blumauer, Andreas Nagy, Helmut |
| author_role | aut aut |
| author_sort | Blumauer, Andreas |
| author_variant | a b ab h n hn |
| building | Verbundindex |
| bvnumber | BV046900484 |
| classification_rvk | ST 515 |
| classification_tum | DAT 616 |
| ctrlnum | (OCoLC)1220878729 (DE-599)DNB1205021906 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| edition | 1st edition |
| format | Book |
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| id | DE-604.BV046900484 |
| illustrated | Illustrated |
| index_date | 2024-07-03T15:24:17Z |
| indexdate | 2024-07-10T08:56:58Z |
| institution | BVB |
| institution_GND | (DE-588)1065136749 |
| isbn | 9783902796707 3902796707 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032310147 |
| oclc_num | 1220878729 |
| open_access_boolean | |
| owner | DE-12 DE-91G DE-BY-TUM |
| owner_facet | DE-12 DE-91G DE-BY-TUM |
| physical | 256 Seiten Illustrationen 24 cm x 16 cm |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | edition mono/monochrom |
| record_format | marc |
| spelling | Blumauer, Andreas Verfasser (DE-588)133257053 aut The knowledge graph cookbook recipes that work Andreas Blumauer and Helmut Nagy 1st edition Vienna edition mono/monochrom 2020 256 Seiten Illustrationen 24 cm x 16 cm txt rdacontent n rdamedia nc rdacarrier Wissensmanagement (DE-588)4561842-2 gnd rswk-swf Semantic Web (DE-588)4688372-1 gnd rswk-swf Semantic Web Semantic AI Knowledge management Semantic Web (DE-588)4688372-1 s Wissensmanagement (DE-588)4561842-2 s DE-604 Nagy, Helmut Verfasser (DE-588)1219222429 aut edition mono/monochrom (DE-588)1065136749 pbl DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032310147&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Blumauer, Andreas Nagy, Helmut The knowledge graph cookbook recipes that work Wissensmanagement (DE-588)4561842-2 gnd Semantic Web (DE-588)4688372-1 gnd |
| subject_GND | (DE-588)4561842-2 (DE-588)4688372-1 |
| title | The knowledge graph cookbook recipes that work |
| title_auth | The knowledge graph cookbook recipes that work |
| title_exact_search | The knowledge graph cookbook recipes that work |
| title_exact_search_txtP | The knowledge graph cookbook recipes that work |
| title_full | The knowledge graph cookbook recipes that work Andreas Blumauer and Helmut Nagy |
| title_fullStr | The knowledge graph cookbook recipes that work Andreas Blumauer and Helmut Nagy |
| title_full_unstemmed | The knowledge graph cookbook recipes that work Andreas Blumauer and Helmut Nagy |
| title_short | The knowledge graph cookbook |
| title_sort | the knowledge graph cookbook recipes that work |
| title_sub | recipes that work |
| topic | Wissensmanagement (DE-588)4561842-2 gnd Semantic Web (DE-588)4688372-1 gnd |
| topic_facet | Wissensmanagement Semantic Web |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032310147&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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