Prof. Dr. Wolfgang Ziegler - I4ICM:...
Transcript of Prof. Dr. Wolfgang Ziegler - I4ICM:...
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Prof. Dr. Wolfgang Ziegler
Hochschule Karlsruhe, Kommunikation und Medienmanagement
Institute for Information and Content Management I4ICM
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o 1987-1997 Theoretical Physics (Ph.D.), Würzburg Univ., Germany
o 1997 Tech. Doc Services (CMS Consultant, Developer XML/XSLT)
o 2001 Central Documentation Services (LIEBHERR)
o 2003 Professor of Information & Content Management
(Karlsruhe Univ. Appl. Sciences)
(Meta-)Data, Information & Process Modelling for CMS,
XML Processing & Publishing, CMS Evaluation & Introduction, Content
Automation & Delivery
o Independent CMS & CDP consultant
I4ICM Institute for Information and Content Management (I4ICM)
(Methodologies: REx, PI-Class®, PI-Mod, Content-Delivery, CoReAn)
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Various Aspects of Intelligent Systems
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Intelligence helps !
o to systemize
o to automize
o to understand?
©Prof. Dr. Ziegler
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▪ CMS principle: Controlled reuse of content modules (topics) in multiple documents or media by the use of metadata
▪ CMS offer solutions foro variant management
o version management
o translation management
o cross media publishing management
▪ CDP principle:Dynamic content delivery for electronic (web) media supported by metadata driven search functionalities
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Systems offering web based access to modular, aggregated content or other information for various user groups by related retrieval mechanisms.
Basic functionalities
▪ Access or import content from relevant data sources and corresponding systems
▪ Manage and update content within the content lifecycle
▪ Retrieval functionalities including user interfaces for content searching by users.
▪ Web-based display of content on a modular or document based level
▪ Web services handling requests from other applications and events.
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▪ Mobile devices and corresponding apps used in private spheres, and that
are consequently being demanded also in business use
▪ Search driven information access from retrieval applications which includes
facetted and filtered searching for consumer goods
▪ Virtual reality and augmented reality enriching visual environments by
additional information
▪ Machine translation and its growing acceptance in private and business
contexts
▪ Artificial intelligence technologies which allow the automated extraction of
decision-relevant information and knowledge from various data sources
▪ Big data technologies for analyzing mass data generated for example from
customer behavior in sales processes or from the tracking of machine states
▪ IoT (Internet of Things) and Industry 4.0 initiatives which aim to control
and react to the real-time behavior of products.
©Prof. Dr. Ziegler
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CMS, … CDP
xMS
xMS
xMS On Site
CDP
CMSCMS Supplier
Additional
Information
&
Sources
User Information
Service Information
Machine State
Off Site
Portal/MobileRequirement for
Standardized Exchange Format
Delivery
Machine state
(errors, messages,
operating conditions)
Intelligent Product
I4.0/IoT-ready
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▪ Intelligent Content
Methodologies, technologies, systems which allow to
automize (or at least systemize) content processes.
Processes cover content aggregation, content delivery,
(retrieval, search, request) and quality assurance
▪ Content Intelligence
Methodologies, technologies which allow to track and
check content and content related processes.
Processes cover content creation and metadata use,
content reuse in CMS and content use in CDP
(overlap with artificial intelligence)
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▪ Native Intelligence
Semantic content and semantic metadata for process
automization
▪ Augmented Intelligence
Additional relations between content objects described
e.g. by ontologies
▪ Artificial Intelligence
Automated extraction of metadata and knowledge by
statistical methods
©Prof. Dr. Ziegler
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▪ Purpose of native intelligence is to automize processes
within CMS or to access information stored in CDP
▪ Consists of
o Semantic information modeling
• Standards: DITA, DocBook, S1000D, … PI-Mod
• Custom Structures: CMS-dependant
• Technologies: XML, (DTP)
o Semantic metadata
• Standards: Dublin Core, S1000D, iSPEC2200, RDS-PP, eCLass, iiRDS
• Technologies: RDF, Turtle, Notation 3, JSON, …
• Methodologies: PI-Classification, …
©Prof. Dr. Ziegler
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Physical Objects
(Product Components)
Content
Content
Content
Content Objects
(Modular Topics)
Operation
Dismount
Repair
Information
Classes
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▪ The basic dimensions of (semantic) meta data for
modular
content topics
©Prof. Dr. Ziegler
Content
Product-Class
Component
Product
Information-Class
Information
Type
Information
Product
Extended Metadata
(Variant Features/Properties & Functional)
©P
rof. D
r. Z
ieg
ler
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©Prof. Dr. Ziegler
Product-Class
Base/
Telescopic Rod
X3B, X3-H1,
X5-B, X5-D,…
Information-Class
Operation/
Height
Adjustment
User Manual,
Service Manual,…
pi-
fan
.de
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EN Translation
provided by RWS Group, Germany
Taxonomy
of
(intrinsic)
Product
Component
Classes
Analogous procedure
of component-based
decomposition and
classification of software
products:
• software components
• software classes/objects
• GUI components
• programming units
pi-
fan
.de
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EN Translation
provided by RWS Group, Germany
Taxonomy
of
(intrinsic)
Information
Classes
pi-
fan
.de
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EN Translation
provided by RWS Group, Germany
Taxonomy
of
(extrinsic)
Product
Classes
pi-
fan
.de
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©Prof. Dr. Ziegler
Rotor
Display
Heating
X3B
T3B
Content
Topics
Multidimensional
Information
Space
Safety
Repair
Functional Description
User Manual
Service Manual
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Rotor
Display
Heating
X3B
T3B
Content
Topic
Safety
Repair
Functional Description
User Manual
Service Manual
Intrinsic
Taxonomies
Intrinsic
Taxonomies
Extrinsic
Hierarchies Extrinsic
Hierarchies
Hierarchies, Taxonomies, List, …
Variant properties Functional Metadata
Multidimensional
Information
Space
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Content
Topic
Tools
Variant properties
Functional Metadata (Collections)
Time Spare
Parts
Error
Codes
Maint
Intervals…
Mutidimensional
Information
Space
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Content
Topic
Variant
Properties/
Features Functional Metadata
(Collections)Geo-
metryParts
No
Mate-
rialFeatures
Multidimensional
Information
Space
Location
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▪ Product features included or in addition to (extrinsic)
product classes / names
▪ Example PI-Fan: Type TP-DH2 (Combination type)
o Table Fan (T)
o Continuous Switch (P)
o Display (D)
o Heating (H)
o 2-Level Heating (2)
▪ Goal: Facilitate planning of new variants/configurations
and metadata handling in CMS
©Prof. Dr. Ziegler
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©Prof. Dr. Ziegler
Einstellen der Gebläsestärke
Die Stärke des Gebläses kann in fünf Stufen eingestellt werden.
Die Stärke des Gebläses kann stufenlos eingestellt werden.
Die Stärke des Gebläses kann in sieben Stufen eingestellt werden.
• Drehen Sie den Drehregler (Abb. 23) bis die gewünschte Stärke
erreicht ist.
Die Stärke des Gebläses kann ebenfalls an der Fernsteuerung eingestellt
werden (s. Abschnitt 5.4 Fernsteuerung).
TAB, TB5,
T445,TX5B,…TPB,TAMP, ….
T7B,TFX,..
Extrinsic Classification as Variant Property
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©Prof. Dr. Ziegler
Einstellen der Gebläsestärke
Die Stärke des Gebläses kann in fünf Stufen eingestellt werden.
Die Stärke des Gebläses kann stufenlos eingestellt werden.
Die Stärke des Gebläses kann in sieben Stufen eingestellt werden.
• Drehen Sie den Drehregler (Abb. 23) bis die gewünschte Stärke
erreicht ist.
Die Stärke des Gebläses kann ebenfalls an der Fernsteuerung eingestellt
werden (s. Abschnitt 5.4 Fernsteuerung).
Stufe=5 Stufe=Cont
Stufe=7
Features as Variant Property
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CMS
CDP
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©Prof. Dr. Ziegler[www.pi-fan.de]
Structured Search
Direct Search
Facets Navigation
Cleaning the rotor
Mounting the rotorProcedures
X-Series
All Components
Docufy
Topic Pilot
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©Prof. Dr. Ziegler
Schema
Content Delivery Server
[www.pi-fan.de]
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©Prof. Dr. Ziegler
Schema
Content Delivery Server
[www.pi-fan.de]
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©Prof. Dr. Ziegler
Empolis
Content Express
[www.pi-fan.de]
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©Prof. Dr. Ziegler
Practice innovation
IDS c-rex.net
[www.pi-fan.de]
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▪ Taxonomies and
hierarchies are
two-dimensional
descriptions
of a dree-
dimensional
world
©Prof. Dr. Ziegler
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Rotor
Display
Heating
X3B
T3B
Content
Topic
Safety
Repair
Functional Description
User Manual
Service Manual
Intrinsic
Taxonomies
Intrinsic
Taxonomies
Extrinsic
Hierarchies Extrinsic
Hierarchies
Hierarchies, Taxonomies, List, …
Variant
Features/
Properties
Functional
Metadata
Mutidimensional
Information
Space
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▪ Multi occurences of product components at different
locations (in taxonomy)
▪ Relations between product components; Depencies of
topics on combinations of components
▪ Dependencies of additional variant properties on
product components
▪ Dependencies of information types on other taxonomic
values
©Prof. Dr. Ziegler
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©Prof. Dr. Ziegler
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▪ Purpose of Augmented Intelligence is to model the
complexity of real world products and information
▪ Overcome typical shortcomings of the
taxonomic modelling of metadata
▪ Introduce model of objects, their properties and
(conditional) relations between each other
©Prof. Dr. Ziegler
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Rotor
Display
Heating
X3B
T3B
Content
Topic
Safety
Repair
Functional Description
User Manual
Service Manual
Intrinsic
Taxonomies
Intrinsic
Taxonomies
Extrinsic
Hierarchies Extrinsic
Hierarchies
Hierarchies, Taxonomies, List, …
Variant
Features/
Properties
Functional
Metadata
Mutidimensional
Information
Space
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CMS
CDP
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CMS
CDP
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CMS
CDP
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©Prof. Dr. Ziegler
Source:
Ontolis
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©Prof. Dr. Ziegler
Source:
Ontolis
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CMS
CDP
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©Prof. Dr. Ziegler
Source:
Coreon
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Source:
Coreon
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©Prof. Dr. Ziegler
Source:
Coreon
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CMS
CDP
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©Prof. Dr. Ziegler
Source:
Intelligent views /
K-infinity
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CMS
CDP
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©Prof. Dr. Ziegler
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▪ Purpose of AI is the extraction of knowledge from(large/big) data and content sources
Assigning content automatically to a given ontologyor taxonomy
▪ Scenarios (in TC and within context of CMS/CDP)o Migration of legacy data (e.g., between CMS)
o Structured access to unstructured content
o Quality Control (classification, duplicates)
o …
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Model
Featu
re E
xtra
ctio
n
Algorithm
(Weighting)
Training data
(pre-classified topics)
Test data/topics
(unclassified)
Prediction/
Classification
Training
Classification
Quelle: Jan Oevermann (2016) „Intelligente Klassifizierung von technischen Inhalten –
Automatisierung und Anwendungspotenziale “.
In: Tagungsband zur tekom Jahrestagung 2016, tcworld : Stuttgart
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Quelle: Jan Oevermann (2016): „Reconstructing Semantic Structures in Technical Documentation with Vector Space Classification“. In:
Proceedings of the Posters and Demos Track of the 12 th International Conference on Semantic Systems. CEUR Workshop Proceedings
©Prof. Dr. Ziegler
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Source: Sebastian Bader / Jan Oevermann (2017): „Semantic Annotation of Heterogeneous Data
Sources: Towards an Integrated Information Framework for Service Technicians “. (to appear)
©Prof. Dr. Ziegler
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©Prof. Dr. Ziegler
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▪ Content Control
o Controlled language checker CLC
o Terminology checking
o Similaritiy analysis (content duplicates) in CMS
▪ Reuse tracking in CMS: Report Exchange (REx) Metrics
▪ Use tracking in CDP: Content Relevance Analytics (CoReAn)
©Prof. Dr. Ziegler
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J. Oevermann; fastclass.de Similarity
Duplicates
Variants
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CMS CDP
KPI
Delivery &
Feedback
KPI Metrics:
• Reuse Rates
(Abundancy)
• Redundancy
• Document
Sharing factor
• Variant
management
• Correlations;
Distributions
…
Indirect feedback
Metrics:
• visiting time,
• Visit frequency
• search behaviour
• search terms
• …
Direct feedback
• Rating
• Satisfaction
Improve:
• Product
• Information
• Terminology
(Harvesting)
Business Intelligence
(REx)
Web Analytics
(CoReAn)
Artificial
IntelligenceQuality assurance:
• Similarity analysis
• Classification quality
…
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©Prof. Dr. Ziegler
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▪ Content is managed in CMS by Native Intelligence, and in many other data sources.
Artificial Intelligence and Content Intelligence (REx, …) can ensure content quality,
variant management and reuse efficiency in CMS
▪ Augmented Intelligence will/might drive the interaction between product and
information development
▪ Industry 4.0 / IoT concepts rely on precise identification of parts and corresponding
information delivered by CDP:
Native Intelligence of data is required to ensure retrieval precision
Artificial Intelligence can ensure data/metadata quality
▪ Manual search & retrieval processes in CDP rely on most complete network of
information
Augmented intelligence reveales network and related content
Artificial Intelligence can assign structured and unstructured content (documents
and parts of them) automatically to the information network to reduce manual effort
©Prof. Dr. Ziegler
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▪ Depends on the use case and level of digitization
▪ The Digitization Cascade The Intelligence Cascade
o Do Native Intelligence
o Let Guide Augmented Intelligence
o Let Do Artificial Intelligence
▪ Smart Examples
LifeSciences, Navigation, Consumer, …,
Documentation/Information)
©Prof. Dr. Ziegler
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▪ Ziegler, W. (2017): „Metadaten für intelligenten Content“
In: tekom Schriftenreihe Band 22
▪ www.i4icm.de
©Prof. Dr. Ziegler