Prof. Dr. Wolfgang Ziegler - I4ICM:...

Prof. Dr. Wolfgang Ziegler

Hochschule Karlsruhe, Kommunikation und Medienmanagement

Institute for Information and Content Management I4ICM

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)

Various Aspects of Intelligent Systems

Intelligence helps !

o to systemize

o to automize

o to understand?

©Prof. Dr. Ziegler

▪ 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

©Prof. Dr. Ziegler

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.

©Prof. Dr. Ziegler

▪ 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

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

▪ 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)

©Prof. Dr. Ziegler

▪ 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

▪ 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

Physical Objects

(Product Components)

Content

Content

Content

Content Objects

(Modular Topics)

Operation

Dismount

Repair

Information

Classes

▪ 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

©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

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

EN Translation


Taxonomy

of

(intrinsic)

Information

Classes

pi-

fan

.de

EN Translation


Taxonomy

of

(extrinsic)

Product

Classes

pi-

fan

.de

©Prof. Dr. Ziegler

Rotor

Display

Heating

X3B

T3B

Content

Topics

Multidimensional

Information

Space

Safety

Repair

Functional Description

User Manual

Service Manual

Rotor

Display

Heating

X3B

T3B

Content

Topic

Safety

Repair


User Manual

Service Manual

Intrinsic

Taxonomies

Intrinsic

Taxonomies

Extrinsic

Hierarchies Extrinsic

Hierarchies

Hierarchies, Taxonomies, List, …

Variant properties Functional Metadata

Multidimensional

Information

Space

Content

Topic

Tools

Variant properties

Functional Metadata (Collections)

Time Spare

Parts

Error

Codes

Maint

Intervals…

Mutidimensional

Information

Space

Content

Topic

Variant

Properties/

Features Functional Metadata

(Collections)Geo-

metryParts

No

Mate-

rialFeatures

Multidimensional

Information

Space

Location

▪ 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

©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

©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

CMS

CDP

©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

©Prof. Dr. Ziegler

Schema

Content Delivery Server

[www.pi-fan.de]

©Prof. Dr. Ziegler

Empolis

Content Express

[www.pi-fan.de]

©Prof. Dr. Ziegler

Practice innovation

IDS c-rex.net

[www.pi-fan.de]

▪ Taxonomies and

hierarchies are

two-dimensional

descriptions

of a dree-

dimensional

world

©Prof. Dr. Ziegler

Rotor

Display

Heating

X3B

T3B

Content

Topic

Safety

Repair


User Manual

Service Manual

Intrinsic

Taxonomies

Intrinsic

Taxonomies

Extrinsic


Hierarchies


Variant

Features/

Properties

Functional

Metadata

Mutidimensional

Information

Space

▪ 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

©Prof. Dr. Ziegler

▪ 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

Rotor

Display

Heating

X3B

T3B

Content

Topic

Safety

Repair


User Manual

Service Manual

Intrinsic

Taxonomies

Intrinsic

Taxonomies

Extrinsic


Hierarchies


Variant

Features/

Properties

Functional

Metadata

Mutidimensional

Information

Space

CMS

CDP

©Prof. Dr. Ziegler

Source:

Ontolis

CMS

CDP

©Prof. Dr. Ziegler

Source:

Coreon

Source:

Coreon

CMS

CDP

▪ 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 …

©Prof. Dr. Ziegler

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

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

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

▪ 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

J. Oevermann; fastclass.de Similarity

Duplicates

Variants

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

…

▪ 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

▪ 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

▪ Ziegler, W. (2017): „Metadaten für intelligenten Content“

In: tekom Schriftenreihe Band 22

▪ www.i4icm.de

©Prof. Dr. Ziegler

Prof. Dr. Wolfgang Ziegler - I4ICM:...

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