Semantische Technologien. Datenspeicher oder Wissensmodelle?

Semantische Technologien
Datenspeicher oder
Wissensmodelle?
Semantische Technologien in Wissenschaft, Wirtschaft und Kultur
Dr. Karsten Ehms, Siemens AG, Corporate Technology
siemens.com/corporate-technologyUnrestricted © Siemens AG 2019

Unrestricted © Siemens AG 2019
July 2019Page 2 Siemens Corporate Technology
Semantics - Magic or Mania („Gigantomanie“)
Harris 1977
Any sufficiently advanced technology is
indistinguishable from magic.
Arthur C. Clarke‘s 3rd law (1973)

“Plan”
• Einführung – Hintergrund
• Knowledge Graphs als Semantsiche Technologien
• Beispiele für Knowledge Graphs bei der Siemens AG
• Entscheidungs- und Ausbreitungsstrategien – Analogie Web 2.0
• Wrap up – Q&A
► Intro
Background
► Focus
Knowledge
Graphs
► Examples
Cases
► Adoption and
Decision
► Wrap Up
Q&A

2005 Peak of LWO Activities (2000-2005)

Corporate user centric social platforms
2011 20162006 2008 2013
Weblogs (internal, posts, news, jams)
Wikis (global, internal, pages, topic portals)
Messaging Networks (internal)
Technology Communities / Portals (internal, urgent requests)
* Tiled entry page
Weblogs (external)
ø 2,5 tags
per post

Siemens Tagging Framwork (2011)
Thesaurus
EDITOR
(browser based)

From Tags to Semantics (2008)
Content: Theseus Alexandria – Collective Ontology Development

How far do you want to „jump“?
(Meta) Data-Structures with different levels of expressive power (expressiveness, expressivity) and precision
keywords/
tags
glossaries
thesauri
ontologies
(limited)
ontologies
(first-order logic)
ontologies
(frames)
classifications
taxonomies
folksonomies
controlled
keywords

Knowledge Representation 1.0
Quelle: Microsoft
Quelle:commons.wikimedia.org

Focus on Knowledge Graphs
► Intro
Background
► Focus
Knowledge
Graphs
► Examples
Cases
► Adoption and
Decision
► Wrap Up
Q&A

Why (Knowledge) Graphs?
Benefits for data representation
• targeted at representing entities and
their relations
• therefore potentially easier to
understand
• structures can emerge without
“schema migration” (flexibility)
• integration of multiple linkable data
sources, schemata, types via URLs
(esp. in RDF)
• formal semantic representation
facilitates inference and machine
processing
Plant
Part
Report
OEM
Part
Location

Continuous Learning
Knowledge graphs are a company's living memory which need to be
cared for: From manual knowledge mgmt. to automation
Structured Data Semi-Structured Data Unstructured DataData Sources
Knowledge
Consumers
Knowledge
Storage (Graph)
Knowledge Model
& continuous mgmt.
Domain Ontology
Knowledge
Extraction
AutomatedMachine LearningSchema/NLP
Manual AutomatedSemi-Automated
Applications (standard/customized)
Bots
Graph
Access Search Query Explorer Visualizer Discovery
Graph
Analytics
Manual
Legend
Today:
Future improve-
ment potential Automated
Manual
effort

Knowledge graphs & NLP technologies increasingly attractive for
venture capital invests – first VC also by industrial players
Source: Quid®
Clusters
● graph / relevant /
publishers / matching
75%
●
natural language /
text analytics /
computational
linguistics / language
processing
24%

Benefits of digital technology across customer’s value chain
Maintenance &
services
Runtime &
operation
Design &
engineering
Improved productivity &
time-to-market
Higher flexibility &
resilience
Increased availability &
efficiency
Combining the virtual & physical world …
… across entire customer value chains
Data
analytics
Cloud & platform
technology
Cyber-
Security
Secure
connectivity
Artificial
Intelligence
Simulation
tools

Learning Memories as Vision
for representing Domain Knowledge
Degree of automated knowledge digitalization à
1
Isolated Data Silos
with hand-crafted
expert systems
2
Domain-specific
Knowledge Graphs
generated from DBs
3
Connected Knowledge
Graph via automated
structure and
link discovery
4
Learning Memories
extract expert
knowledge from
observations
Industrial Knowledge Graph
Knowledge
Digitalized Knowledge (via reasoning and learning)Collected data
From isolated data silos to learning memories

Human decision making depends on semantic knowledge for
perception, reasoning, and decision making
Knowledge
Graph
AI Algorithm
Working Memory
(integrate – understand)
Decision Making
(act)
Episodic Memory
(remember)
Perception
(see)
Semantic Memory
(know)
Declarative

Three levels of “digital knowledge” and related technologies
Relevant technological research fields
Decision Making
• Reasoning and Constraint Solving
• Machine/Deep Learning
• Question Answering
Storage and Integration
• Graph/NoSQL databases
• Constraints and Rules
• Probabilistic programming
• Ontologies
Generation
• NLP/Text understanding
• Machine/Deep Learning
• Computer vision
• Sound recognition
• Virtual data Integration
• Information retrieval
• …
Decision Making
Storage and Integration
Generation
Knowledge Graph & Memory
Knowledge
Automation
Observations à Multi-structured Data
Humans Machines

Industrial Knowledge Graph – Siemens Examples
• Knowledge Graphs as focus topic in semantic technologies
• about 30 use cases across Siemens
• more than 10 products under development
► Intro
Background
► Focus
Knowledge
Graphs
► Examples
Cases
► Adoption and
Decision
► Wrap Up
Q&A
A1

Use cases for knowledge graphs can be clustered into
five categories – overview and use case examples
Data quality Digital companion
Improving data availability and
quality by combining and
comparing data from various
sources to fill in missing data sets
or identify potentially wrong data
and data duplicates
Enhancing features of existing
products or services with digital
companions that are able to
understand and process user
questions and providing the
needed data insights
Data access &
dashboarding
Maintaining up-to-date meta-data,
creating transparency on all
available data and making them
accessible to users via queries
Recommender
system
Providing users high quality
recommendations by identifying
similarities in historical data
Constraints &
planning
Enabling autonomous systems
to understand data and its
dependencies and take own
decisions, such as autonomous
planning of production proces-ses
Use case examples:
• BOM quality
• Digital Twin / Plant Twin
• Reference Projects
• mindsphere
Use case examples:
• Manage my Machine Maintenance
• Question Answering Companion
for COMOS
• Smart Service Companion
Use case examples:
• OpereX
• Building Twin
• Smart Data Web
• mindsphere
Use case examples
• AI @ Selection Tool
• Advanced Diagnostic System
• Generative Design of Produc-
tion Process
Use case examples:
• Dynamic Production Process
Mgmt.
• Autonomous System Revolution –
ASR
Degree of complexity
A2
A4

Examples for hands on knowledge graphs representation

User Adoption – Learning from Social Web / Collaboration
► Intro
Background
► Focus
Knowledge
Graphs
► Examples
Cases
► Adoption and
Decision
► Wrap Up
Q&A

Technology adoption patterns – in general and 2.0
Source: wikimedia.org -Craig Chelius Oliver Widder (2009)

Good Information Systems – very simple
Addressable
• robust links between systems (usually URIs/URLs )
• granular
Retrievable
• search mechanisms
• history
Obeservable
• activities, social signals
• transparency, permissions
hUI
pfUI
German only,
sorry!

Core Decisions on Information Spaces
see: “good information systems”
Social Object (DNA)
Internal Strucure
Metadata Structure(s)
Transparency >< Security Individuality >< Usability

Closed by Default statt Open – Linkes OPEN Data
Integrationcosts
Software complexity / “Permission” System
Costs !
or: Culture / Trust Error ?

Open as a challenge
Government Digital
Services Cabinet Office
on Jan 26, 2011
http://www.slideshare.net/C
olemanE

Decision oriented Perspective
Richard Feynman (1918-1988)
(at the challenger space shuttle inquiry)
What I cannot create, I do not understand!

Wrap Up – „Zusammenfassung“
• Handwerk der Datenstruktur-Modellierung mitunter “sperrig” für die
Fachanwender im Fokus
• Entwicklung von interaktiven “graphischen” Werkzeugen hängt Jahrzehnte
hinterher (vgl. Web 2.0) <- Anreizstrukturen in der Kern- selbst Angewandten
Informatik (Interdisziplinarität an Dt. Hochschulen)
• Hype um die Skalierung / Skalierbarkeit trifft nur teilweise auf semantische
Technologien zu (“Semantik, als das was der Mensch versteht”)
• Durchbrüche werden in der Statistik/Algorithmik erzielt, die präzise
intellektuelle Modellierung ist so anspruchsvoll wie eh und je
• Dennoch gibt es Fortschritte in Teilbereichen
► Intro
Background
► Focus
Knowledge
Graphs
► Decision
2nd Order
► User Adoption
(1st order, 2.0)
► Examples
Cases
► Wrap Up
Q&A

“Ad hoc Foresighting“
variable / enabler today - x + x years
• computing power ↑↑ ↑↑
• connectivity ↑ ↑
• data accessible ↑↑ ?
• user interfaces ↕ ?
• intellectual capacity ? ??

Further Reading – Q&A
• https://www.sigs-datacom.de/ots/2018/ki/1-anwendungsszenarien-fuer-wissensnetze-bei-
siemens.html
• http://ceur-ws.org/Vol-2180/paper-86.pdf
• Recent article on graph networks
https://www.zdnet.com/google-amp/article/google-ponders-the-shortcomings-of-machine-learning/
• Nickel, Murphy, Tresp, Gabrilovich. A Review of Relational Machine Learning for Knowledge Graphs:
From Multi-Relational Link Prediction to Automated Knowledge Graph Construction. Proceedings of
the IEEE, (invited paper), 2016.
• Baier, Ma, Tresp. Improving Visual Relationship Detection using Semantic Modeling of Scene
Descriptions, ISWC 2017
• Mehdi, Kharlamov, Savkovic, Xiao, Kalayci, Brandt, Horrocks, Roshchin,
Runkler. Semantic Rule-Based Equipment Diagnostic, ISWC 2017
• Volker Tresp, Cristóbal Esteban, Yinchong Yang, Stephan Baier, and Denis Krompaß. Learning with
Memory Embeddings. NIPS 2015 Workshop on Nonparametric Methods for Large Scale
Representation Learning(extended TR), 2015
• Nickel, M., Murphy, K., Tresp, V., & Gabrilovich, E. (2015). A review of relational machine learning
for knowledge graphs. Proceedings of the IEEE, 104(1), 11-33.
► Intro
Background
► Focus
Knowledge
Graphs
► Decision
2nd Order
► User Adoption
(1st order, 2.0)
► Examples
Cases
► Wrap Up
Q&A
Ergänzungen aus der Q&A Session:
http://videolectures.net/eswc2016_hendler_wither_OWL/
http://videolectures.net/iswc2017_taylor_applied_semantics/

Vielen Dank für Ihre Aufmerksamkeit!
Dr. Karsten Ehms
Senior Key Expert
Member of Research Group Semantics & Reasoning
Siemens AG
Corporate Technology / CT RDA BAM SMR-DE
Otto-Hahn-Ring 6
81739 München Germany
karsten.ehms@siemens.com
https://www.siemens.com/global/de/home/unternehmen/innovation
en/corporate-technology.html

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