Know how
to use Know-how
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RDF, Ontologies and Meta-Data Workshop, Edinburgh, 7.6.06
Jürgen Angele, ontoprise
Jayant Sharma, oracle
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©2005 ontoprise GmbH, Karlsruhe
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ontoprise GmbH
Founded:
1999 (Spin Off Univ. Karlsruhe)
Team:
40 Employees
Context:
“Semantic Karlsruhe” (~ 80 R&D)
- ontoprise, Karlsruhe
- AIFB Karlsruhe
- FZI, Karlsruhe
Products:
- OntoStudio®
- OntoBroker®
- SemanticMiner®
- SemanticGuide®
- SemanticIntegrator®
Technology:
- Technology Leader
(Gartner Group, Forrester Research)
-Vision: SemanticWeb
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ontoprise
 innovative products and services
„Optimize the use of know-how in your organization“
„We are looking for knowledge,
that we lost by information.“
Thomas Stearns Eliot (1888-1965), amerik.-engl. Dichter
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OntoStudio / OntoBroker
OntoStudio
OntoBroker
Web Service
Inference Server
DIG
F-Logic
OWL
F-Logic
Compiler
Rewriter
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OWL
Compiler
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RDF
Compiler
Textual analyses
Mathematical Operators
String Operators
Other Operators
Inference Kernel
OXML
Compiler
SPARQL
Compiler
Internal Database (EDB)
Connectors
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OntoStudio / OntoBroker / SemanticMiner
OntoStudio
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DIG
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Compiler
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OWL
Compiler
•
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RDF
Compiler
Textual analyses
Mathematical Operators
String Operators
Other Operators
Inference Kernel
OXML
Compiler
SPARQL
Compiler
Connectors
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SemanticMiner
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Application Architecture
OntoBroker
Access
Integration
<article>
<articleid>a-5634</articleid>
<category>printer</category>
<name>hp81</name>
<price currency=‘USD’>500</price>
<producer>hp</producer>
<resolution>1960 dpi</resolution>
<type>laser</type>
….
</article>
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OntoStudio
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OntoStudio
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OntoStudio
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OntoBroker Architecture
Web Service
Inference Server
DIG
F-Logic
F-Logic
Compiler
OWL
Rewriter
Built-Ins
OWL
Compiler
•
•
•
•
RDF
Compiler
OXML
Compiler
Textual analyses
Mathematical Operators
String Operators
Other Operators
Inference Kernel
SPARQL
Compiler
Connectors
Internal Database (EDB)
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High Performance Inferencing
400000
350000
times
300000
4.x übersetzt
compiled
OB 3.6
250000
200000
XSB
Win Prolog
150000
SWI Prolog
100000
50000
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14
test case
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High Performance Inferencing 2
scalability
Best of Breed
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Audi: Semantic Testcar Configuration
Background
• Complex dependencies decrease the
speed of development
• Knowledge is distributed over
different departments
Goal
• Design of a Semantic Guide for
• capturing the dependencies
• Configuration of components
• Integration into existing order system
• Engineers can concentrate on creative
efforts
• Integration of different data sources
(RDBs)
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Ontology combines rules, structures and information
Structures
Dependencies, rules
Ontology
Mapping of existing information
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Ontologies represent the meaning of information
340 kW
Represent the meaning of
information
M V8-340
has_Power
Part_ID
Engine, Motor
I
-Concepts and hierarchies
(Car, has_Part, Engine, Body, …)
controls
-Synonyms (Engine, Motor)
Has_Part
Electronics
I
-Attributes and relations
(Part_ID, designed_for_power,
controls)
340 kW
has_Part
CAR
I
Part_ID
designed_for_power
has_Part
I
-other
Chassis,
Under-carriage
has_Part
SE 32-566
Part_ID
I
Body
FW 4x4-587
Sample Ontology (Source ontoprise)
“An ontology is a hierarchically structured set of terms for
describing a domain that can be used as a skeletal
foundation for a knowledge base.” Swartout, Patil, Knight
and Russ.
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Ontologies represent the logic of information
340 kW
Represent the logic of
information
M V8-340
has_Power
Part_ID
Engine, Motor
I
has_Power(Engine) <
designed_for_power(Chassis)
-Rules to define constraints
(Chassis has to be designed for the
power of the engine)
Otherwise error
controls
Has_Part
Electronics
I
-Rules for defining any functional,
logical, geometrical, chronological
dependencies (has_Power
influences gearbox and tires)
340 kW
has_Part
CAR
I
Part_ID
designed_for_power
has_Part
I
Chassis,
Under-carriage
-Rules for information integration
(value “Engine has_power” is
stored in “PDM p, Table t1”; value
“designed_for_power” is stored in
“CAT c, Table t2”)
has_Part
SE 32-566
Part_ID
I
Body
FW 4x4-587
Sample Ontology (Source ontoprise)
-Rules to define different contexts
“Ontologies are the backbone of semantic technologies.
They enable companies to integrate information, make
them tangible and re-usable.” Prof. Dr. Rudi Studer.
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Relationships/Constraints
Example Rule: The maximum power of the motor must not exceed the one of
the brakes: Pmotor < Pbrakes
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Customer Service Support for Kuka Roboter
Background
• 65% of all customer in the
manufacturing industry change their
suppliers because there are not
satisfied with the service
• Service engineers spend a lot of
time with known problems
Goal
• Capturing and usage of engineers
and experts know-how
• Decision support for choosing the
right solution
• Increase customer satisfaction
Implementation
• Semantic Customer Service Support
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Know-how has to be transfered
!!
R&D
?
? !?
??
!
!
Delivery
Service
Goals
• Capture and Use (!) Know-how of skilled engineers
• Deliver decision support for service job on customer site
• Optimize feedback from Service to R&D
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System asks questions and gives answers
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Why KUKA commits to the SemanticGuide
Value add
Reducing costs:
• Qualification of task by hotline using SemanticGuide
• Optimizing search time for information access
• Reducing ‚Trial And Error‘
• Optimizing ‚First Time Fix‘ as well as ‚Time To Fix‘
• Reducing costs for parts ‚Spare Part Overtake‘
• Fewer travels of service personal
Improving quality:
•
•
•
•
Guided analysis for difficult problems
Proactive suggestions for maintenance
Faster replication of knowledge by feed back
Distributing knowledge on 300 service engineers
Motivation of service engineers:
• Easy Handling, seamless integration into CS module of SAP
• Fall back strategy if available knowledge is not sufficient
• Information is available very fast for all engineers
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Semantic Information Integration
It is generally estimated that for each $1 spent for an
application, companies spend on average $5 to $9
© IBM, Nelson Mattos
for the integration.
What is the problem of information integration?
• structural heterogeneity – different application systems
store their data in different structures
• semantic heterogeneity – intended meaning of
information items is different in the various application systems
• inconsistency and redundancy problems – data in
different application systems might be partially inconsistent or
redundant
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Software AG’s
Enterprise Information Integrator v.2.2
source: Software AG
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EII Value Propositions

Single View
 Aggregating data from multiple systems
 Presenting relevant information in the user’s terminology
 Giving different perspectives into the same information

Business Agility
 Minimizing impact of change
 Ease of maintenance
 Rapid implementation of new strategies

Increased Productivity
 Capturing business rules directly in the Information Model
 Determining the optimal system access
 Bringing every user to the same level of effectiveness and
productivity
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Chemistry: OntoBroker passes Advanced Placement Test
Background
• Development of a Digital Aristoteles
• Phase 1 successfully closed in 2003
• Phase 2 since January 2004
Functions
• Capturing of extensive set of chemical
knowledge
• System passed the „Advanced
Placement Test“
• Query is answered and answer is
explained
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OntoBroker® passed the Advanced Placement Test!
 Correct Answers
 Correct Explanations
Performance




CYCORP
1650 Minutes (>27 hrs.)
Student
240 Minutes
Stanford Research 38 Minutes
Ontoprise
9 Minutes
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Syllbus question no. 10
question
HSO4- + H2O <=> H3O+ + SO42In the equilibrium represented above, the species that acts as
bases include which of the following?
I.
HSO4II. H2O
III. SO42a)
b)
c)
d)
e)
II only
III only
I and II
I and III
II and III
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Pharma / Lifescience
Screening
Target
Identification
Biology
Chemistry
Production
Trials
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Acetyl CoA
HMG -CoA
Simvastatin
HMG-CoA reductase
rate-determining
enzyme for the entire
pathway
Mevalonate
Lovastatin
Pravastatin
Mevalonate
pyrophosphate
Isopenteryl
pyrophosphate
Geranyl
pyrophosphate
Farnesyl
pyrophosphate
Dimethylallyl
pyrophosphate
Isopentenyl
Transfer RNA
The cholesterol biosynthesis
Squalene
pathway (simplified version)
Desmosterol
Ubiquinone
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Cholesterol
Dolichol
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Pathway ontology
pathway
group of
part of
plays a role in
catalysed by
enzyme
reaction
acts as
takes place in
has product
inhibits
activates
disease
has
isoform
protein
has
substrate
is type of
compound
codes for
tissue
gene
part of
has
found in
organism
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Pathway ontology: cholesterol biosynthesis
cholesterol biosynthesis pathway
part of
3 -hydroxy-3-methylglutaryl-CoA + 2 NADPH + 2 H+ =>
(R)-mevalonate + CoA + 2 NADP+
plays a role in
has
substrate
coronary heart disease
CHD
has product
HMG-CoA
catalysed by
HMG-CoA reductase
1.1.1.34
Mevalonate
inhibits
acts as
takes place in
HMDG_HUMAN P04035
Lovastatin
codes for
liver
HMGCR gene
part of
found in
© 2006 ontoprise GmbH
has
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Homo
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Rules
IF r is a reaction
AND e is an enzyme
AND r is catalysed by e
AND o is an organism
AND o has gene g
AND g codes for protein p
AND p acts as e
IF p is a protein
AND r is a reaction
AND r catalysed by e
AND p acts as e
AND d is a disease
AND r plays a role in d
THEN p is target for d
THEN r can take place in o
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Research projects
 SmartWeb
 Leitinnovationsprojekt (top priority projects
for German government)
„Mobile access to the Semantic Web“
demonstrated with a scenario for the world
soccer championship 2006
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Figures
 SmartWeb






around 60 MB RDF(S) data
2500 classes
927 relations
177713 instances
417059 relation instances
81 rules
 Halo
 500 rules
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Figures
 Uniprot
(600 MB out of 25 GB)
 747834 instances
 2690647 relation instances
 MeSH
 Gene
 200000 classes
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Requirements for an RDF store







persistency
high performance
transactions, backup, multiuser, access rights,..
fast import / export
query language
tightly integrated with reasoning
tightly integrated with modeling environment
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Ontobroker Architecture: RDF
Web Service
Inference Server
DIG
F-Logic
OWL
F-Logic
Compiler
Rewriter
OWL
Compiler
Built-Ins
•
•
•
•
RDF
Compiler
Textual analyses
Mathematical Operators
String Operators
Other Operators
Inference Kernel
OXML
Compiler
SPARQL
Compiler
Connectors
RDF Database (Oracle)
Internal Database (EDB)
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Ontobroker Architecture: RDF
Web Service
Inference Server
DIG
F-Logic
F-Logic
Compiler
OWL
Rewriter
Built-Ins
OWL
Compiler
•
•
•
•
RDF
Compiler
Oracle
OXML
Compiler
Textual analyses
Mathematical Operators
String Operators
Other Operators
Inference Kernel
SPARQL
Compiler
RDF Database (Oracle)
Internal Database (EDB)
Connectors
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Ontobroker: Semantic SOA Bundle
Business
Process Layer
ORACLE Application Server
BPEL Process
OntoBroker/OWL
(Ontology Server
and Rule Engine)
Logic Framework
OWL/WRL
Rules
Semantic Data Hub
Sem Metadata Rep
…
Intranet
RDF Layer
RDF-DB
RDF-DB
SQLDBMS
RDF-DB
Common
Data Layer
FileSystems
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Thank you!
Prof. Dr. Jürgen Angele, angele@ontoprise.de
Jayant Sharma, jayant.sharma@oracle.com
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