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Copyright ©2005 ontoprise GmbH, Karlsruhe
Know how to use Know-how
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RDF, Ontologies and Meta-Data Workshop, Edinburgh, 7.6.06Jürgen Angele, ontoprise
Jayant Sharma, oracle
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Technology:
- Technology Leader (Gartner Group, Forrester
Research)
-Vision: SemanticWeb
Founded: 1999 (Spin Off Univ. Karlsruhe)
Team: 40 Employees
Context: “Semantic Karlsruhe” (~ 80 R&D) - ontoprise, Karlsruhe
- AIFB Karlsruhe- FZI, Karlsruhe
Products: - OntoStudio® - SemanticMiner®- OntoBroker® - SemanticGuide®
- SemanticIntegrator®
ontoprise GmbH
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ontoprise
„We are looking for knowledge, that we lost by information.“
Thomas Stearns Eliot (1888-1965), amerik.-engl. Dichter
innovative products and services
„Optimize the use of know-how in your organization“
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OntoStudio / OntoBroker
Menu
OntoStudio
OntoBroker
OWL Compiler
Inference Server
F-Logic Compiler
RDF Compiler
OXMLCompiler
Inference Kernel
Built-Ins• Textual analyses• Mathematical Operators• String Operators • Other Operators
Internal Database (EDB)
Web Service
Connectors
DIG
F-Logic OWL
Rewriter
SPARQLCompiler
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OntoStudio / OntoBroker / SemanticMiner
Menu
OntoStudioOntoBroker
SemanticMiner
OWL Compiler
Inference Server
F-Logic Compiler
RDF Compiler
OXMLCompiler
Inference Kernel
Built-Ins• Textual analyses• Mathematical Operators• String Operators • Other Operators
Internal Database (EDB)
Web Service
Connectors
DIG
F-Logic OWL
Rewriter
SPARQLCompiler
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Menu
AccessIntegration
OntoBroker
<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>
Application Architecture
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OntoStudio
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OntoStudio
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OntoStudio
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OntoStudio
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OntoStudio
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OntoStudio
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OntoStudio
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OntoStudio
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OntoStudio
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OntoStudio
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OntoStudio
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OntoStudio
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OntoBroker Architecture
OWL Compiler
Inference Server
F-Logic Compiler
RDF Compiler
OXMLCompiler
Inference Kernel
Built-Ins• Textual analyses• Mathematical Operators• String Operators • Other Operators
Internal Database (EDB)
Web Service
Connectors
DIG
F-Logic OWL
Rewriter
SPARQLCompiler
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High Performance Inferencing
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1 2 3 4 5 6 7 8 9 10 11 12 13 14
test case
tim
es
OB 3.6 übersetzt
XSB
Win Prolog
SWI Prolog
OB 4.x compiled
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High Performance Inferencing 2
scalability
Best of Breed
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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)
Audi: Semantic Testcar Configuration
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Ontology combines rules, structures and information
Ontology
Mapping of existing information
StructuresDependencies, rules
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Ontologies represent the meaning of information
Sample Ontology (Source ontoprise)
I
I
I
I
I
340 kW
Part_ID
340 kW
has_Power
designed_for_powerPart_ID
has_Part
has_Part
Has_Part
has_Part
CAR
Engine, Motor
Chassis,Under-carriage
Electronics
Body FW 4x4-587
M V8-340
SE 32-566
controls
Part_ID
“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.
Represent the meaning of information
-Concepts and hierarchies (Car, has_Part, Engine, Body, …)
-Synonyms (Engine, Motor)
-Attributes and relations (Part_ID, designed_for_power, controls)
-other
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Ontologies represent the logic of information
Sample Ontology (Source ontoprise)
I
I
I
I
I
340 kW
Part_ID
340 kW
has_Power
designed_for_powerPart_ID
has_Part
has_Part
Has_Part
has_Part
CAR
Engine, Motor
Chassis,Under-carriage
Electronics
Body FW 4x4-587
M V8-340
SE 32-566
controls
Part_ID
“Ontologies are the backbone of semantic technologies. They enable companies to integrate information, make them tangible and re-usable.” Prof. Dr. Rudi Studer.
Represent the logic of information
-Rules to define constraints (Chassis has to be designed for the power of the engine)
-Rules for defining any functional, logical, geometrical, chronological dependencies (has_Power influences gearbox and tires)
-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”)
-Rules to define different contexts
has_Power(Engine) < designed_for_power(Chassis)
Otherwise error
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Relationships/Constraints
Example Rule: The maximum power of the motor must not exceed the one of the brakes: Pmotor < Pbrakes
Menu
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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
Customer Service Support for Kuka Roboter
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Service DeliveryR&D
! !?
!!? !???
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
Know-how has to be transfered
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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
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
It is generally estimated that for each $1 spent for an application, companies spend on average $5 to $9 for the integration. © IBM, Nelson Mattos
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Software AG’sEnterprise 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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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
Chemistry: OntoBroker passes Advanced Placement Test
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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
questionHSO4
- + H2O <=> H3O+ + SO42-
In the equilibrium represented above, the species that acts as bases include which of the following?
I. HSO4-
II. H2O
III. SO42-
a) II only b) III only c) I and II d) I and III e) II and III
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Pharma / Lifescience
TargetIdentification
Biology Chemistry Production Trials
Screening
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The cholesterol biosynthesis
pathway (simplified version)
Dolichol
Acetyl CoA
HMG -CoA
Mevalonate
Mevalonatepyrophosphate
Isopenterylpyrophosphate
Geranyl pyrophosphate
Farnesyl pyrophosphate
Cholesterol
Dimethylallylpyrophosphate
IsopentenylTransfer RNA
Squalene
Ubiquinone
HMG-CoA reductase Simvastatin
LovastatinPravastatin
rate-determining enzyme for the entire pathway
Desmosterol
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Pathway ontology
reaction
gene
pathway
tissuecompound
enzyme
organism
proteindisease
part ofcatalysed by
acts as
codes for
haspart of
found in
takes place in
hassubstrate
has product activates inhibits
plays a role in
hasisoform
is type of
group of
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Pathway ontology: cholesterol biosynthesis
3 -hydroxy-3-methylglutaryl-CoA + 2 NADPH + 2 H+ => (R)-mevalonate + CoA + 2 NADP+
HMGCR gene
cholesterol biosynthesis pathway
liver
Lovastatin
HMG-CoA reductase1.1.1.34
Homo sapiens
HMDG_HUMAN P04035
coronary heart diseaseCHD
part of
catalysed by
acts as
codes for
haspart of
found in
takes place in
hassubstrate
has product
inhibits
plays a role in
MevalonateHMG-CoA
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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
THEN r can take place in o
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
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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
OWL Compiler
Inference Server
F-Logic Compiler
RDF Compiler
OXMLCompiler
Built-Ins• Textual analyses• Mathematical Operators• String Operators • Other Operators
Web Service
Connectors
DIG
F-Logic OWL
Rewriter
SPARQLCompiler
Internal Database (EDB)
Inference Kernel
RDF Database (Oracle)
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Oracle
Ontobroker Architecture: RDF
OWL Compiler
Inference Server
F-Logic Compiler
RDF Compiler
OXMLCompiler
Built-Ins• Textual analyses• Mathematical Operators• String Operators • Other Operators
Web Service
Connectors
DIG
F-Logic OWL
Rewriter
SPARQLCompiler
Internal Database (EDB)
Inference Kernel
RDF Database (Oracle)
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ORACLE Application Server
Ontobroker: Semantic SOA Bundle
BPEL Process
RDF-DB SQL-DBMS File-
Systems
OntoBroker/OWL(Ontology Server and Rule Engine)
RDF-DB RDF-DB
Intranet
RDF LayerCommon
Data Layer
Logic Framework Semantic Data HubOWL/WRL Sem Metadata RepRules …
BusinessProcess Layer
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Thank you!
Prof. Dr. Jürgen Angele, [email protected] Sharma, [email protected]