Www.ontoprise.de © 2006 ontoprise GmbH Home - 1 - | Menu | Partner | End Copyright ©2005 ontoprise GmbH, Karlsruhe Know how to use Know-how

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© 2006 ontoprise GmbH Home - 1 - | Menu | Partner | End

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



Web Service

Connectors

DIG

F-Logic OWL

Rewriter

SPARQLCompiler

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Menu

AccessIntegration

OntoBroker

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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



Web Service

Connectors

DIG

F-Logic OWL

Rewriter

SPARQLCompiler

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High Performance Inferencing

0

50000

100000

150000

200000

250000

300000

350000

400000

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


Web Service

Connectors

DIG

F-Logic OWL

Rewriter

SPARQLCompiler


Inference Kernel

RDF Database (Oracle)

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Oracle

Ontobroker Architecture: RDF

OWL Compiler

Inference Server

F-Logic Compiler

RDF Compiler

OXMLCompiler


Web Service

Connectors

DIG

F-Logic OWL

Rewriter

SPARQLCompiler


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]

Documents

Www.ontoprise.de © 2006 ontoprise GmbH Home - 1 - | Menu | Partner | End Copyright ©2005 ontoprise GmbH, Karlsruhe Know how to use Know-how