© 2006 ontoprise GmbH Home | Menu | Partner | End Copyright ©2005 ontoprise GmbH, Karlsruhe Know how to use Know-how RDF, Ontologies and Meta-Data Workshop, Edinburgh, Jürgen Angele, ontoprise Jayant Sharma, oracle

© 2006 ontoprise GmbH Home | Menu | Partner | End 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

© 2006 ontoprise GmbH Home | Menu | Partner | End ontoprise We are looking for knowledge, that we lost by information. Thomas Stearns Eliot ( ), amerik.-engl. Dichter innovative products and services Optimize the use of know-how in your organization

© 2006 ontoprise GmbH Home | Menu | Partner | End OntoStudio / OntoBroker Menu OntoStudio OntoBroker OWL Compiler Inference Server F-Logic Compiler RDF Compiler OXML Compiler Inference Kernel Built-Ins Textual analyses Mathematical Operators String Operators Other Operators Internal Database (EDB) Web Service Connectors DIG F-LogicOWL Rewriter SPARQL Compiler

© 2006 ontoprise GmbH Home | Menu | Partner | End OntoStudio / OntoBroker / SemanticMiner Menu OntoStudio OntoBroker SemanticMiner OWL Compiler Inference Server F-Logic Compiler RDF Compiler OXML Compiler Inference Kernel Built-Ins Textual analyses Mathematical Operators String Operators Other Operators Internal Database (EDB) Web Service Connectors DIG F-LogicOWL Rewriter SPARQL Compiler

© 2006 ontoprise GmbH Home | Menu | Partner | End Menu Access Integration OntoBroker a-5634 printer hp hp 1960 dpi laser …. Application Architecture

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© 2006 ontoprise GmbH Home | Menu | Partner | End OntoBroker Architecture OWL Compiler Inference Server F-Logic Compiler RDF Compiler OXML Compiler Inference Kernel Built-Ins Textual analyses Mathematical Operators String Operators Other Operators Internal Database (EDB) Web Service Connectors DIG F-LogicOWL Rewriter SPARQL Compiler

© 2006 ontoprise GmbH Home | Menu | Partner | End High Performance Inferencing OB 4.x compiled

© 2006 ontoprise GmbH Home | Menu | Partner | End High Performance Inferencing 2 scalability Best of Breed

© 2006 ontoprise GmbH Home | Menu | Partner | End High Performance Inferencing KAON2

© 2006 ontoprise GmbH Home | Menu | Partner | End Information Retrieval – Difficulties in Finding Ontology Guided Search Optimize Search Results Single View over heterogeneous sources Users are swamped with creating correct queries. To many results are shown. The user ist lost. Users dont know what sources to search.

© 2006 ontoprise GmbH Home | Menu | Partner | End Company-wide Knowledge Management Project Goals Make the Companys Competences context-sensitive visible useful Increase efficiency in Operations and Consulting Result Integration of heterogeneous Sources Guided Search Why Deutsche Telekom uses Semantic Technology

© 2006 ontoprise GmbH Home | Menu | Partner | End Integration and Guided Search Integration Opentext Livelink Pirobase Intranet File-Server Multiple Databases Guided Search context-sensitive Navigation Query Expansion by synonyms and Sub-topics Typo correction and linguistic analysis

© 2006 ontoprise GmbH Home | Menu | Partner | End Guided Search at Deutsche Telekom Level of Tolerance Knowledge Browser Hierarchy Subtopics Synonyms Definition Document Hits w/ Context

© 2006 ontoprise GmbH Home | Menu | Partner | End Company-wide Knowledge Management, Phase I and II Goals Make competencies of company applicable Context sensitive access to documents, knowledge base and experts (yellow pages) Moderated search over distributed sources (e.g. Opentext, Pironet, etc. ) Why T-Systems Business Services uses Semantic Search Project numbers Users: 750 Project duration: 10 Month Project effort: 150 PD

© 2006 ontoprise GmbH Home | Menu | Partner | End Everyday Situation: Distributed Knowledge Cellphone Nokia 3110 is not compatible with adaptor N345 Torsional of the engine should be lower than of the gearbox For TMC functionality in your navigation a TMC radio is necessary

© 2006 ontoprise GmbH Home | Menu | Partner | End 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

© 2006 ontoprise GmbH Home | Menu | Partner | End Ontology combines rules, structures and information Ontology Mapping of existing information Structures Dependencies, rules

© 2006 ontoprise GmbH Home | Menu | Partner | End Ontologies represent the meaning of information Sample Ontology (Source ontoprise) I I III 340 kW Part_ID 340 kW has_Power designed_for_power Part_ID has_Part Has_Part has_Part CAR Engine, Motor Chassis, Under-carriage Electronics Body FW 4x4-587 M V8-340 SE 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

© 2006 ontoprise GmbH Home | Menu | Partner | End Ontologies represent the logic of information Sample Ontology (Source ontoprise) I I III 340 kW Part_ID 340 kW has_Power designed_for_power Part_ID has_Part Has_Part has_Part CAR Engine, Motor Chassis, Under-carriage Electronics Body FW 4x4-587 M V8-340 SE 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

© 2006 ontoprise GmbH Home | Menu | Partner | End Relationships/Constraints Example Rule: The maximum power of the motor must not exceed the one of the brakes: Pmotor < Pbrakes Menu

© 2006 ontoprise GmbH Home | Menu | Partner | End 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

© 2006 ontoprise GmbH Home | Menu | Partner | End 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

© 2006 ontoprise GmbH Home | Menu | Partner | End System asks questions and gives answers

© 2006 ontoprise GmbH Home | Menu | Partner | End 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

© 2006 ontoprise GmbH Home | Menu | Partner | End 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

© 2006 ontoprise GmbH Home | Menu | Partner | End Software AGs Enterprise Information Integrator v.2.2 source: Software AG

© 2006 ontoprise GmbH Home | Menu | Partner | End EII Value Propositions Single View Aggregating data from multiple systems Presenting relevant information in the users 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

© 2006 ontoprise GmbH Home | Menu | Partner | End 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

© 2006 ontoprise GmbH Home | Menu | Partner | End 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

© 2006 ontoprise GmbH Home | Menu | Partner | End Syllbus question no. 10question HSO H 2 O H 3 O + + SO 4 2- In the equilibrium represented above, the species that acts as bases include which of the following? I.HSO 4 - II.H 2 O III.SO 4 2- a)II only b)III only c)I and II d)I and III e)II and III

© 2006 ontoprise GmbH Home | Menu | Partner | End Syllbus question no. 10encoding r:Reaction[hasReactants->>{"HSO4","H2O"}; hasProducts->>{"H3O","SO4"}]. answer("A") <- base(r,"H2O") and not base(r,"SO4") and not base(r,"HSO4"). answer("B") <- base(r,"SO4") and not base(r,"HSO4") and not base(r,"H2O"). answer("C") <- base(r,"HSO4") and base(r,"H2O"). answer("D") <- base(r,"HSO4") and base(r,"SO4"). answer("E") <- base(r,"H2O") and base(r,"SO4"). FORALL X <- answer(X). Read A: if H2O is a base in the reaction r and SO4 and HSO4 arent bases in the reaction r, then A is the right answer.

© 2006 ontoprise GmbH Home | Menu | Partner | End FORALL A,B,V1,V2,V3 conjugateacidbase(A,B) <- chemparse(A,V1) and chemparse("H",V2) and diffvector(V1,V2,V3) and chemparse(B,V3). FORALL R,R1,R2,P1,P2 base(R,P1) and base(R,R2) and acid(R,P2) and acid(R,R1) >{R1,R2}; hasProducts->>{P1,P2}] and conjugateacidbase(R1,P1) and conjugateacidbase(P2,R2). FORALL X,Y,X1,Y1 conjugateacidbase(X,Y) <- acidbase(X,X1,Y,Y1). answer("E") <- base(r,"H2O") and base(r,"SO4"). FORALL X,Y,X1,Y1conjugateacidbase(X,Y) <- acidbase(X,X1,Y,Y1). FORALL A,B,V1,V2,V3 conjugateacidbase(A,B) <- chemparse(A,V1) and chemparse("H",V2) and diffvector(V1,V2,V3) and chemparse(B,V3). P1="SO4, R2="H2O, P2,"H3O, R1="HSO4" FORALL R,R1,R2,P1,P2 base(R,P1) and base(R,R2) and acid(R,P2) and acid(R,R1) >{R1,R2}; hasProducts->>{P1,P2}] and conjugateacidbase(R1,P1) and conjugateacidbase(P2,R2). X="HSO4, Y="SO4, X1=13.0, Y1=4.0 FORALL X,Y,X1,Y1 conjugateacidbase(X,Y) <- acidbase(X,X1,Y,Y1). X="H3O, Y=H20, X1=14.0, Y1=3.0 FORALL X,Y,X1,Y1 conjugateacidbase(X,Y) <- acidbase(X,X1,Y,Y1). A="HSO4, B="SO4, V1=..., V2=..., V3=... FORALL A,B,V1,V2,V3 conjugateacidbase(A,B) <- chemparse(A,V1) and chemparse("H",V2) and diffvector(V1,V2,V3) and chemparse(B,V3). A="H3O, B=H2O, V1=..., V2=..., V3=... FORALL A,B,V1,V2,V3 conjugateacidbase(A,B) <- chemparse(A,V1) and chemparse("H",V2) and diffvector(V1,V2,V3) and chemparse(B,V3). Syllbus question no. 10inferencing process

© 2006 ontoprise GmbH Home | Menu | Partner | End FORALL A,B,V1,V2,V3 conjugateacidbase(A,B) <- chemparse(A,V1) and chemparse("H",V2) and diffvector(V1,V2,V3) and chemparse(B,V3). rule acidsbasesreaction: FORALL R,R1,R2,P1,P2 base(R,P1) and base(R,R2) and acid(R,P2) and acid(R,R1) >{R1,R2}; hasProducts->>{P1,P2}] and conjugateacidbase(R1,P1) and conjugateacidbase(P2,R2). FORALL X,Y,X1,Y1 conjugateacidbase(X,Y) <- acidbase(X,X1,Y,Y1). answer("E") <- base(r,"H2O") and base(r,"SO4"). FORALL X,Y,X1,Y1conjugateacidbase(X,Y) <- acidbase(X,X1,Y,Y1). FORALL A,B,V1,V2,V3 conjugateacidbase(A,B) <- chemparse(A,V1) and chemparse("H",V2) and diffvector(V1,V2,V3) and chemparse(B,V3). Syllbus question no. 10explanations FORALL I,EX1,F1,F2,F3,F4 explain(EX1,I) <- I:Instantiation[ofRule->>acidsbasesreaction;instantiatedVars- >>{i(P2,F1),i(R1,F2),i(R2,F3),i(P1,F4)}] and EX1 is ("In the reaction above "+F2+","+F4+" and "+F1+","+F3+" are conjugate acid-base pairs. "+F1+" and "+F2+" are acting as acids, and "+F3+" and "+F4+" are acting as bases."). P1="SO4, R2="H2O, P2,"H3O, R1="HSO4" In the reaction above HSO4,SO4 and H3O,H2O are conjugate acid- base pairs. H3O and HSO4 are acting as acids, and H2O and SO4 are acting as bases.

© 2006 ontoprise GmbH Home | Menu | Partner | End Pharma / Lifescience Target Identification BiologyChemistryProductionTrials Screening

© 2006 ontoprise GmbH Home | Menu | Partner | End The cholesterol biosynthesis pathway (simplified version) Dolichol Acetyl CoA HMG -CoA Mevalonate pyrophosphate Isopenteryl pyrophosphate Geranyl pyrophosphate Farnesyl pyrophosphate Cholesterol Dimethylallyl pyrophosphate Isopentenyl Transfer RNA Squalene Ubiquinone HMG-CoA reductase Simvastatin Lovastatin Pravastatin rate-determining enzyme for the entire pathway Desmosterol

© 2006 ontoprise GmbH Home | Menu | Partner | End Pathway ontology reaction gene pathway tissue compound enzyme organism protein disease part of catalysed by acts as codes for has part of found in takes place in has substrate has productactivates inhibits plays a role in has isoform is type of group of

© 2006 ontoprise GmbH Home | Menu | Partner | End 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 reductase Homo sapiens HMDG_HUMAN P04035 coronary heart disease CHD part of catalysed by acts as codes for has part of found in takes place in has substrate has product inhibits plays a role in MevalonateHMG-CoA

© 2006 ontoprise GmbH Home | Menu | Partner | End Compound attributes - Direct attributes - properties - links to taxonomies - internet links Lovastatin [8-[2-(4-hydroxy-6-oxo-tetrahydropyran-2- yl)ethyl]-3,7-dimethyl-1,2,3, 7,8,8a-hexahydronaphthalen-1-yl] 2- methylbutanoate C24H36O5 Molecular Weight: g/mol Molecular Formula: C24H36O5 Hydrogen Bond Donor Count: 1 Hydrogen Bond Acceptor Count: 5 Rotatable Bond Count: 7 Canonical SMILES: CCC(C)C(=O)OC1CC(C=C2C1C(C(C=C2)C)CCC3 CC(CC(=O)O3)O)C Pharmacological Action: Anticholesteremic Agents Antineoplastic Agents Hydroxymethylglutaryl-CoA Reductase Inhibitors Stimulates the production of low-density lipoprotein receptors in the liver

© 2006 ontoprise GmbH Home | Menu | Partner | End 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

© 2006 ontoprise GmbH Home | Menu | Partner | End 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

© 2006 ontoprise GmbH Home | Menu | Partner | End Figures SmartWeb around 60 MB RDF(S) data 2500 classes 927 relations instances relation instances 81 rules Halo 500 rules

© 2006 ontoprise GmbH Home | Menu | Partner | End Figures Uniprot (600 MB out of 25 GB) instances relation instances MeSH Gene classes

© 2006 ontoprise GmbH Home | Menu | Partner | End 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

© 2006 ontoprise GmbH Home | Menu | Partner | End Ontobroker Architecture: RDF OWL Compiler Inference Server F-Logic Compiler RDF Compiler OXML Compiler Built-Ins Textual analyses Mathematical Operators String Operators Other Operators Web Service Connectors DIG F-LogicOWL Rewriter SPARQL Compiler Internal Database (EDB) Inference Kernel RDF Database (Oracle)

© 2006 ontoprise GmbH Home | Menu | Partner | End Oracle Ontobroker Architecture: RDF OWL Compiler Inference Server F-Logic Compiler RDF Compiler OXML Compiler Built-Ins Textual analyses Mathematical Operators String Operators Other Operators Web Service Connectors DIG F-LogicOWL Rewriter SPARQL Compiler Internal Database (EDB) Inference Kernel RDF Database (Oracle)

© 2006 ontoprise GmbH Home | Menu | Partner | End ORACLE Application Server Ontobroker: Semantic SOA Bundle BPEL Process RDF-DB SQL- DBMS File- Systems OntoBroker/OWL (Ontology Server and Rule Engine) RDF-DB Intranet RDF Layer Common Data Layer Logic FrameworkSemantic Data Hub OWL/WRLSem Metadata Rep Rules… Business Process Layer

© 2006 ontoprise GmbH Home | Menu | Partner | End Thank you! Prof. Dr. Jürgen Angele, Jayant Sharma,