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The Semantic Web, Applications and Migration Path at HP Laboratories. Bernard Burg Manager, Associative Metadata Department, HP Labs Palo Alto bernard.burg@hp.com 11 August, 2003 Sydney University. HP Fast Facts. Company name: Hewlett-Packard Company

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The Semantic Web, Applications and Migration Path at HP Laboratories


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    1. The Semantic Web, Applications and Migration Path at HP Laboratories Bernard Burg Manager, Associative Metadata Department, HP Labs Palo Alto bernard.burg@hp.com 11 August, 2003 Sydney University

    2. HP Fast Facts • Company name: Hewlett-Packard Company • Headquarters: Palo Alto, California • CEO and Chairman: Carleton S. (Carly) Fiorina • HP serves more than one billion customers in more than 160 countries on five continents • 141, 000 Employees Worldwide • $72B company Sydney University, Semantic Web & HP

    3. HP’s Mission • HP's mission is to invent technologies and services that drive business value, create social benefit and improve the lives of customers—with a focus on affecting the greatest number of people possible. • HP dedicates $4 billion (U.S.) annually to its research and development of products, solutions and new technologies. Sydney University, Semantic Web & HP

    4. HP is #1 globally in imaging and printing #1 globally in personal computers #1 globally in UNIX, Windows and Linux servers #1 globally in enterprise storage #1 globally in management software #3 globally in IT services Sydney University, Semantic Web & HP

    5. HP’s 4 Global Business Units Imagingand Printing Group Enterprise Systems Groups Personal System Group HP Services Printing and multifunction Digital Photography Scanners and projectors Supplies and accessories Servers Storage Networking Utility Data Centers Adaptive Enterprise Desktops and workstations Notebooks and Tablet PCS Handheld Devices Software Services Sydney University, Semantic Web & HP

    6. HP Labs’ roles • Contribute to HP strategy creation and alignment • Deliver technology that enables HP to win in HP’s selected strategies through: • Breakthrough technologies • Technology advancements • Create new opportunities for HP that go beyond current strategies • Invest in fundamental science and technology in areas of interest to HP Sydney University, Semantic Web & HP

    7. palo alto cambridge japan israel india HP Labs Worldwide • Director & SVP Dick Lampman • ~750 employees worldwide • ~5% of $4B HP R&D budget bristol Sydney University, Semantic Web & HP

    8. HP Labs Major Research Areas Printing, Imagingand Storage Internet ComputingPlatform Solutions andServices Advanced Studies Printing technologies Imaging Technologies Digital Photography Storage (MRAM, ARS) Utility Data Center Adaptive Enterprise Mobile Systems Trusted Systems Digital Media Systems Intelligent EnterpriseTechnologies Semantic Web Systems Research Data Management Quantum Information Processing Computational Bioscience Sydney University, Semantic Web & HP

    9. Motivation for the Semantic WebApplication IntegrationMarket and Early Adopters

    10. Interaction Mediation Buyers Model (Ontology) Sellers Model (Ontology) Interface Buyer Seller Interaction Interaction -> effect in terms of state change ‘Spontaneous’ state change -> effect in terms of interaction Order State Placed Shared Model at Interface (Ontology) Shipped addToBasket Delivered checkOut Accepted Cancelled Returned Sydney University, Semantic Web & HP

    11. Motivating the Semantic Web from human<->computer interoperability to computer<->computer interoperability XML, the current web, is not adequate • captures structure, not semantics (relationships, constraints) • tags (properties) have no description • requires humans intermediaries to define mappings RDF/OWL, the Semantic Web, has promise • RDF models objects, relationships • relationships (properties) are objects (have descriptions) • OWL adds rich constraints • machines can infer mappings (the big hope for interoperability) The Semantic Web is a W3C standard Sydney University, Semantic Web & HP

    12. Application integration today RosettaNet Appli x Appli y Msg 1 Msg 1 Msg 2 Msg 2 … … Msg k Msg n The Essence of the Semantic Web • Semantic modeling is key technology, because it allows machine processing of metadata descriptions • XML lacks modeling power Manual mapping - Brittle – must be updated as msgs evolve Maps elements, not relationships Sydney University, Semantic Web & HP

    13. ontologies SW Compiler Decrease:- manual mapping - integration time Msg 1 Msg 1 Msg 2 Msg 2 … … Msg k Msg n Msg l Increase: - flexibility- resilience Leverage Semantic Modeling TransformationProgram aggregation Added Value of Semantic Modeling Application integration with Semantic Web Appli x Appli y Msg schema Msg schema Sydney University, Semantic Web & HP

    14. Potential Markets & Early adopters • Existing market: • Application Integration Software Market • $4.28B in 2001 $15.53B in 2006 (IDC #27236, June 02) • New spaces (for HP) • Adaptive Enterprise • The path to semi-structured data mgmt • SIMILE (HP, MIT, W3C) and Digital Publishing • Early adopters: • Adobe, Boeing, HP, IBM, SUN Microsystems, … Sydney University, Semantic Web & HP

    15. From the Semantic Web Standard toa Migration Path

    16. Trust Rules Data Proof Data Logic Digital Signature Ontology Vocabulary Self-desc. doc. RDF + rdfschema XML + NS + xmlschema URI Unicode What’s the Technology Angle?Why not just XML? • Goal is semantic interoperability • XML gives data exchange standard for consenting partiesHard to reuse, hard to extend schema, hard to merge data • RDF gives common data model -> syntactic interoperation • URIs gives common space of identifiers • Ontology layer gives explicit conceptual model behind the terms – allows translation between schemas, data integration, reuse, full interoperation • Logic/proof layer allows exchange of evidence chains (“believe this because …”) Sydney University, Semantic Web & HP

    17. Semantic Web Technologies • composable, extensible fact/metadata representationgives syntactic interoperability • RDF triple-model • representation for structure and nature of terms – ontologiesalso composable and extensibleprovides foundation for semantic interoperability • description logics • OIL, DAML, DAML+OIL, OWL (lite, DL, full) • techniques for translating between ontologies, ontology-based data integration • proof and trust layers for exchange of evidence chains (believe because …) Sydney University, Semantic Web & HP

    18. RDF in a nutshell • A data model for assertions about things labelled by URIs plus an XML syntax • all facts are “subject -> predicate -> object” triplesthese form a graph of assertions • predicates disambiguated by XML namespace • everything is a resource (or a literal string) • can “reify” assertions so can assert“W3C claims ‘RDF importance veryHigh’” http://doc dc:creator dcq:creatorType rdf:value Illustrator Joe Smith Sydney University, Semantic Web & HP

    19. Ontology in a nutshell • a formal, explicit specification of a shared conceptual model (aka domain model) • describes the terms used and their relationships • concept names and concept hierarchy • roles (predicates) and role hierarchy • concept expressions, associated axioms • could think of it as a glorified schemaentity-relationship models are a subset Sydney University, Semantic Web & HP

    20. Nuin: agent toolkit Joseki: RDF Canonicalisation Jena 1 Jena2 2002 Jena1 is used by more than 60% of the community 2003 Jena2 is still the leader What HP does for you! Source of picture: W3C Sydney University, Semantic Web & HP

    21. Applications RDQL • Full support for RDF2003 Rule Systems External reasoners RDFS OWL Lite Ontology API OWL DAML +OIL OWL Full OWL DL OWL Lite RDFS The RDF 2003 API Jena SPI Fast path query Mem BDB RDB Progress on Technology – From Jena 1 to Jena 2 Jena 2 Architecture Jena 1 Architecture Jena 2 = Jena 1 plus Applications • Generalised ontology API with profiles for the OWLs, DAML+OIL, RDFS,… RDQL • OWL Syntax Checker The DAML API • Rule Systems XML ARP • Extensible reasoning support for RDFS and OWL Lite, including support for external plug-in reasoners n-triples n-triples The RDF 2003 API The RDF API XML Event handling Reification RDF Filter • Fast-path database query (via Genesis) Writers Readers • Efficient reification • Event handling Stores • Necessitated complete rearchitecture Sydney University, Semantic Web & HP

    22. JosekiCoarse-grained RDF processing • A NetAPI • standard operations on models • coarse-grain wrapper to local fine-grained interaction • Application framework for RDF applications • Application paradigm • Publishing RDF data • Large RDF models • Multiple applications collaborating • Shared, updated RDF Sydney University, Semantic Web & HP

    23. The Nuin agent engine • A BDI agent engine, written in Java • based on Rao’s AgentSpeak(L) languge [Rao 1997] • extensible knowledge representation based on FOPC with actions • designed to be programmer extensible at all points • default capabilities to make it easy to write agent behaviour “out-of-the-box” • interpreter for abstract actions • built-in action library of core capabilities • script parser for human-readable script syntax • abstract services to allow pluggable connection to infrastructure service providers (FIPA platforms, SOAP, Joseki, etc) • integrated with Jena for semantic web processing Sydney University, Semantic Web & HP

    24. reasoner reasoner reasoner Nuin architecture overview beliefs evaluationfunctions event andmessage queue agent core desires intentions agent configuration (rdf) interpreter serialized agent script(s) plan library action library knowledge source knowledge source’ abstract service adapter layer message service directory service Java objectinvocation JADE agent platform RSS translator exampleconcrete services Sydney University, Semantic Web & HP

    25. Genesis: migration path of SW Distributed Genesis models on top of scalable, persistent Jena Ease of Use Higher-level objectsImmutabilitySecurityDistribution/Caching Apps (EAI/SIMILE) Optimization Semi-autonomous performance analysis, benchmark, tuning,STOR Genesis Jena 2 Scalability Support Jena 2 APIApp-specific schemasMySQL,PostgreSQL,Oracle Efficient Graph Queries Database Sydney University, Semantic Web & HP

    26. How to use these tools • Currently Available • Jena 2, Joseki • Open source, BSD license, no restrictions to use • http://www.hpl.hp.com/semweb/index.html • Announced for October • Nuin • Open source, BSD license, no restrictions to use • http://www.hpl.hp.com/semweb/index.html Sydney University, Semantic Web & HP

    27. Mobile Users, Contexts,Agents

    28. Our customers • are mobile (small devices) • want immediate solutions to their problems • have little time to waste • web browsing shows its limits in this context Sydney University, Semantic Web & HP

    29. @HPL-PA Work policies food me @home Topology of Mobile Applications • hypothesis • open world: any service, any object, real/virtual • micro-worlds mapped to • domains (inside firewalls, security, trust, games, work) • cells (location awareness, cellular nets) • Need of policies and contexts • Need of semantic descriptions • Need of proactive behaviour Sydney University, Semantic Web & HP

    30. Building Blocs: Standards • W3C Semantic Web (ongoing effort) • FIPA Agents solved the communication problem between Agents (about 20 implementations, 7 open-source) • Agents are ubiquitous, from server, to laptop, PDA and phone. Sydney University, Semantic Web & HP

    31. Ipswich Dublin Saarbruecken San Francisco London Paris Berlin Chambery Palo Alto Lausanne Lisbon Sendai Salt Lake City Parma Miami Barcelona Honolulu Sydney Melbourne Dunedin Building Blocs: Large Scale Deployment Open testbed 70 platforms deployed over 5 continents, Agentcities Workshop at AAMAS Web Services, work in Technical committee with: IBM, HP, Intel, Fujitsu, SAP, Sun Microsystems, Mitre, Motorola… Sydney University, Semantic Web & HP

    32. Added value to our customers • did we bring the solutions to their problems? • less browsing • more active & pertinent services • due to: • superior context awareness • agents proactivity Sydney University, Semantic Web & HP

    33. Next Steps

    34. HP as a research partner • HP is supporting several collaborations in Australia (7?) • I am working on projects around mobility and context • Monash U. Melbourne (2 projects) • Flinders U. Adelaide • Sydney U. Sydney University, Semantic Web & HP

    35. HP - Australia in General • HP has high profile collaborations in USA: • MIT, Berkeley (Citrus project) … • HP has high profile collaborations in EU: • HPL Bristol has European projects, Universities, student exchange… • HP has high profile collaborations in India: • HPL India has build an amazing network of connections • In Australia, we should become the lighthouse project for Semantic Web in context modelling to become HP’s champions Sydney University, Semantic Web & HP

    36. Raising HP’s investments in Australia is • Build a HP’s network of influence in Australia • Excellence in academia • Champions in “relevant” domains • Succeed in existing project • Establish student exchange • Link with industrial tissue • Shared projects with: HP, Academia and industrial partners • Develop market specific relations • Increase HP’s revenues Australia Sydney University, Semantic Web & HP