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XML + Semantics = DARPA Agent Markup Language (DAML)

XML + Semantics = DARPA Agent Markup Language (DAML). William Holmes, Dr. Paul Kogut Management & Data Systems Valley Forge, PA June 4, 2001. Roadmap. The Semantic Web Agents & Ontologies Object Management Group (OMG) Initiatives The D ARPA A gent M arkup L anguage (DAML) What is it?

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XML + Semantics = DARPA Agent Markup Language (DAML)

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  1. XML + Semantics = DARPA Agent Markup Language (DAML) William Holmes, Dr. Paul Kogut Management & Data Systems Valley Forge, PA June 4, 2001

  2. Roadmap • The Semantic Web • Agents & Ontologies • Object Management Group (OMG) Initiatives • The DARPA Agent Markup Language (DAML) • What is it? • How does it fit in? / What is its role? • LM M&DS UML-based Ontology Toolset (UBOT) • Ontology Design & Consistency Checking • Automated Annotation via AeroTextTM

  3. Semantic Web: The Vision Hi Pete, it’s Lucy. I’m at the doctor’s office. Mom needs to see a specialist and then has to have a series of physical therapy sessions. Biweekly or something. Can you split the chauffeuring with me? Great! I’ll have my agent set up the appointments. Hello? Sure Lucy. RING … RING ... * Berners-Lee, Hendler, Lassila “The Semantic Web” Scientific American, May 2001

  4. The Vision Lucy’s agent looks up several lists of providers and checks for ones in-plan for Mom’s insurance, within a 20-mile radius of her home, and with a rating of excellent or very good. Schedule a treatment plan for Mom using Pete and my schedules. Only use providers that are in-plan for Mom’s insurance, are within a 20-mile radius, and have a rating of excellent or very good. Lucy’s agent formulates a schedule of appointments for therapists with appointments available that fit into Pete and Lucy’s schedule. Lucy’s agent retrieves information about Mom’s prescribed treatment from the doctor’s agent. Semantic Web * Berners-Lee, Hendler, Lassila “The Semantic Web” Scientific American, May 2001

  5. That’s Great but How? • Need Agents • Definition (Merriam-Webster): • one who is authorized to act for or in the place of another as a business representative • Provide a means of processing the volumes of information found on the web. • Need Ontologies • Definition: • Philosophy - A theory about the nature of existence. • A.I. - A formal definition of relations among terms. • Provide a “semantic grounding” for the web.

  6. What are Agents? • In software, “Agent” is used in many different ways: • persistent process/daemon: • mobile code • autonomous robots • “intelligent agent” - what makes it intelligent? • simple definitions that capture the essence of agents: • an Object that decides when to say go and when to say no - OMG • “programs that operate at a high enough semantic level that they can form new connections to other programs in order to get a job done” Burstein, McDermott

  7. Internet / Intranet agents I need to go to Fort Worth on Monday for 3 days. hotels car rental personal assistant agent itinerary, tickets & maps maps airlines Why Agents? • Agents are the next generation of middleware • built on top of existing middleware (e.g., CORBA, EJB, Jini) • run-time integration via dynamic discovery and resource negotiation • emphasis on broker and facilitator agents (e.g. yellow pages) • Agents are the next generation user interface • more complex applications require personal assistant agents • multi-modal interfaces e.g. speech, handwriting, gestures • user specifies goals and agent handles details according to user preferences

  8. Why Agents? (Cont.) • Agents are the next level of component abstraction • agents are components with attitudes • beliefs, desires, goals…* • agents interact like humans via speech acts • request, inform, promise • agents share a context for efficient communication • domain model ontologies are used at run-time • ontology agent/services - query, retrieve and translate ontologies *Labrou, Finin, Peng “Agent Communication Languages:The Current Landscape” IEEE Intelligent Systems March/April 1999

  9. Examples of Agent Applications* • personal assistant - digital secretary • travel arrangements • meeting schedule coordination • personalized information filtering • mobile computing • internet/intranet information retrieval/summarization • electronic commerce • enterprise workflow - e.g., sales, order processing, shipping • military command and control • synthetic characters (e.g., Extempo Systems, Virtual Personalities) • robots - manufacturing, office, domestic • design and engineering *see Hendler “Is There An Intelligent Agent in Your Future?” http://helix.nature.com/webmatters/agents/agents.html

  10. Ontologies • Machine readable semantic specifications. • Include terms, relations, and inference rules • What does “capital” mean? • Seat of government (Tallahassee, Harrisburg, Austin) • An upper-case letter • monies, securities, investments, etc… • the top of a column or pillar. • XML is Not Enough!!! • Allows definition of syntax, but not semantics (meaning) • Can be considered the “Assembly Language” of the Web.

  11. OMG Initiatives • OMG Agent Platform Special Interest Group (SIG) • extend the OMG Object Management Architecture (OMA) to better support agent technology • identify and recommend new OMG specifications in the agent area • recommend agent-related extensions to existing and emerging OMG specifications • promote standard agent modeling techniques • see http://www.objs.com/agent/index.html • OMG Ontology Working Group • Align the domain modeling activities of OMG with the Semantic Web initiative of the World Wide Web Consortium and with related ontology development projects such as DARPA DAML and IEEE SUO (Standard Upper Ontology).

  12. DARPA Agent Markup Language • Machine-Readable Ontologies & Annotation (markup) • Aimed at “Resources”, Not just web-pages • Sensors • Services • Appliances • Lots of industry Buzz* • Scientific American • IEEE Distributed Systems • New York Times • ZDNet • … *See http://www.daml.org/inthenews.html

  13. DAML annotation queries web pages links web crawlers DAML ontologies annotate manually or semi-automatically queries DAML annotation links queries schema RDBMS queries data web pages, databases, legacy software, devices, sensors... have annotations linking their terms to ontologies agents DAML: Basic Idea

  14. Evolution of Metadata explicit semantic agreements via machine-readable ontologies implicit semantic agreements on paper! Subject verb object semantics for selected sentences document parsing info XML schema Full semantics for all content keywords browser web crawler XML parsers agents (near-term) agents (future) DAML Annotation: Extreme Metadata

  15. DAML Program • Main DAML website = www.daml.org • Duration: August 2000 to Fall 2002 • Approach: • MIT W3C semantic web activity • http://www.w3c.org/2001/sw/ • “The semantic Web and its languages” in IEEE Intelligent Systems, November/December 2000, pages 67-73 available at http://www.ksl.Stanford.EDU/projects/DAML/ • Extend XML/RDF • represent ontologies • annotate web pages and other information with links to ontologies

  16. DAML Program (Cont.) • 17 research teams and 1 integration team • industry, academia and World Wide Web Consortium • expertise in AI knowledge representation, logic and web technologies • cooperation with European Union IST Program • www.daml.org/committee/ • DAML language definition • Ontology Definition • Rules Definition

  17. DAML Program (Cont.) • DAML tools • ontology development and verification • web page annotation • dynamic composition of agent services • distributed query processing and inference • ontology translation • DAML trial applications • Government: Intelink, Center for Army Lessons Learned • Commercial: e-commerce, information retrieval

  18. DAML RDFS RDF XML The Origins of DAML • Extensible Markup Language (XML) • provides syntactic interoperability • depends on implicit semantic agreements • Resource Description Framework (RDF) • designed to represent metadata for web resources in an XML syntax • triples: • RDF Schema (RDFS) • adds OO concepts: class and subclass <shoeGen:GovermentOrganization rdf:ID="DARPA”/> <shoeGen:OrganizationHomePage rdf:about="http://www.darpa.mil/"> <shoeProj:authorOrg rdf:resource="#DARPA" /> </shoeGen:OrganizationHomePage> * For more information see www.w3.org

  19. Status of DAML • DAML+Oil (ontology) • released January 2001 - latest revision March 2001 • language specifications and documentation: • http://www.daml.org/2001/03/daml+oil-index.html • design rationale • http://www.cs.man.ac.uk/~horrocks/Slides/index.html • DAML-L (logic) • rule representation and reasoning • development in progress

  20. UML-Based Ontology Toolset (UBOT) • We are applying: • graphical modeling and formal verification techniques from software engineering • text extraction from natural language processing • lexical semantic resources from cognitive science • to build a tool-set that supports • creation, extension and consistency checking of DAML ontologies • DAML annotation of information resources for agents • intended for users who have minimal training in knowledge representation and agent theory • see http://ubot.lockheedmartin.com/

  21. UBOT Team • Lockheed Martin Management & Data Systems • architecture, development and integration • Versatile Information Systems (Northeastern University) • formal verification of UML • Lockheed Martin Advanced Technology Center • field test of DAML and UBOT • Kestrel Institute • automated formal methods

  22. DAML Ontology Engineer UBOT XMI models UML DAML Translation UML GUI Consistency checking results XMI models Extended DAML ontologies UML Formalization Baseline DAML ontologies Slang models Semantic inconsistencies Specware UBOT Architecture: Ontology Engineering

  23. UBOT UML DAML Translation XMI UML GUI corrected annotation uncorrected annotation automatically generated Extraction to DAML Translation DAML annotated text or web pages DAML Annotator DAML Ontologies Text or web pages Text Extraction UBOT Architecture: Annotation

  24. UBOT Architecture: COTS Components • UML GUI • Tau UML Suite (Telelogic) • Specware (Kestrel Institute) • supports ontology consistency checking via formal methods • SNARK theorem prover (SRI) • Text Extraction • AeroText (LM M&DS) • extracts entities (e.g. people, organizations, etc.) from natural language • recognizes relationships between entities (e.g. [organization]hired[person] ) • developed for the U.S. Intelligence Community • 12 years experience with sophisticated linguistic processing • many fielded applications

  25. UML GUI: Tau UML Suite

  26. Text Extraction: AeroText Document Window Extraction Display

  27. Automatic Annotation: AeroDAML

  28. Questions?

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