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SAIC System architecture

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SAIC Architecture

SAIC Architecture

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  • This diagram shows the entire arsenal of AI tools available and built into our architecture in order to solve any problem requiring an AI solution. All communicate via a central OKBC bus. The AI tools may be classified into 3 VERY ROUGH categories. In some cases, a tool classified under question answering may be used as a problem solving tool and vice versa. HIKE provides the infrastructire to facilaite communication between components. SAIC also provides the web based GUI within HIKE
  • Unfamiliar or large domains mean the language and reasoning may be non-obvious to the user long reasoning chains even of simple modus ponens typically requires explanation sophisticated axioms need explanation and so do the magical axioms that are inside most theorem provers. Simple cuts require explanations to non-prolog people disjoint primitives resulting in 0 cardinality slots require explanation in description logics etc if critical decisions will be made based on deductions, then reasoning verification is required
  • Unfamiliar or large domains mean the language and reasoning may be non-obvious to the user long reasoning chains even of simple modus ponens typically requires explanation sophisticated axioms need explanation and so do the magical axioms that are inside most theorem provers. Simple cuts require explanations to non-prolog people disjoint primitives resulting in 0 cardinality slots require explanation in description logics etc if critical decisions will be made based on deductions, then reasoning verification is required
  • Unfamiliar or large domains mean the language and reasoning may be non-obvious to the user long reasoning chains even of simple modus ponens typically requires explanation sophisticated axioms need explanation and so do the magical axioms that are inside most theorem provers. Simple cuts require explanations to non-prolog people disjoint primitives resulting in 0 cardinality slots require explanation in description logics etc if critical decisions will be made based on deductions, then reasoning verification is required
  • Unfamiliar or large domains mean the language and reasoning may be non-obvious to the user long reasoning chains even of simple modus ponens typically requires explanation sophisticated axioms need explanation and so do the magical axioms that are inside most theorem provers. Simple cuts require explanations to non-prolog people disjoint primitives resulting in 0 cardinality slots require explanation in description logics etc if critical decisions will be made based on deductions, then reasoning verification is required
  • SQ230a How would the Y1 Phase II Persian Gulf Scenario be affected if BW experts of Libya did not provide advanced technology and scientific expertise aid to a terrorist group?
  • Members of the un security opposed iraq because the un security council passed a resolution that opposed iraq. There is an axiom that states that members of a group that oppose an enemy oppose the enemy. (note others also directly opposed iraq in ground fact style and we explain that as well; here we only go through the non-ground deductions)
  • Followup to this shows that un resolution xx is a sanction which is a diplomatic action
  • SQ230a How would the Y1 Phase II Persian Gulf Scenario be affected if BW experts of Libya did not provide advanced technology and scientific expertise aid to a terrorist group?
  • We do not yet handle skolems or functions in as nice a manner as we should
  • This is the list of institutions invovled on the project. The institutions listed are very prestigious and highly respected in the academic world.
  • This is the list of institutions invovled on the project. The institutions listed are very prestigious and highly respected in the academic world.
  • This diagram shows the entire arsenal of AI tools available and built into our architecture in order to solve any problem requiring an AI solution. All communicate via a central OKBC bus. The AI tools may be classified into 3 VERY ROUGH categories. In some cases, a tool classified under question answering may be used as a problem solving tool and vice versa. HIKE provides the infrastructire to facilaite communication between components. SAIC also provides the web based GUI within HIKE

SAIC System architecture SAIC System architecture Presentation Transcript

  • Presentation Agenda - SAIC Introduction - Stanford (KSL) - SRI International - Stanford (Formal Reasoning Group) - NWU - MIT - CMU - TextWise - SAIC SummaryDARPA
  • SAIC Integrated Knowledge Environment (SIKE) Architecture Architecture exists at two levels - System Level Architecture Transport Layer Syntactic Layer Knowledge Architecture Semantic LayerDARPA
  • HPKB Integrated Knowledge Environment (HIKE) Architecture Architecture exists at two levels - System Level Architecture Transport Layer Syntactic Layer Knowledge Architecture Semantic LayerDARPA
  • System Level Architecture Features A distributed heterogeneous environment to solve Crisis Management Challenge Problem. Federation of OKBC(Open Knowledge Base Connectivity) servers Added power of component-based approach for the distribution of knowledge content Web based graphical user interfaceDARPA
  • Analyst HIKE HIKE START START GUI GUI GKB GKB SNARK Editor SNARK Editor JOT JOT Ocelot Ocelot && PERK PERK SME SMETextWise TextWise MAC/FAC MAC/FAC ATPL ATPL WebKB Ontolingua Ontolingua WebKB ATP ATP
  • Crisis Management - Knowledge Level Architecture Knowledge Architecture design is an output of the Knowledge Architecture working group convened by SAIC Includes the SAIC merged ontology The SAIC merged ontology contains the year 1 knowledge bases from KSL, NWU, FRG, SRI, SAIC, and CMU Ontology merging effort led by Stanford KSL led to development of the KB merging toolDARPA
  • SAIC CM CP Knowledge Architecture HPKB Upper Level SAIC Merged Ontology (Y1) PQ Interests Actions Cases Analogy ... Year 2 Domain SpecificDARPA
  • SAIC Merged Ontology (Y1) Domains Capability Analysis Benefits/Risks analysis Terrorism World Fact Book International economics model A National interests model A model of economic, military, and diplomatic support/opposition. World oil flowDARPA
  • SAIC Merged Ontology (Y1) Domains Properties of multilateral organizations Capabilities and Resources International Organizations, Companies Military weapons, artillery, personnel Strike Capabilities EIA pages (oil quotas, etc) International Organizations, memberships, goals Geographical informationDARPA
  • Common Knowledge Components PQ Ontology Ontology used to define the vocabulary available for the user to query the system. Actions A model of international actions described in the International System Framework Document (ISF). Interests A model of national interests and strategic interests defined by the ISF.DARPA
  • Common Knowledge Components (Cont’d) Analogy Ontology Case Library Year 1 Scenario Year 2 Scenario 1998 Iranian-Taliban Crisis Abu Musa Incident Caspian Pipeline Consortium (CPC) Operation Desert Shield 1990-1 1984-8 Tanker WarDARPA …
  • Knowledge Base Development Strategy Shared upper structure and SAIC merged ontology Common components across developers Periodic KB merging into common componentsDARPA
  • Knowledge Architecture Currently available in Ontolingua HPKB upper level SAIC merged Ontology (Y1) PQ Ontology Knowledge Components ….. http://ontolingua.stanford.eduDARPA
  • SAIC Crisis Management Year 2 PQ distribution Different technology developers assume responsibility for specific PQs, but make use of shared knowledge structures PQ distribution as shown (next slide)DARPA
  • Parameterized Question Distribution 200 SRI 220 SRI 240 SAIC 201 SRI 221 SRI 202 SRI 222 SRI 251 SAIC 203 SRI 223 NWU 252 KSL 204 SRI 224 NWU 253 KSL 205 FRG 225 NWU 254 SRI 206 SRI 226 NWU 255 SAIC 207 FRG 228 NWU 124 KSL, MIT 209 SRI 125 KSL, MIT 210 SRI 230 FRG 126 KSL, MIT 211 KSL 231 FRG 127 KSL, MIT 212 KSL 232 KSL 128 KSL 213 KSL 233 KSL 214 SAIC 234 SRI 216 MIT/START 236 SAIC 217 MIT/START 237 SAIC 238 SAIC 219 SRI 239 SAICDARPA
  • Critical Component Experiments (CCEs) Theory Merging CCE Led by KSL. Merges CMU, FRG, KSL, NWU, SAIC and SRI Knowledge Bases. Develops merging tools and techniques Merging evaluation (TBD)DARPA
  • Critical Component Experiments (CCEs) Knowledge Extraction (TextWise) TextWise parses a multi-year multi-source corpus to produce output that populates terrorism templates defined by SAIC. Phased approach Terrorist Group Template definitions loaded into SNARK KB (currently available) Post January: Population of Terrorist Event and Supporting Action templatesDARPA
  • Critical Component Experiments (CCEs) Natural language interface to selected Parameterized Questions using START/SNARK MIT START team parses natural language and converts this text into KIF formalizations that are then input to SRI SNARK theorem prover. Server used for START queries also used by SAIC GUI interface.DARPA
  • Critical Component Experiments (CCEs) Analogical Reasoning Led by NWU NWU will answer the analogical reasoning PQs for the SAIC integration team. The questions will be answered as follows Analogy Ontology (NWU) SME, MAC/FAC (Analogical Reasoner) (NWU) Case Library (SAIC) All Ontologies stored in OntolinguaDARPA
  • SAIC Crisis Management User Interfaces GUI interface to SNARK (live) remote version (Server at SRI) local (server on laptop) GUI interface to ATP Lisp translator to facilitate batch interface processing of PQsDARPA
  • Stanford KSLDARPA
  • Stanford KSL Richard Fikes Deborah McGuinness James Rice Gleb Frank Yi SunDARPA
  • Stanford KSL-ATP & ATPL ATP is supported and in use for challenge problem work Providing ATP for use by FRG ATP has been upgraded to handle larger KBs ATP client side listener developed for remote building and testing of KBs (see demo!) ATPL available for SAIC challenge problem use offered knowledge server support to NWUDARPA
  • KSL-Challenge Problem Work PQ answers (over 1/4 of questions) KB diagnostics differential questions Merging CCE Led merge of Y1 KBs Developed initial merging tool Providing knowledge library of individual and merged Y1 (and Y2) KBsDARPA
  • Explanation Approach I Break queries and answers into components based on their logical form conjunctive antecedents are separated follow-up queries are generated for those that are not directly asserted query bindings may be presentedDARPA
  • Explanation Approach II Present in pseudo natural language Use documentation strings and internal templates Axiom: Diplomatic-Opposition-Propagation-Due-To-Group-Membership (=> (and (Opposed-Diplomatically ?group ?enemy ?time-range) (Group-Members ?group ?member)) (Opposed-Diplomatically ?member ?enemy ?time-range)) Doc String: ?member diplomatically opposed ?enemy because ?member is a member of ?group, which opposed ?enemy.DARPA
  • Explanation Approach III Prune (and/or rewrite) internal axioms delete internal axioms such as “if a class is known to be non-primitive, its primitiveness is false” by setting explanation-visibility to be internal generate abstract presentation strings for axioms such as taxonomic inheritanceDARPA
  • Explanation Approach IV Present abstractions for multiple answers “members of the UN-Security Council opposed Iraq” rather than listing all of the members Provide meta language for contextual and domain-oriented pruning explanation visibility, slots to use for abstraction, “interesting” slots, etc.DARPA
  • TAA68 What countries diplomatically opposed Iraq after the Persian Gulf War?DARPA
  • Incremental ExplanationsDARPA
  • Incremental Explanations IIDARPA
  • DARPA
  • Status and Plans Status Implemented for ATP Tested on KSL Y1 and some Y2 queries Plans Implement pruning meta language based on description logic foundation Expand to other reasoners (e.g., SNARK) Demonstrations availableDARPA
  • SRIDARPA
  • SRI’s Contribution to Integration Helped conceptualize the HIKE GUI Delivered a PC-based SNARK server Helped produce the SAIC merged ontology START/SNARK interface Loading information extracted by TextwiseDARPA
  • Merging with Team SAIC Syntactic merge Semantic merge Computational mergeDARPA
  • Syntactic Merge KBs translated into the same language Different ways to write the same thing (person ?x) or (instance-of ?x person) We converted our KBs into a syntax that will be readable by KSL Most (95%) of the work can be automatedDARPA
  • Semantic Merge Semantic merge Identical terms should have the same definitions Differences in representational choices (Supporting-Terrorist-Attack ?action) = (and (instance-of ?action action) (supports ?action terrorist-attack)) Mostly manual, but some tools possibleDARPA
  • Computational Merge Merged KB can be as efficiently reasoned with as the original Sorted vs unsorted language Consider (father ?x ?y) The first argument must be a male The second argument must be a person In a sorted language, ?x will unify with only malesDARPA
  • CMCP Knowledge Base HPKB Upper Level SAIC Merged Ontology (Y1) PQ Interests Actions Agents Cases Reading Option Option Comprehension Generation EvaluationDARPA
  • CMCP Knowledge Base Responsibility for about 20 PQs Actively co-developing content with SAICDARPA
  • Interface with Project Genoa Direct Structured entry by SMEs Argumentation A1 Fusion A1.1 A1.2 A1.3 A1.4 Fusion Fusion Fusion Fusion Q 1.1.1 Q 1.1.2 Q 1.1.3 Q 1.2. 1 Q 1.2.2 Q 1.2.3 Q 1. 3.1 Q 1.3. 2 Q 1.3.3 Q 1.4.1 Q 1.4..2 Q 1.4.3 Argument Fina l Conclusion Publish Templates OK Caution Warning Is the project being managed according to the project plan? Evidence: Will the effort be completed on or ahead of schedule? Will this effort be completed w ithin the budget? Will the technical solution be developed according to plan? Arguments Will project resources for this effort be available according to plan? OK Caution Warning Will operations be satisfied by the results of the project?DARPA Evidence: Will the projected capital & operating costs meet requirements? Will the projected operating performance meet requirements? Do projected operating benefits justify expected expenditures? Are communications between project & operations staff satisfactory?
  • Interface with Project Genoa Accomplishments for 1998 SEAS Server HTTP/HTML CL-HTTP Server WWW Browser CWEST SEAS HTML Grasper Generator Ontology Manager OKBC OKBC Ocelot KBMS GKB-Browser Arg./Sit. Ontology Perk Storage System Gister Engine A1 F usion SQL Oracle Oracle DBMS A1.1 A1.2 A1 .3DARPA DB Server F usion F usion F usion F usion Q1 .1.1 1.1 .2 1 .1 .3 Q1.2 .1 1 .2.2 1 .2 .3 Q1.3 .1 1 .3.2 1.3 .3 Q1 .4.1 .4 ..Q 1.4 .3 Q Q Q Q Q Q Q1 2
  • Interface with Project Genoa Plans for 1999 Integration at content level Use situation ontology from HPKB for argument indexing Multi-user editing of arguments Use collaboration system for asynchronous editing Domain-specific GUI for editing argument ontologyDARPA Enhance GKB-Editor to be more accessible to
  • MIT - STARTDARPA
  • MIT (START): Y2 Integration Plans Link START to other HPKB systems by translating English queries into PQ specifications, then forwarding the translated queries Extend the START Server’s KB with background knowledge to support analyst’s activities Support answering selected Parameterized Questions for the Y2 Crisis Management Challenge Problem Increase START’s access to “live” information from the World Wide Web by incorporating robust access interfacesDARPA
  • MIT (START): New Coverage for Y2 • Material from the International System Framework and Agent-Specific Background Information documents, supporting PQs 216, 217, 124, 125, 126 and 127 • Background information on terrorist groups, including membership, activities, funding and locations • Weapon strike capabilities between Persian Gulf regions and countries • Information on Fortune 500 companies, including locations of headquarters, CEOs, assets, profits and stock prices • Information on 30,000 U.S. cities, including areas, populations, coordinates, time zones and weatherDARPA
  • MIT (START): New Coverage for Y2Material from the International System Framework and Agent-Specific Background Information documents, supporting PQs216, 217, 124, 125, 126 and 127Background information on terrorist groups, includingmembership, activities, funding and locations Weapon strike capabilities between Persian Gulf regions andcountries Information on Fortune 500 companies, including locations ofheadquarters, CEOs, assets, profits and stock prices Information on 30,000 U.S. cities, including areas, populations,coordinates, time zones and weather
  • NWUDARPA
  • CMUDARPA
  • CMU CM Plans Extract relevant ground facts from the Web company instances name locations of operations economic sector products produced and raw materials consumed (especially those on export-control lists) relations with other companies pieces of infrastructure instances of <EconomicActionType>DARPA
  • CMU CM Plans Deliver extracted facts to integration teams via OKBC. Use facts to support PQs 200, 201, 203, 211, 216, etc. by representing economic interests, capabilities and actions of international agents, and links among agents.DARPA
  • Integration of Text Extraction with SAIC Terrorism DB Ian Niles TextWise, LLC
  • SAIC Terrorism DB(defobject ABU-NIDAL-ORGANIZATION"International terrorist organization led by Sabri al-Banna. Split from PLO in 1974. Made up of various functional committees, including political, military, and financial.(Source: 1996 Patterns of Global Terrorism:App. B: Background on Terrorist Groups, http://www.iet.com/Projects/HPKB/Web mirror/GLOB_terror/appb.html)” (own-slot-value nick-name ABU-NIDAL-ORGANIZATION "ANO") (individual ABU-NIDAL-ORGANIZATION) (instance-of ABU-NIDAL-ORGANIZATION terrorist-group) (residence-of-organization ABU-NIDAL-ORGANIZATION libya))
  • Integration of CRCs into DB Terrorist Group template instances were automatically generated from KNOW-IT output in three steps: A base template instance is created for each example of the proper noun category 54 (terrorist groups) CRCs referencing terrorist groups are mapped to slots of the terrorist group template. The automatically generated slots are inserted into the appropriate template instances.DARPA
  • Automatically Generated Template Instances(defobject HAMAS "(Source: 1998 TextWise LLC Terrorism Database)" (individual HAMAS) (instance-of HAMAS terrorist-group) (affiliated-with Palestine-Liberation-Organization) (own-slot-value nick-name HAMAS Hamas) (own-slot-value nick-name HAMAS Islamic-Resistance- Movement) (residence-of-organization HAMAS Israel) (residence-of-organization HAMAS United-States) (residence-of-organization HAMAS West-Bank))
  • Automatically Generated Template Instances (con’t)(defobject Hizballah "(Source: 1998 TextWise LLC Terrorism Database)" (individual Hizballah) (instance-of Hizballah terrorist-group) (affiliated-with Islamic-Jihad) (own-slot-value nick-name Hizballah Islamic-Jihad-for-the-Liberation-of-Palestine) (own-slot-value nick-name Hizballah Lebanese-Hizballah) (own-slot-value nick-name Hizballah Party-of-God) (own-slot-value nick-name Hizballah Hezbollah) (own-slot-value nick-name Hizballah Hizbollah) (own-slot-value nick-name Hizballah Organization-of-the-Oppressed-on-Earth) (own-slot-value nick-name Hizballah Revolutionary-Justice-Organization) (residence-of-organization Hizballah Lebanon))
  • Future Integration Work Crafting more rules to extract instances of the 54 (terrorist group) proper name category Automatic generation of instances of the two other Terrorism DB templates Mapping more relations and combinations of relations to template slots Making the ouput KIF 3.0 CompliantDARPA
  • •Carnegie Mellon University •Massachussets Institute of Technology •North Western University •SRI International •Stanford University (Formal Reasoning Group) •Stanford University (Knowledge Systems Laboratory) •Stanford University (Scaleable Knowledge Composition)•TextWise
  • •Carnegie Mellon University •George Mason University •Information Sciences Institute •Massachussets Institute of Technology •North Western University •SRI International •Stanford University (Formal Reasoning Group) •Stanford University (Knowledge Systems Laboratory) •Stanford University (Scaleable Knowledge Composition) •Stanford Medical Informatics•TextWise
  • BackupsDARPA
  • SAIC Crisis Management only KB Development Time (Exluding TextWise) 25000 20000 SRI(SAIC) 15000Axioms KSL NWU 10000 CMU 5000 0 TQA TQC TQD TQB Feb June (SQ) May Aug Dec Mar Jan Apr Months
  • SAIC Crisis Management only KB Development Time 90000 80000 70000 SRI(SAIC) 60000 KSLAxioms 50000 NWU 40000 TextWise 30000 CMU 20000 10000 0 TQA TQC TQD TQB Feb June (SQ) May Aug Dec Mar Jan Apr Months
  • TextWise 1. Create a terrorism database partition by retrieving a large multi- year, multi-source corpus of documents which mention the terms"terrorism", "terrorist" or "terrorists" and running the document processing system over these documents (date of deliverable: 11/27). 2. Create an index from every canonicalized PN in the version of PNDBin /home/chess/CYC to all of its non-canonicalized variants (date ofdeliverable: 11/27). 3. Implement the pseudo-code for the Template Instance Generator (TIG)(date of deliverable: 12/31). 4. Design and implement component which will convert sets of CRCs intoinstances of Supporting Actions and Terrorist Attacks templates.DARPA
  • CreditsCMU - webKB Stanford KSL - Ontolingua, ATP• Tom Mitchell • Richard Fikes• Mark Craven • Deborah McGuinness • James RiceMIT - START • Gleb Frank• Boris Katz• Gary Borchardt Stanford - FRG • John McCarthyNWU - Flow Model • Tom Costello• Ken Forbus• Jeff Usher TextWise - Know-IT • Liz LiddySRI - SNARK, GKB Editor • Woojin Paik• Vinay Chaudhri• Richard Waldinger USC ISI - LOOM/EXPECT• Mark Stickel • Yolanda Gil
  • CreditsUSC ISI - LOOM SMI - Protege• Bob Mcgregor • Mark Musen• Hans Chalupsky • Natalya Fridman Noy• David Moriarty • Bill GrossoCycorp - Cyc SAIC - SIKE• Doug Lenat • Dave Easter• Ben Rode • Albert Lin • Barbara StarrTeknowledge - TFS • Don Henager• Adam Pease • Henry Gunthardt• John Li • Ben Good• Cleo Condoravdi • Brian Truong • Bryner PanchoGMU - Disciple • Lei Wang• George Tecucci
  • HIKE N-tier Architecture HIKE HPKB HPKB HIKE HIKE HIKE Server Technology Technology Server Client Client Component Component HTTP HPKB HPKB HIKE HIKE Technology HIKE Technology HIKE Server Server Component Client Sockets (TCP/IP) Component Client HPKB HPKB HIKE HIKE HIKE Technology OKBC HIKE HIKE Server Stub Technology Stub Component HIKE Server Component Client Client OKBC Loom HPKB HPKB Loom HIKE HIKE Technology OKBC OKBC Stub Technology Stub Component Server Server ComponentJava RMI
  • Three Levels of Integration There are 3 levels at which integration can occur: Transport layer (e.g. Sending information from one server to another) Syntactic layer (Ensuring that information is in the same syntax as that defined by another system) Semantic layer (Ensuring that all concepts and theories are aligned)DARPA
  • HIKE Analyst START HIKE START GUI GUI GKB GKB SNARK SNARK Editor Editor Ocelot Ocelot SME SMETextWise TextWise MAC/FAC MAC/FAC WebKB Ontolingua Ontolingua WebKB ATP ATP
  • Knowledge Architecture Currently available in Ocelot (Via GKB editor) HPKB upper level Actions Ontology Interests Ontology SAIC/SRI Y1 Ontology lajolla.ai.sri.com:8000DARPA
  • Knowledge Servers A federation of OKBC Knowledge Servers LOOM (USC ISI) Ontolingua (Stanford KSL) Ocelot (SRI) Cyc (Cycorp) ATP Manual Knowledge Acquisition Tools GKB Editor (SRI) Ontolingua (Stanford KSL) JOTDARPA ATPL
  • Knowledge Servers (Cont’d) Semi- Automatic Knowledge Acquisition KNOW-IT (TextWise) Text extraction from the web, newsfeeds and other sources webKB (CMU) Knowledge Extraction (and discovery) from web based sources. Expect (USC ISI) Automatic generation of rulesDARPA
  • Question Answering Natural Language Understanding START (MIT) Parses natural language queries. Multimedia web based answers from annotated web sources. TextWise Parses natural language queries. Returns answers from web based sources by parsing textual information. Theorem Provers SNARK (SRI)DARPA ATP (Stanford KSL)
  • Problem Solvers Machine Learning Disciple Learning Agent (GMU) multi-strategy learning methods Problem Solving Methods Problem Solving Methods Stanford Medical Informatics (SMI) Three layered PSM to detect, classify, and monitor battlefield activities. Information Science Institute (ISI) Course of Action Generation problem solvers to create alternative solutions to workaroundsDARPA problems.
  • Problem Solvers (Cont’d) Bayesian Networks SPOOK (Stanford Robotics Laboratory) System for Probabalistic Object Oriented Knowledge - supports reasoning with uncertainty Qualitative Reasoning NWU/KSL supports construction of certain types of models such as flow models, e.g. : World Oil flow model Common Sense reasoning about the battlespace, focusing on the trafficability/terrain suitability task.DARPA
  • Problem Solvers (Cont’d) Monitoring Process Massachusetts Institute of Technology (MIT) provides tools for constructing and controlling networks of distributed monitoring processesDARPA
  • Crisis Management - Knowledge Level Architecture Knowledge Architecture design is an output of the Knowledge Architecture working group convened by SAIC Includes the SAIC merged ontology The SAIC merged ontology contains the year 1 knowledge bases from KSL, FRG, SRI/SAIC, and CMU Ontology merging effort led by Stanford KSL led to development of the KB merging toolDARPA