The document outlines various organizations' plans to contribute to the SAIC Crisis Management integrated knowledge environment (HIKE) in year 2. It discusses Stanford KSL enhancing explanation capabilities for the ATP reasoner and merging additional knowledge bases. SRI will help with ontology merging and load information extracted by Textwise. MIT START plans to link to other HIKE systems and expand coverage areas. NWU and CMU did not provide details on their year 2 plans.

11. Presentation Agenda
- SAIC Introduction
- Stanford (KSL)
- SRI International
- Stanford (Formal Reasoning Group)
- NWU
- MIT
- CMU
- TextWise
- SAIC Summary
DARPA

12. SAIC Integrated Knowledge
Environment (SIKE)
Architecture
Architecture exists at two levels -
System Level Architecture
Transport Layer
Syntactic Layer
Knowledge Architecture
Semantic Layer
DARPA

13. HPKB Integrated Knowledge
Environment (HIKE)
Architecture
Architecture exists at two levels -
System Level Architecture
Transport Layer
Syntactic Layer
Knowledge Architecture
Semantic Layer
DARPA

14. 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 interface
DARPA

15. Analyst
HIKE
HIKE
START
START GUI
GUI
GKB
GKB SNARK
Editor SNARK
Editor JOT
JOT Ocelot
Ocelot
&&
PERK
PERK
SME
SME
TextWise
TextWise MAC/FAC
MAC/FAC
ATPL
ATPL
WebKB Ontolingua
Ontolingua
WebKB ATP
ATP

16. 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 tool
DARPA

17. SAIC CM CP Knowledge
Architecture
HPKB Upper Level
SAIC Merged Ontology (Y1)
PQ Interests Actions Cases Analogy ...
Year 2 Domain Specific
DARPA

18. 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 flow
DARPA

19. 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 information
DARPA

20. 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

21. 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 War
DARPA
…

22. Knowledge Base
Development Strategy
Shared upper structure and SAIC merged
ontology
Common components across developers
Periodic KB merging into common
components
DARPA

23. Knowledge Architecture
Currently available in Ontolingua
HPKB upper level
SAIC merged Ontology (Y1)
PQ Ontology
Knowledge Components
…..
http://ontolingua.stanford.edu
DARPA

24. 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

25. 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 SAIC
DARPA

26. 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

27. 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 templates
DARPA

28. 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

29. 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 Ontolingua
DARPA

30. 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 PQs
DARPA

31. Stanford KSL
DARPA

32. Stanford KSL
Richard Fikes
Deborah McGuinness
James Rice
Gleb Frank
Yi Sun
DARPA

33. 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 NWU
DARPA

34. 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) KBs
DARPA

35. 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 presented
DARPA

36. 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

37. 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 inheritance
DARPA

38. 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

39. TAA68 What countries diplomatically opposed
Iraq after the Persian Gulf War?
DARPA

40. Incremental Explanations
DARPA

41. Incremental Explanations II
DARPA

42. DARPA

43. 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 available
DARPA

44. SRI
DARPA

45. 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 Textwise
DARPA

46. Merging with Team SAIC
Syntactic merge
Semantic merge
Computational merge
DARPA

47. 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 automated
DARPA

48. 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 possible
DARPA

49. 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
males
DARPA

50. CMCP Knowledge Base
HPKB Upper Level
SAIC Merged Ontology (Y1)
PQ Interests Actions Agents Cases
Reading Option Option
Comprehension Generation Evaluation
DARPA

51. CMCP Knowledge Base
Responsibility for about 20 PQs
Actively co-developing content with SAIC
DARPA

52. 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?

53. 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 .3
DARPA
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

54. 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
ontology
DARPA Enhance GKB-Editor to be more accessible to

55. MIT - START
DARPA

56. 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 interfaces
DARPA

57. 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 weather
DARPA

58. 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 weather

59. NWU
DARPA

60. CMU
DARPA

61. 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

62. 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

63. Integration of Text Extraction
with SAIC Terrorism DB
Ian Niles
TextWise, LLC

64. 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))

65. 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

66. 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))

67. 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))

68. 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 Compliant
DARPA

69. •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

70. •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

71. Backups
DARPA

72. SAIC Crisis Management only
KB Development Time (Exluding TextWise)
25000
20000
SRI(SAIC)
15000
Axioms
KSL
NWU
10000
CMU
5000
0
TQA
TQC
TQD
TQB
Feb
June (SQ)
May
Aug
Dec
Mar
Jan
Apr
Months

73. SAIC Crisis Management only
KB Development Time
90000
80000
70000
SRI(SAIC)
60000
KSL
Axioms
50000
NWU
40000
TextWise
30000
CMU
20000
10000
0
TQA
TQC
TQD
TQB
Feb
June (SQ)
May
Aug
Dec
Mar
Jan
Apr
Months

74. 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

75. Credits
CMU - webKB Stanford KSL - Ontolingua, ATP
• Tom Mitchell • Richard Fikes
• Mark Craven • Deborah McGuinness
• James Rice
MIT - START • Gleb Frank
• Boris Katz
• Gary Borchardt Stanford - FRG
• John McCarthy
NWU - Flow Model • Tom Costello
• Ken Forbus
• Jeff Usher TextWise - Know-IT
• Liz Liddy
SRI - SNARK, GKB Editor • Woojin Paik
• Vinay Chaudhri
• Richard Waldinger USC ISI - LOOM/EXPECT
• Mark Stickel • Yolanda Gil

76. Credits
USC ISI - LOOM SMI - Protege
• Bob Mcgregor • Mark Musen
• Hans Chalupsky • Natalya Fridman Noy
• David Moriarty • Bill Grosso
Cycorp - Cyc SAIC - SIKE
• Doug Lenat • Dave Easter
• Ben Rode • Albert Lin
• Barbara Starr
Teknowledge - TFS • Don Henager
• Adam Pease • Henry Gunthardt
• John Li • Ben Good
• Cleo Condoravdi • Brian Truong
• Bryner Pancho
GMU - Disciple • Lei Wang
• George Tecucci

77. 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 Component
Java RMI

78. 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

79. HIKE Analyst
START HIKE
START GUI
GUI
GKB
GKB SNARK
SNARK
Editor
Editor
Ocelot
Ocelot
SME
SME
TextWise
TextWise MAC/FAC
MAC/FAC
WebKB Ontolingua
Ontolingua
WebKB ATP
ATP

80. Knowledge Architecture
Currently available in Ocelot (Via GKB
editor)
HPKB upper level
Actions Ontology
Interests Ontology
SAIC/SRI Y1 Ontology
lajolla.ai.sri.com:8000
DARPA

81. 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)
JOT
DARPA ATPL

82. 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 rules
DARPA

83. 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)

84. 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 workarounds
DARPA problems.

85. 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

86. Problem Solvers (Cont’d)
Monitoring Process
Massachusetts Institute of Technology (MIT)
provides tools for constructing and controlling
networks of distributed monitoring processes
DARPA

87. 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 tool
DARPA