Large-Scale Repositories of Highly Expressive Reusable Knowledge

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Interoperable Knowledge Representation for
Intelligence Support (IKRIS)
A challenge problem project on knowledge
representation sponsored by DTO
Technical Team Leaders
Prof. Richard Fikes Dr. Christopher
Welty Knowledge Systems, Knowledge Structures
Group Artificial Intelligence Laboratory
(KSL) T. J. Watson Research Center Stanford
University IBM Corporation
Northeast Regional Research Center Leaders
Dr. Brant Cheikes (MITRE) Dr. Mark Maybury
(MITRE)
Government Champions
Steve Cook (NSA) Jean-Michel Pomarede
(CIA) John Donelan (CIA) John Walker (NSA)
2/7/06
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Knowledge Representation and Reasoning

• Knowledge Representation
• Encoding descriptions
• That correspond in some coherent way to a world
  of interest
• Are usable by a computer to make conclusions
  about that world
• Primary areas of activity
• Developing declarative formalisms for expressing
  knowledge
• Mostly general-purpose languages (e.g.,
  First-order logic)
• Encoding knowledge (knowledge engineering)
• Mostly identifying and describing conceptual
  vocabularies (ontologies)
• Reasoning
• Automating coherent creation of new knowledge
  from existing knowledge
• Primary areas of activity
• Development and analysis of computational
  reasoning methods
• Task-specific methods such as planning,
  scheduling, diagnosis,
• Methods for managing reasoning such as hybrid
  reasoning,

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Challenge Problems for the IC

• DTO (Disruptive Technology Office) funded
  challenge problem projects
• Focus is on problems that require collaboration
  to solve
• DTO recognizes knowledge representation (KR) as a
  critical technology
• IKRIS is addressing two KR challenges
• Enabling interoperability of KR technologies
• Developed by multiple contractors
• Designed to perform different tasks
• Interoperable representations of scenarios and
  contextualized knowledge
• To support automated analytical reasoning about
  alternative hypotheses

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Hypothesis Modeling and Analysis

• Tools for modeling and analyzing alternative
  hypothetical scenarios

• Models enable automated reasoning to accelerate
  and deepen analysis
• Consistency and plausibility checking, deductive
  question-answering, hypothesis generation,
• Requires sophisticated knowledge representation
  technology
• Actions, events, abnormal cases, alternatives,
  open-ended domains,

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Interoperable KR Technology

• No one representation language is suitable for
  all purposes
• Technology development necessarily involves
  exploring alternatives
• Differing tasks require differing representation
  languages
• So, modules using differing KR languages need to
  be interoperable
• Requires enabling modules to use each others
  knowledge
• The IKRIS approach to achieving interoperability
  
• Select and refine a standard knowledge
  interchange language
• Called IKRIS Knowledge Language (IKL)
• Develop translators to and from IKL
• Each system module will then
• Use its own KR language internally
• Use IKL for inter-module communication
• Translate knowledge to and from IKL as needed

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IKRIS Organization

• Prime Contractor MITRE, Brant Cheikes and Mark
  Maybury
• Technical Team Leads Fikes (Stanford KSL) and
  Welty (IBM Watson)
• Working Groups
• Interoperability Pat Hayes, University of West
  Florida
• Chris Menzel, Michael Witbrock, John Sowa, Bill
  Andersen, Deb McGuinness,
• Scenarios Jerry Hobbs, Information Sciences
  Institute
• Michael Gruninger, Drew McDermott, David Martin,
  Selmer Bringsjord,
• Contexts Selene Makarios, Stanford KSL
• Danny Bobrow, Valeria de Paiva, Charles Klein,
  David Israel,
• Evaluation Dave Thurman, Battelle Memorial
  Institute
• Technology Transfer Paula Cowley, Pacific
  Northwest National Laboratory
• Translation technology and example translators
  Stanford KSL
• Government Champions
• Steve Cook, John Donelan, Jean-Michel Pomarede,
  John Walker

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IKRIS Project Schedule

• Preparation January - April, 2005
• Kickoff Meeting April 2005
• Established working groups and their charters
• Developed work plan and began work in each group
• Working groups April 2005 through April 2006
• Producing results and planning technology
  transfer
• Evaluation January through September 2006
• Iterative evaluation of workshop results
• Second face-to-face workshop April 2006
• Finalize and coordinate results of working groups
• Finalize plans for technology transition and for
  completing evaluation
• Technology transition April through September
  2006
• Initiation of planned transition activities

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FOL Knowledge Interchange Languages

• KIF (Knowledge Interchange Format)
• ASCII Lisp-style syntax
• No formal model theory
• Pre-WWW/XML/Unicode
• Included a set theory, definition language, etc.
• Subset became de facto AI/KR standard
• Subset developed as a proposed ANSI standard
• CL (Common Logic)
• Based on KIF
• Formal model theory (based on Menzel/Hayes)
• Abstract syntax
• Web savvy
• In final stages of becoming an ISO standard
• IKL (IKRIS Knowledge Language)
• Extension of CL
• Extensions include propositions, quoting

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CLIF Syntax for IKL

• Designed for use on an open network
• Names are made globally unique by
• Including a URI as part of the name
• Using the XML namespace conventions to abbreviate
  names
• Universal quantifiers can be restricted by a
  unary predicate
• E.g., All humans own a car.
• (forall ((x isHuman)) (exists ((y Car)) (Owns x
  y)))
• Existential quantifiers can be restricted by a
  number
• E.g., All humans have as parts 10 toes.
• (forall ((x isHuman))
• (exists 10 (y) (and (Toe y) (PartOf y
  x))))

Cool!
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Examples of CL/IKL Expressivity

• Relations and functions are in the universe of
  discourse
• E.g., (owlinverseOf parent child)
• A relation or function can be represented by a
  term
• E.g., (forall (x y r) (iff (r x y)
  ((owlinverseOf r) y x)))
• Given the above axiom,
• ((owlinverseOf parent) Arthur Ygrain)
• is equivalent to
• (child Arthur Ygrain)
• and entails
• (parent Ygrain Arthur)

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Examples of CL/IKL Expressivity

• A unary relation could be allowed to take
  multiple arguments
• So that, e.g., (isHuman Fred Bill Mary)
• abbreviates
• (and (isHuman Fred) (isHuman Bill) (isHuman
  Mary))

• We might call such relations Predicative
• E.g., assert (Predicative isHuman)
• What it means to be Predicative could be
  axiomatized as follows
• (forall (r) (if (Predicative r)
• (forall (x y z) (iff (r x y z)
• (and (r x)
  (r y) (r z))))))
• Predicative itself could be Predicative
• (Predicative Predicative)
• allowing such abbreviations as
• (Predicative isHuman isAnimal isFish)

WOW!
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Examples of CL/IKL Expressivity

• Sequence names
• Allows a sentence to stand for an infinite number
  of sentences, each obtained by replacing each
  sequence name by a finite sequence of names
• A sequence name is any constant beginning with
  
• E.g., the general axiom for Predicative is as
  follows
• (forall (r) (if (Predicative r)  
  (forall (x y ...) (iff (r x y ...)
• (and (r x)
  (r y ...))))))
• Function list and relation isList are
  predefined as follows
• (forall (...) (isList (list ...)))

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Extending CL to Include Propositions

• Goal Support representation of contextualized
  and modal knowledge
• Achieved by making propositions first-class
  entities in IKL
• Refer to them by name, quantify over them, have
  relations between them and other entities, define
  functions that apply to them,
• Technically, a proposition is a 0-arity relation
• The operator that is used to denote propositions
• that takes a sentence as an argument
• E.g., (that (Married Ygrain Uther))
• A that expression denotes the proposition
  expressed by its argument
• E.g., (that (Married Ygrain Uther))
• is a name, denoting the proposition that Ygrain
  and Uther are married
• Issue When are two propositions equivalent?
• E.g., does (and a b) name the same proposition as
  (and b a)?
• IKL provides a propositional equivalence
  relation, but does not build it in
• General propositional equivalence is undecidable

BAM!
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Relativizing Names in IKL

• In some cases, the denotation of logical names
  needs to be relativized
• (believes Mary
• (that (forall (x) (if (Child x Joe) (Male
  x))))
• but what if Mary thinks Frank is Joe?
• Need to talk about marys version of Joe
• Special class of functions quoted names
• name is a function that returns the right
  thing
• (Joe) is just Joe
• (Joe Mary) would be Frank (what Joe denotes
  to Mary)
• E.g.
• (believes (Mary
• (forall (x) (if (Child x (Joe Mary)) (Male
  x))))

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IKRIS Language Translators

• Developing 2-way IKL translators for several KR
  languages
• OWL, RDF, KIF, CycL, Slate/MSL
• API for parsing/generating IKL
• Design goal round trip compliance
• Significant new work in KR
• Major challenge to round trip OWL
• Simple embedding in IKL
• Requires axiom patterns and meta-data
• (forall (P Q)
• (gt (forall (x) (gt (P x) (Q x)))
• (owlsubclassOf P Q)))

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Interoperable Scenarios

• IKRIS is addressing two KR challenges
• Enabling interoperability of KR technologies
• Developed by multiple contractors
• Designed to perform different tasks
• Interoperable representations of scenarios and
  contextualized knowledge
• To support automated analytical reasoning about
  alternative hypotheses
• Developing an interoperable representation for
  processes
• Includes
• Time points, time intervals, durations, clock
  time, and calendar dates
• Events and relationships that overlap in time and
  interact
• Process constructs, preconditions, states, etc.

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An Interlingua for Processes
PSL
SWSL/ FLOWS
OWL-S
inter-theory
DONE!
SPARK
ResearchCyc
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The Scenarios Inter-Theory (ISIT)

• The Scenarios Working Group is producing an IKL
  inter-theory
• vocabulary
• Bridging axioms to other vocabularies
• Trigger axioms for making optional
  representational commitments
• The inter-theory vocabulary includes
• The OWL time ontology
• Terminology for clock time, calendars, intervals,
  points, etc.
• Terms such as the following to describe
  processes
• Event
• EventType
• State
• StateType

• Eventuality
• EventualityType
• FluentFor
• Subevent

• Precondition
• PreconditionToken
• Effect

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ISIT Bridging Axioms

• Example bridging axioms to Cyc for Event and
  EventType
• For every EventType x, there is a Cyc subclass
  of cycEvent that has the same instances as x
• (forall ((x EventType)))
• (exists (y) (and (cycgenls y cycEvent)
• (forall (e) (iff
  (cycisa e y)
• 
  (instanceOf e x)))))))
• For every subclass y of CycEvent, there is an
  EventType that has the same instances as y
• (forall (y) (if (cycgenls y cycEvent)
• (exists (x) (and (EventType x)
• (forall (e)
• (iff
  (cycisa e y)
• 
  (instanceOf e x)))))))

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ISIT Trigger Axioms

• Example trigger axioms for Cyc event/token
  distinction
• In Cyc, EventTypes are classes and events are
  individuals
• The inter-theory is neutral on the issue
• A commitment can be made on this issue using a
  triggering axioms
• If the TypesAreClasses trigger is true,
  EventTypes and the subclasses of CycEvents are
  equivalent
• (forall (x) (if (TypesAreClasses)
• (iff (cycgenls x cycEvent)
  (EventType x))))

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ISIT Modules

• Pre/Post conditions
• Classic AI-planning descriptions
• Triggering axioms for situations vs. flows
• Causality
• Can an event cause an event?
• Expected outcomes
• Triggering axioms identify the distinction
• Inputs/Outputs
• Processes (esp. information processing) can have
  inputs and outputs (different from pre/post
  conditions)
• Control Flow
• Are if/then/while important to model logically?
• Still under discussion

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IS IT an Ontology?

• ISIT includes the five ontologies
• New vocabulary for generalizations of common
  terms
• Trigger axioms exclude parts of the Inter Theory
  under certain conditions
• In a strict sense, it is not an ontology, but an
  amalgem of existing ontologies
• Pan-ontology?

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Interoperable Contextualized Knowledge

• IKRIS is addressing two KR challenges
• Enabling interoperability of KR technologies
• Developed by multiple contractors
• Designed to perform different tasks
• Interoperable representations of scenarios and
  contextualized knowledge
• To support automated analytical reasoning about
  alternative hypotheses

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Contextualized Knowledge is Pervasive

• The circumstances surrounding a specific activity
• E.g., In this conversation, the suspect refers
  to Faris.
• A published document
• E.g., Based on the schedule, the Holland Queen
  will arrive in Boston sometime on April 29, and
  depart there sometime on May 1.
• An intelligence report
• E.g., Pakes is listed, according to a certain
  source, on the crew roster of the Holland Queen.
• A database
• E.g., Pakes is assumed, based on certain records,
  to not be a citizen of USA.
• An assumption
• E.g., Pakess presence on board the Holland Queen
  is assumed to be typical (i.e. he does not behave
  abnormally).
• A set of beliefs
• E.g., In the belief system of Abu Musab al
  Zarqawi, democracy is evil.

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Interoperable Contextualized Knowledge

• IKRIS is producing
• A context logic with a formal model theory
• Called IKRIS Context Logic (ICL)
• Recommended ways of using the logic for IC
  applications
• E.g., to represent alternative hypothetical
  scenarios
• Methodology for translating into and out of IKL
• Methodology for automated reasoning
• The model theory supports configurable
  entailments
• Three immediate customers
• PARC, Cycorp, KANI

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Context Logic

• In McCarthys context logic
• Contexts are primitive entities
• Propositions can be asserted with respect to a
  context
• (ist c ?) means that proposition ? is true in
  context c
• E.g., (ist CM (forall (x) (implies (P x) (G
  x)))) (ist C0 (P Fred))
• How can automated reasoning be done with ist
  sentences?
• E.g., assert ( CM C0) and derive (ist C0 (G
  Fred))
• Contextualize constants rather than sentences
• Constants in ist sentences are interpreted with
  respect to the context
• E.g., Fred in (ist C0 (P Fred)) is interpreted
  with respect to C0
• Replace each constant with a function of the
  context and the constant
• E.g., (forall (x) (implies (P (iso CM x)) (G
  (iso CM x))))
• (P (iso C0 Fred))
• Use a first-order reasoner to make deductions

Whoa!
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KANIs Hypothesis Graph
S9 The event is at Select Gourmet Foods.
N3
New hypothesis added by the analyst
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Conflict Detected by KANI
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Helping Resolve Inconsistencies
Event will not occur on April 30
Pakes is not a participant
Event is not a face-to-face meeting
Event is not in Atlanta
Pakes is not in Boston on April 30
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Evaluation and Tech Transfer

• Evaluation
• Goals
• Demonstrate the practical usability of results on
  IC-relevant problems
• Provide functionality goals, scoping, and
  feedback for results
• Evaluation will be informal using sample IC tasks
• Tests will include
• Round trip translations into and out of IKL
• Inter-system knowledge exchange using IKL.
• Tech Transfer
• Goal Transition results into DTO programs and
  the IC at large
• Producing showcase presentations of results for
  transition audiences
• Being advised and facilitated by our government
  champions and MITRE

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Using CS4 to Demonstrate IKRIS Technology

• Our demonstration shows interoperability and
  collaboration among three selected NIMD
  technologies KANI, SLATE, and Noöscape
• Two motivations for interoperation
• Different (overlapping) data
• The CS4 was carefully enhanced and partitioned so
  no system by itself had sufficient knowledge to
  solve CS4
• Different (overlapping) capabilities
• To be successful, each had to call upon the
  resources of the others.
• Translators are being developed to support the
  knowledge representation languages needed to
  support those systems and to enable knowledge
  sharing.

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Summary

• IKRIS is enabling progress to be made on
  significant KRR problems
• We are addressing two KR challenges relevant to
  the IC
• Enabling interoperability of KR technologies
• Developed by multiple contractors
• Designed to perform different tasks
• Interoperable representations of scenarios and
  contextualized knowledge
• To support automated analytical reasoning about
  alternative hypotheses
• Initial versions of the technical results have
  been completed
• For more information, check out the IKRIS Web
  site
• http//nrrc.mitre.org/NRRC/ikris.htm