IKRIS: Interoperable Knowledge Representation for Intelligence Support

1. 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 WeltyKnowledge 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

2. 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, …

3. 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

4. Hypothesis Modeling and Analysis What happened? What’s the current situation? What’s going to happen? … • 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, …

5. 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 other’s 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

6. 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, DavidMartin, 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

7. 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

8. 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

9. 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!

10. Examples of CL/IKL Expressivity • Relations and functions are in the universe of discourse E.g.,(owl:inverseOf parent child) • A relation or function can be represented by a term E.g.,(forall (x y r) (iff (r x y) ((owl:inverseOf r) y x))) Given the above axiom, ((owl:inverseOf parent) Arthur Ygrain) is equivalent to – (child Arthur Ygrain) and entails (parent Ygrain Arthur)

11. 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!

12. 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 ...)))

13. 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!

14. 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 “mary’s 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))))

15. 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) (=> (forall (x) (=> (P x) (Q x))) (owl:subclassOf P Q)))

16. 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.

17. An Interlingua for Processes PSL SWSL/ FLOWS OWL-S inter-theory DONE! SPARK ResearchCyc

18. 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

19. ISIT Bridging Axioms • Example bridging axioms to Cyc for Event and EventType: • “For every EventType x, there is a Cyc subclass of cyc:Event that has the same instances as x” (forall ((x EventType))) (exists (y) (and (cyc:genls y cyc:Event) (forall (e) (iff (cyc:isa e y) (instanceOf e x))))))) • “For every subclass y of Cyc:Event, there is an EventType that has the same instances as y” (forall (y) (if (cyc:genls y cyc:Event) (exists (x) (and (EventType x) (forall (e) (iff (cyc:isa e y) (instanceOf e x)))))))

20. 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 Cyc:Events are equivalent” (forall (x) (if (TypesAreClasses) (iff (cyc:genls x cyc:Event) (EventType x))))

21. 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

22. 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?

23. 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

24. 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., Pakes’s 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.

25. 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

26. Context Logic • In McCarthy’s 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!

27. KANI’s Hypothesis Graph S1: There will be a coordinated event. S2: The event will occur on April 30. S3: Pakes is a participant. S4: Ramazi is a participant. S5: Goba is a participant. … N1 N2 S8: The event is a face-to-face meeting. N4 S10: The event is in Atlanta. N5 S11: Pakes is in Boston on April 30. S9: The event is at Select Gourmet Foods. N3 New hypothesis added by the analyst

28. Conflict Detected by KANI S1: There will be a coordinated event. S2: The event will occur on April 30. S3: Pakes is a participant. S4: Ramazi is a participant. S5: Goba is a participant. … N1 N2 S8: The event is a face-to-face meeting. S9: The event is at Select Gourmet Foods. N4 N3 S10: The event is in Atlanta. N5 S11: Pakes is in Boston on April 30.

29. Helping Resolve Inconsistencies N1 S1,S4,S5,… N1.3 N1.1 S2,S3 ~S2,S3 N1.2 S2,~S3 N2.1 N2.2 S8 S8 N2.3 N2 ~S8 S8 N4.2 N4.1 N4.3 N3.3 N3.2 N3.1 N3.3 S10 S10 S10 S9 S9 S9 S9 N5.3 N5.1 N5.2 N4.4 N4 S11 S11 S11 ~S10 S10 N5.5 ~S11 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

30. 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

31. 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.

32. Summary • IKRIS is enabling progress to be made on significant KR&R 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