OntoMorph: A Translation System
for Symbolic Knowledge
Hans Chalupsky
Loom/PowerLoom Group
USC Information Sciences Institute
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Overview
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Motivation
Translation Problem
OntoMorph Overview
OntoMorph Applications
Conclusions
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Motivation
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Observations
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Ontology development collaborative but independent
Repeated merges/imports are the norm
Different tasks solved using different KR systems
 different syntax, knowledge models, expressivity
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Different tasks require different modeling styles
Translation problem is ubiquitous
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Merging of semantically overlapping ontologies
Distributed heterogeneous agent communication
Integration of independently developed K-based systems
Porting of K-based systems to different KR infrastructure
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Some Opinions
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“We need standards, KIF, OKBC, HPKB upper
structure, etc. to minimize translation needs….”
“We’ll never decide on that standard representation
language, everybody has their own favorite…”
“People will hack through any representation
available…”
“We better learn to live in a world where everybody
represents things differently…”
“What’s the big deal, I can write any X-to-KIF
translator in 5 Prolog clauses/minutes”
“Writing translators is not really difficult…”
Sound familiar?
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The Translation Problem
Target KB
Source KB
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KBs describable in some linear syntax
sentence-based translation
single expression to whole KB
arbitrary semantic shift allowed  morphing
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Translation Dimensions
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KR language syntax
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KR language expressivity
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class distinction vs. attribute relations, argument order,
argument reification, naming conventions,...
Model coverage and granularity
Representation paradigms
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quantification, negation, defaults, sets, modals, ...
Modeling conventions
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KIF, Loom, PowerLoom, MELD, SNePS, OntoLingua, …
time, action, plans, causality, propositional attitudes, ...
Inference system bias
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collections vs. individuals, subclass-of vs. =>, ...
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Example: Syntax Differences
Loom:
(defconcept Automobile
“The class of passenger cars.”
:is-primitive Road-Vehicle)
MELD:
(#$isa #$Automobile #$Collection)
(#$genls #$Automobile #$RoadVehicle)
(#$comment #$Automobile “The class of passenger cars”)
KIF:
(defrelation Automobile (?x)
“The class of passenger cars”
:=> (Road-Vehicle ?x))
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Example: Model Differences
Ontology A
Ontology B
Vehicle
WheeledVehicle
M151
Automotive-Device
TrackedVehicle
HMMWV
M1
M60
maxSlopeRange
(AngleRange -60 45)
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M-1
M-151
traction-type
traction-type
tracked
wheeled
max-gradient
45
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Traditional Translation Methods
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Manual translation is
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slow
tedious
error-prone
hard to repeat
hard to document and trace
often simply not practical
Special-purpose translators are
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tedious to write
hard to maintain
not easily reusable
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Need: Translation Tool
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Desiderata:
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Translate arbitrary KR languages
No fixed knowledge model
Concise, declarative specification of transformations
Support for automatic, repeatable translations
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Solution: OntoMorph
Rapid and concise specification of KB translators  via
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Syntactic rewriting
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pattern-directed rewrite rules
sentence-level transformation of syntax trees
based on pattern matching
Semantic rewriting
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modulates syntactic rewriting
uses integrated PowerLoom KR system
based on (partial) semantic models
uses logical inference
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OntoMorph Rewrite Engine
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Pattern language and execution model similar to
PLisp (Pattern Lisp), Lisp-70 Pattern Matcher
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Features
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Rapid generation of ontology and KR language translators
Rewrites arbitrary syntax trees
Powerful pattern language for concise specification and
destructuring of expressions
Full backtracking (can parse Type-0 languages)
Named rule sets provide modularization and search control
Seamless integration with PowerLoom facilitates semantic
rewriting
Available in Lisp, C++ and Java (written in STELLA)
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Pattern Language
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Literals match themselves:
 foo, hans, 2, (a (b c) d), etc.
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Variables match complete subtrees:
 ?x, ?bar, ?
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Sequence variables match tree subsequences:
 (??x foo ??y), ??
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Grouping (AND) matches a sequence of tokens:
 {a ?x c}
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Alternatives (OR) match alternative token sequences:
 {a|(b ?x)|c d}
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Optionals match optional token sequences:
 {a [b c]}
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Pattern Language, cont.
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Repetition matches a pattern multiple times:
 {a|b}+, {a|b}*1-2
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Binding input matched by a pattern to a variable:
 ?x := {a|(b ?y)|c} matched against (b d)
binds ?x to (b d).
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Example Pattern
Match key-value pairs in any order:
(defconcept ?name
{?is := {:is | :is-primitive} ?def |
:characteristic ?cha |
:annotations
?ann := {(documentation ?doc) |
(:and ?? (documentation ?doc) ??) |
?}}*0-3)
Example pattern instance:
(defconcept Dog
:annotations (:and Object-Type
(documentation “Canine”))
:is-primitve Animal)
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Basic Operation
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Rewrite Rule Syntax
pattern => result
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Execution Model
Example Rule: (isa ?i ?c) => (?c ?i)
Input stream:
( isa car1 Ford ) ( speed car1 ………
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Basic Operation
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Rule Syntax
pattern => result
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Execution Model
Example Rule: (isa ?i ?c) => (?c ?i)
Input stream:
( isa car1 Ford ) ( speed car1 ………
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Basic Operation
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Rule Syntax
pattern => result
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Execution Model
Example Rule: (isa ?i ?c) => (?c ?i)
Input stream:
( Ford car1 ) ( speed car1 ………
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Named Rule Sets and Recursion
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Rule Set Syntax
(defruleset name
pattern1 => result1
...
patternN => resultN)
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Function calls and rule recursion
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Rule sets and functions can be invoked recursively
Arguments are consumed and results pushed back onto the
input stream.
{<Term> ?result}
<Term ?x>
<is-atom? ?x>
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Rewrite Rule Example
Rule definition:
(defruleset Term
(?op := {\+|-|\*|/} ?x ?y) => (?op <Term ?x> <Term ?y>)
(1\+ ?x) => (\+ <Term ?x> 1)
(1- ?x) => (- <Term ?x> 1)
(square ?x) => (\* <Term ?x> <Term ?x>)
?x => ?x )
(defruleset Condition
(lt ?x ?y) => (negative? (- <Term ?x> <Term ?y>))
(gt ?x ?y) => <Condition (lt ?y ?x)> )
Rule application:
(rewrite (gt (/ (1+ M) N) (square N)) Condition)
=> (negative? (- (* N N) (/ (+ M 1) N)))
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Rewrite Rule Example: Turing Machine
(defruleset Turing
(?p := (??x (?s ?h ?act ?ss) ??y) ?s (?l ?h ?r)) =>
<Turing (?p ?ss <Move-Head ?act (?l ?h ?r)>)>
(?prog ?state ((??l) ?h (??r))) => (??l ?h ??r) )
(defruleset Move-Head
{R ((??l) ?h ())} => ((??l ?h) $ ())
{R ((??l) ?h (?r ??rt))} => ((??l ?h) ?r (??rt))
{L (() ?h (??r))} => (() $ (?h ??r))
{L ((??lt ?l) ?h (??r))} => ((??lt) ?l (?h ??r))
{?new (?l ?h ?r)} => (?l ?new ?r) )
Compute f(x) = x + 2:
(rewrite ( ((s1 $ R s2) (s2 1 R s2) (s2 $ 1 s3)
(s3 1 R s3) (s3 $ 1 s1))
s1 (() $ (1 1 1)) ) Turing)
=> ($ 1 1 1 1 1)
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Semantic Rewriting
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Syntactic rewriting very useful but limited:
(defruleset Conflate-Truck-Types
({Light-Truck | Heavy-Truck | ... } ?x)
=>
(Truck ?x) )
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Instead, use semantic test:
(defruleset Conflate-Truck-Types
{(?class ?x)
<ask (subset-of ?class Truck)>}
=>
(Truck ?x) )
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Semantic rewriting via integration with PowerLoom KRS
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Two-Pass Translation Scheme
Source KB
Pass 1:
Import Rules
PowerLoom KB
Pass 2:
Translation Rules
Target KB
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Rewriting Non-Lisp-Style Expressions
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Lisp-style s-expressions for tree representation.
Rewriting not limited to Lisp-style languages.
Rewrite engine operates on input stream of tokens.
Currently, STELLA reader serves as tokenizer.
Other tokenizers can easily be substituted.
Rewrite engine could specify tokenizers.
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special-purpose tokenizers will be much more efficient.
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OntoMorph Application:
Input Translation for COA Critiquer
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Input Translation for COA Critiquer
OntoMorph
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Fusion Output to EXPECT:
Translation Issues
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Different Names
(defruleset Rename-Collection
FixMilitaryTask => FIX
{ProtectingSomething | Protecting…Region} => PROTECT
Translation-LocationChange => MOVE
… … …)
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Fusion Output to EXPECT:
Translation Issues cont.
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Different Syntax: MELD/KIF => Loom
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(isa task1 Fix-MilitaryTask) => (Fix task1)
(isa COAMt COASpecificationMicrotheory) => (COA COAMt)
(relationInstanceExistsCount subOrgsDirect unit1 ArmoredSpec)
=> (:about unit1 (:exactly 2 subOrgsDirect ArmoredSpec))
(defruleset Rewrite-Frame-Predicate
(relationInstanceExistsCount ?rel ?inst ?type ?count)
=>
(:ABOUT <Rewrite-Term ?inst>
(:EXACTLY ?count <Rename-Relation ?rel>
<Rename-Collection ?type>)) )
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Fusion Output to EXPECT:
Translation Issues cont.
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Different Representations
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(taskHasPurpose BlueDivisionTask
(thereExists ?p
(isa ?p (CollectionSubsetFn ProtectingSomething
(TheSetOf ?obj
(and (objectTakenCareOf ?obj Boundary1)
(performedBy ?obj BlueDivision1)))))))
=>
(and (Protect protect-000)
(Purpose-Action Protect-000)
(purpose-of BlueDivisionTask Protect-000)
(action-obj Protect-000 Boundary1)
(who protect-000 BlueDivision1))
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Fusion Output to EXPECT:
Translation Issues cont.
(defruleset Rewrite-Purpose-Pattern1
{(taskHasPurpose ?task
(thereExists ?var
(isa ?var (CollectionSubsetFn
?type (TheSetOf ?action ?body)))))
<Generate-Skolem-Name ?type> ?purpose}
=>
(AND
(<Rename-Collection ?type> ?purpose)
(PURPOSE-ACTION ?purpose)
(PURPOSE-OF ?task ?purpose)
<Rewrite-Purpose-SetOf-Body ?body ?action ?purpose> )
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Fusion Output to EXPECT:
Translation Issues cont.
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Missing Representations
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Some COA structure was not explicitly represented
 EXPECT critiquer needed task / subCOA associations
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Some essential COA structure was embedded in comments
(defruleset Track-COA-Assertion
(unitAssignedToTask ?task ?unit)
=>
<!ASSERT (AND (Term ?task) (Term ?unit)
(unitAssignedToTask ?task ?unit))>)
(defruleset Get-Task-Assigned-To-Unit
{?unit
<RETRIEVE \?t (= (unitAssignedToTask \?t) ?unit)>
?task}
=>
<OBJECT-NAME ?task> )
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Fusion Output to EXPECT:
Summary
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File-based translation of Fusion output MELD to KIF to Loom
Required ~30 rewrite rule sets
Tree destructuring capability very useful for rewriting
complex purpose representations
Semantic rewriting needed to recover unrepresented
COA structure
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Translation between Distributed
Heterogeneous Agents
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Rosetta Translation Service
Rosetta
aN
PLANET
Plan Ontology (HPKB)
a2
Agents
Agent Capabilities
Ontology
aN
a1
M
CoABS
Grid
Operators
a1
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Domain
Model
Agent Model
Operators
Mapping KB
Domain
Model
a2
Agent Models
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Rosetta Translation Service
Agent2 model
Agent1 model
Capabilities/
requests
KB of representation
reformulation rules
Requested
service by Agent1
Capabilities/
requests
Capability of Agent2
Rosetta
Wrapper
Agent1
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ForMAT to Prodigy Translation
via Rosetta
ForMAT request:
(:goal (G-144 :send-hawk ((force 42nd-Hawk-Battalion)
(geographic-location Big-Town))))
Format-To-Rosetta translation:
(find (object Plans)
(for (Objective-Based-Goal
(send-unit (object 42nd-Hawk-Battalion)
(to Big-Town)))))
Rosetta goal representation translation:
(find (object Plans)
(for (State-Based-Goal
(is-deployed (object 42nd-Hawk-Battalion)
(at Big-Town)))))
Rosetta-To-Prodigy translation:
(:find-plans
(is-deployed 42nd-Hawk-Battalion Big-Town))
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Using Rosetta with CoABS TIE 1:
Ontology-Based Transformations
Problem: Diverse views and languages (e.g., helicopters had
segments, SAMSITE finder queried with SQL about regions)
Without Rosetta:
 helicopter wrappers had to turn
route segments into areas and
issue query in SQL
 message sent:
:content select distinct s.LAT,
s.LON from SAMSITE s where
s.LAT< 29.3843 and s.LAT> 29.355
and
s.LON< 48.0399 and s.LON> 48.011
:receiver ARIADNE_TEAMCORE
:sender TEAMARIADNE
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With Rosetta:
 helicopter wrappers form
request using route segments
 message sent:
:content
find SAMSITES from DP_A to LZ_A
:sender TEAMARIADNE
:receiver Rosetta
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Conclusion
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Translation is a common problem with K-based systems
OntoMorph tool makes it easier to write translators
OntoMorph applications so far:
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Within HPKB to translate Fusion output
Within CoABS/HPKB TIE to implement Rosetta ontology-based
agent translation services (joint work with Y. Gil and J. Blythe)
Future Work
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Develop library of operators to support common transformations
Semi/Automatic generation of translators (some related work in
database integration community, but larger schemas, higher
expressivity, inconsistency, etc.)
Rewrite rule compiler
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