March 14, Dr. Douglas B. Lenat, 3721 Executive Center Drive, Suite 100, Austin, TX Phone: (512) July 2005 For Your Eyes Only Upper Ontology Symposium

March 14, Tues. a.m.: issues talk, to UO community Tues. p.m.: review my 1 st public Wed. talk Wed. a.m.: semi-public: UO Applications Wed. p.m.: public: value of formal ontol Wed. p.m.: public: about our communique Thu.: related talk at Mitre for S&T analysts For Your Eyes Only 15 min 25min 10min Upper Ontology Symposium

March 14, To first order: I agree with the communiqué (apple pie) How/why I was forced into this field Upper ontology mostly just impacts efficiency –Of the vocabulary (lower ontology): fewer terms, simpler terms –Of the axioms: fewer, terser, less ambiguous –Of the various types of cross-ontology mapping axioms What needs to be shared No correct UO; and yet no need for separate indep. UOs –Have contexts (microtheories) and an ist relation Ontologies at that point seem to be normal 1 st -class objects As with any important region of the ontology, facet that –12 useful (categories of) facets or dimensions of ontology-space Just a few remarks about OpenCyc and ResearchCyc Upper Ontology Symposium

March 14, To first order: I agree with the communiqué (apple pie) How/why I was forced into this field Upper ontology mostly just impacts efficiency –Of the vocabulary (lower ontology): fewer terms, simpler terms –Of the axioms: fewer, terser, less ambiguous –Of the various types of cross-ontology mapping axioms What needs to be shared No correct UO; and yet no need for separate indep. UOs –Have contexts (microtheories) and an ist relation Ontologies at that point seem to be normal 1 st -class objects As with any important region of the ontology, facet that –12 useful (categories of) facets or dimensions of ontology-space Just a few remarks about OpenCyc and ResearchCyc Upper Ontology Symposium

March 14, How/why I was forced into this Goal: Amplify human beings via ubiquitous real AI Reality: Throughout the 1960s and 1970s, every subfield of AI kept hitting the same brick wall: BRITTLENESS BOTTLENECK NL understanding, speech understanding, robotics, learning, expert systems, search, semantic database integration,… (Programs need massive amounts/coverage of common sense and general world knowledge)

March 14, UO mostly just impacts efficiency Of the vocabulary (lower ontology): fewer and simpler terms Ex: non-souled trees and souled trees. Ex: FranceIn1985 Ex: grue and bleen Of the axioms: fewer, terser, less ambiguous – Ex: things grue by day are usually bleen at night – Ex: when smurfing a car, first smurf the key Of the cross-ontology mapping axioms Repr. E.g., natural language sentences E.g., node & link diagrams E.g., high-resolution imagery E.g., n th –order logic formulae E.g., database tuples E.g., algebraic equations

March 14, Thing Individual Intangible SpatialThing Situation Temporal Thing Intangible Individual Something Existing SetOr Collection Static Situation Partially Tangible Partially Intangible Event Time Interval Attribute Value Physical Event Composite TangibleAnd IntangibleObject Intangible Existing Thing Mathematical Object Relationship Predicate ActorSlot Role Set- Mathematical Collection TruthFunctional Quantifier Logical Connective Function- Denotational Configuration

March 14, upper ontology task-specific knowledge HUMMVs lose 18% traction in 4-inch-deep mud Water is wet Vehicles slow down in bad weather So if the UO mostly impacts efficiency, where is the power?

March 14, So if the UO mostly impacts efficiency, where is the power? The Upper Level need only be adequate The Lower Levels supply the minutiae The Intermediate Level is locus of power So Upper + Intermed. is what we need to share

March 14, Answering even an innocuous-sounding question: Can vehicle X get from Y to Z by time t ? may require intermediate-level knowledge about localized spatial things, pathways, earth sciences, weather, topography, oceanography (depth, temperature, biota), terrain, transportation, industry, vehicles, geopolitics (international waters), communications, the driver, holidays,... So Upper + Intermed. is what we need to share

March 14, bits/bytes/streams/network… alphabet, special characters,… words, morphological variants,… syntactic meta-level markups (HTML) semantic meta-level markups (SGML, XML) content (logical representation of doc/page/...) context (models of the users prior/tacit knowledge (incl. common sense, recent history), wants/needs, budget,…and n dimensions of metadata: time, space, level of granularity, the sources purpose/ideology...) What Needs to be Shared? Semantic Web

March 14, bits/bytes/streams/network… alphabet, special characters,… words, morphological variants,… syntactic meta-level markups (HTML) semantic meta-level markups (SGML, XML) content (logical representation of doc/page/...) context (models of the users prior/tacit knowledge (incl. common sense, recent history), wants/needs, budget,…and n dimensions of metadata: time, space, level of granularity, the sources purpose/ideology...) What Needs to be Shared? Tiny vocabulary (# distinctions) of standard relations: rdf:type, subclass, label, domain, range, comment,… Beyond which diversity is tolerated Which means divergence is inevitable What do you mean we have no standard, we have lots of standards! DAML+OIL, OWL add a few more distinctions: inverses, unambiguous properties, unique properties, lists, restrictions, cardinalities, pairwise disjoint lists, datatypes, … To do the logical/arithmetic combination across information sources, we need tens of thousands of relations, not tens

March 14, bits/bytes/streams/network… alphabet, special characters,… words, morphological variants,… syntactic meta-level markups (HTML) semantic meta-level markups (SGML, XML) content (logical representation of doc/page/...) context (common sense, recent utterances, and n dimensions of metadata: time, space, level of granularity, the sources purpose, etc.) What Needs to be Shared? Analogy: # words in the English language To do the logical/arithmetic combination across information sources, we need tens of thousands of relations, not tens

March 14, There is no correct UO Are apes monkeys? Are poinsettias red flowers? Do we need to distinguish instance & subtype? Are these two terms one and the same thing? –Black US Presidents in the 20 th Century –Female US Presidents in the 20 th Century Davidsonian reification of events or not?

March 14, (marriedIn ) Events are rich (no limit to the number of arguments) (groom Wedding0947 JoeSmith) (bride Wedding0947 JaneDoe) (dateOfEvent Wedding0947 (DayFn 13 (MonthFn May (YearFn 1999))))

March 14,

March 14, No need for separate ontologies Are apes monkeys? Are poinsettias red flowers? Do we need to distinguish instance & subtype? Are these two terms one and the same thing? –Black US Presidents in the 20 th Century –Female US Presidents in the 20 th Century Davidsonian reification of events or not? (ist ) Each of these is true in some contexts and false in others Contexts (microtheories) are themselves terms in the ontology. (genlMt HockeyMt SportsMt) 12 facets or dimensions that (largely) characterize a Mt.

March 14, If its raining, carry an umbrella the performer is a human being, the performer is sane, the performer can carry an umbrella; thus: the performer is not a baby, not unconscious, not dead, the performer is going to go outdoors now/soon, their actions permit them a free hand (e.g., not wheelbarrowing) their actions wouldnt be unduly hampered by it (e.g., marathon-running) the wind outside is not too fierce (e.g., hurricane strength) the time period of the action is after the invention of the umbrella the culture is one that uses umbrellas as a rain- (not just sun-)protection device, the performer has easy access to an umbrella; thus: not too destitute, not someone who lives where it practically never rains, not at the office/theater/… caught without an umbrella the performer is going to be unsheltered for some period of time the more waterproof their clothing, the gentler the rain, and the warmer the air, the longer that time period the performer will not be wet anyway (e.g., swimming) the rain is annoying -- but merely annoying. Thus: not ammonia rain on Venus, radioactive post-apocalyptic rain, biblical (Noahs-ark-sized, or frogs/blood as rained on Pharaoh) the performer is not a hydrophobic person, gingerbread man, etc., and not a hydrophilic person, someone dying of thirst, etc.

March 14, Dimensions of Ontol. Contexts Anthropacity / Lets Time GeoLocation TypeOfPlace TypeOfTime Culture Sophistication/Security Topic Granularity Modality/Disposition/Epistemology Argument-Preference Justification

March 14, How we evaluate proposed dimensions Criteria: Do they separate out mutually-irrelevant (and esp. mutually-incompatible) portions of the KB? Is it easy for Cyc to mechanically compute the overlap or disjointness of regions of n-dim. context-space? Cognitive assonance: Do they (esp. their extrema) correspond to familiar real-world notions? Using them, is it empirically faster to enter assertions? Using them, is it empirically faster to do inference?

March 14, Context feature: Time The piece of time (the 1920s, the first five years after WWII, the Pleistocene Era) in which a contexts assertions hold. Useful because: Facts about very distant time periods are often mutually irrelevant; if stated tersely, they are often inconsistent. Inefficient to temporally qualify each assertion individually. In many reasoning contexts, causes precede effects by a small amount of time.

March 14, Context feature: Spatial Location The piece of space (Lebanon, my bloodstream, the Southern Hemisphere, Mike Ditkas backyard) in which a contexts assertions hold. Useful because: Facts about very distant locations are often mutually irrelevant; if stated tersely, they are often inconsistent. Inefficient to spatially qualify each assertion individually. In many reasoning contexts, interacting objects and events are usually spatially proximate.

March 14, Context feature: Culture The cultural point of view assumed by the assertions in a context. This dimension has many subdimensions, e.g.: political culture, sexual culture, sexual orientation culture, age culture, generation culture, religious culture, ancestral culture, geo-political culture, regional culture, region-type culture, legal culture, and more

March 14, Culture, ctnd Useful because: In many reasoning contexts, some cultural perspective (or set of perspectives) is assumed, and other perspectives are not relevant. Accuracy of applications which involve reasoning about agents intent and expectations requires sensitivity to variations in cultural context. Many good ways to infer the cultural POV of author. This dimension is quite possibly the most difficult of all: very complex, very hard to separate fact from preconception. Very hard to maintain objectivity and not antagonize everyone.

March 14, Context feature: Sophistication The level of information, education, intelligence or other capacity for knowledge assumed by the assertions in this context. What capacity for knowledge would a person need in order to: –Understand the assertions, to learn them in this form, –Recognize them as true, once they are hinted at or stated, –Already be familiar with the assertions, at least theoretically, –Have already deeply assimilated the content of the assertions? Useful for dialogue, and collaborative planning applications.

March 14, Context feature: Granularity The level of coarseness assumed by assertions in a context. This dimension has many potential subdimensions: –size of objects, duration of events, parts, subevents, suborganizations, specificity or abstraction of classes (collections), relationships, measurements –Ex: Newtonian vs Relativistic vs Quantum physics Useful because: answers often vary drastically depending on the granularity desired.

March 14, Evaluating proposed dimensions Criteria: Do they separate out mutually-irrelevant (and esp. mutually-incompatible) portions of the KB? Is it easy for Cyc to mechanically compute the overlap or disjointness of regions of n-dim. context-space? Cognitive assonance: Do they (esp. their extrema) correspond to familiar real-world notions? Using them, is it empirically faster to enter assertions? Using them, is it empirically faster to do inference?

March 14, Mathematical Factoring of MetaData Dimensions UnitedStatesIn1985Context: Ronald Reagan is president. PennsylvaniaIn1985Context: Dick Thornburgh is governor. LehighCountyInFebruary1985Context: Dick Thornburgh is governor and Ronald Reagan is president. This inference depends on the time, space, and respective granularities of the contexts. There are at least 900,000 doctors. Dick Thornburgh is governor and there are at least 900,000 doctors.

March 14, Time Indices and Granularities Doug is talking. But not: Doug is talking, at 2:11:15, on 5/4/04. Doug is talking, at 1:45 to 2:45, on 5/4/04. Doug is talking, at 2:05 to 2:40, on 5/4/04. Therefore:

March 14, Qa can be inferred at t 3, with granularity, if t 3 subsumes some instance of the granularity of Pa, and some instance of the granularity of (implies Px Qx), and is at least as big as both of these granularities. If t 4 subsumes some instance of the granularity of Pa, 1, and some instance of the granularity of (implies P Q), 2, then Qa is inferred at t 4, with each granularity in the set of minimal upper bounds of ( 1 2 ). t3t3 Qa ? Backward Inference (implies Px Qx) t1t1 Pa t2t2 Calculi for deciding (dimension by dimension) in what context we can assert a logical conclusion Pa t2t2 t1t1 t4t4

March 14, SouthWestAsiaDataContext June1985LebanonDataContext (MtSpace LebanonDataContext (TimeIndex: June, 1985) (TemporalGranularity: Month) (SpatialGranularity: Governorate)) (MtSpace SouthWestAsiaDataContext (TimeIndex: 1985) (TemporalGranularity: Day) (SpatialGranularity: SquareMile)) Inferring Context Subsumption (genlMt ) The content of this context Subsumes the content of this context

March 14, Getting back to: No need for separate ontologies Declarative assertions that map them to Cyc And thereby map between them (using Cyc as an interlingua) Create a context or Mt for each external ontology O; eventually, there is enough in and about each such Mt that it almost subsumes O. Almost because O might be optimized in some way repr./algorithmically (e.g., a DB)

March 14, Ontologies/Schemata Cyc is Mapped to

March 14, "(synonymousExternalConcept TERM SOURCE STRING) means that the CycL expression TERM is synonymous with at least one of the interpretations of STRING in the external data source SOURCE." (synonymousExternalConcept InnerEar MeSH-Information1997 "Labyrinth | A ") (synonymousExternalConcept Temperature CNLPOntology "temp") (synonymousExternalConcept Concerto WordNet-Version2_0 "N ") (synonymousExternalConcept PowerGenerationComplex-Nuclear LSCOMObjectAndSituationOntology power plant (nuclear) )

March 14, "(overlappingExternalConcept TERM SOURCE STRING) means that the CycL expression TERM overlaps semantically with at least one of the interpretations of STRING in the external data source SOURCE." (overlappingExternalConcept TextualPCW CNLPOntology "document") (overlappingExternalConcept defectors HorusPersonOrganizationOntology "splinterFromOrg") (overlappingExternalConcept SpleniusCapitis MeSH-Information1997 "Neck Muscles | A ")

March 14, "(codeMapping MAP CODE DENOTATION) specifies one mapping for the reified mapping MAP. When a table uses MAP to interpret some field, the value CODE in that field will be interpreted as DENOTATION." (codeMapping FACC-FeatureType-CMLS "BH100" Moat) (codeMapping NGA-FeatureType-CMLS "PLN" (GroupFn Plain-Topographical)) (codeMapping FACC-FeatureType-CMLS "GB020" AircraftArrestingGear) (ForAll ?x (fieldDecoding USGS-GNIS-LS ?x (TheFieldCalled population) (numberOfInhabitants (TheReferentOfTheRow USGS-GNIS) ?x)))

March 14, To first order: I agree with the communiqué (apple pie) How/why I was forced into this field Upper ontology mostly just impacts efficiency –Of the vocabulary (lower ontology): fewer terms, simpler terms –Of the axioms: fewer, terser, less ambiguous –Of the various types of cross-ontology mapping axioms What needs to be shared No correct UO; and yet no need for separate indep. UOs –Have contexts (microtheories) and an ist relation Ontologies at that point seem to be normal 1 st -class objects As with any important region of the ontology, facet that –12 useful (categories of) facets or dimensions of ontology-space Just a few remarks about OpenCyc and ResearchCyc Upper Ontology Symposium

March 14, Cyc Knowledge Base Thing Intangible Thing Intangible Thing Individual Temporal Thing Temporal Thing Spatial Thing Spatial Thing Partially Tangible Thing Partially Tangible Thing Paths Sets Relations Sets Relations Logic Math Logic Math Human Artifacts Human Artifacts Social Relations, Culture Social Relations, Culture Human Anatomy & Physiology Human Anatomy & Physiology Emotion Perception Belief Emotion Perception Belief Human Behavior & Actions Human Behavior & Actions Products Devices Products Devices Conceptual Works Conceptual Works Vehicles Buildings Weapons Vehicles Buildings Weapons Mechanical & Electrical Devices Mechanical & Electrical Devices Software Literature Works of Art Software Literature Works of Art Language Agent Organizations Agent Organizations Organizational Actions Organizational Actions Organizational Plans Organizational Plans Types of Organizations Types of Organizations Human Organizations Human Organizations Nations Governments Geo-Politics Nations Governments Geo-Politics Business, Military Organizations Business, Military Organizations Law Business & Commerce Business & Commerce Politics Warfare Politics Warfare Professions Occupations Professions Occupations Purchasing Shopping Purchasing Shopping Travel Communication Travel Communication Transportation & Logistics Transportation & Logistics Social Activities Social Activities Everyday Living Everyday Living Sports Recreation Entertainment Sports Recreation Entertainment Artifacts Movement State Change Dynamics State Change Dynamics Materials Parts Statics Materials Parts Statics Physical Agents Physical Agents Borders Geometry Borders Geometry Events Scripts Events Scripts Spatial Paths Spatial Paths Actors Actions Actors Actions Plans Goals Plans Goals Time Agents Space Physical Objects Physical Objects Human Beings Human Beings Organ- ization Organ- ization Human Activities Human Activities Living Things Living Things Social Behavior Social Behavior Life Forms Life Forms Animals Plants Ecology Natural Geography Natural Geography Earth & Solar System Earth & Solar System Political Geography Political Geography Weather General Knowledge about Various Domains Cyc contains: 15,000Predicates 68,000Collections 300,000Concepts 3,200,000Assertions Represented in: First Order Logic Higher Order Logic Context Logic Micro-theories Specific data, facts, and observations

March 14, Cyc contains: 15,000Predicates 68,000Collections 300,000Concepts 3,200,000Assertions OpenCyc + 1M taxon./mereol. Axioms + inference engines, interfaces ResearchCyc All of that + whole Cyc KB

March 14, Temporal Relations 37 Relations Between Temporal Things #$temporalBoundsIntersect #$temporallyIntersects #$startsAfterStartingOf #$endsAfterEndingOf #$startingDate #$temporallyContains #$temporallyCooriginating #$temporalBoundsContain #$temporalBoundsIdentical #$startsDuring #$overlapsStart #$startingPoint #$simultaneousWith #$after

March 14, Senses of Part #$parts #$intangibleParts #$subInformation #$subEvents #$physicalDecompositions #$physicalPortions #$physicalParts #$externalParts #$internalParts #$anatomicalParts #$constituents #$functionalPart

March 14, Senses of In Can the inner object leave by passing between members of the outer group? –Yes -- Try #$in-Among

March 14, Senses of In Does part of the inner object stick out of the container? –None of it. -- Try #$in-ContCompletely –Yes -- Try #$in-ContPartially –If the container were turned around could the contained object fall out? No -- Try #$in-ContClosed Yes -- Try #$in-ContOpen

March 14, Senses of In Is it attached to the inside of the outer object? –Yes -- Try #$connectedToInside Can it be removed, if enough force is used, without damaging either object? –Yes -- Try #$in-Snugly or #$screwedIn Does the inner object stick into the outer object? Yes -- Try #$sticksInto

March 14, Event Types #$PhysicalStateChangeEvent #$TemperatureChangingProcess #$BiologicalDevelopmentEvent #$ShapeChangeEvent #$MovementEvent #$ChangingDeviceState #$GivingSomething #$DiscoveryEvent #$Cracking #$Carving #$Buying #$Thinking #$Mixing #$Singing #$CuttingNails #$PumpingFluid 11,000 more

March 14, #$performedBy #$causes-EventEvent #$objectPlaced #$objectOfStateChange #$outputsCreated #$inputsDestroyed #$assistingAgent #$beneficiary #$fromLocation #$toLocation #$deviceUsed #$driverActor #$damages #$vehicle #$providerOfMotiveForce #$transportees Relations Between an Event and its Participants Over 400 more.

March 14, Propositional Attitudes Relations Between Agents and Propositions #$goals #$intends #$desires #$hopes #$expects #$beliefs #$opinions #$knows #$rememberedProp #$perceivesThat #$seesThat #$tastesThat

March 14, Devices Over 4000 Specializations of #$PhysicalDevice – #$ClothesWasher – #$NuclearAircraftCarrier Vocabulary for Describing Device Functions – #$primaryFunction-DeviceType Device Specific Predicates #$gunCaliber #$speedOf Device States (40+) #$DeviceOn #$CockedState

March 14, Constant : Coke-TheWord isa : EnglishWord Mt : EnglishMt singular : cokepnSingular : Coke massNumber : cokepnMassNumber : Coke (denotation Coke-TheWord ProperCountNoun 0 (ServingFn CocaCola)) (denotation Coke-TheWord ProperMassNoun 0 CocaCola) (denotation Coke-TheWord MassNoun 0 Cocaine-Powder) (denotation Coke-TheWord MassNoun 2 ColaSoftDrink) (denotation Coke-TheWord SimpleNoun 0 (ServingFn ColaSoftDrink) Lexical Entry Example: Coke SLANG

March 14, Lexical Entry Example: Eat ( verbSemTrans Eat-TheWord 0 TransitiveNPCompFrame (and (isa :ACTION EatingEvent) (performedBy :ACTION :SUBJECT) (inputsDestroyed :ACTION :OBJECT))) Constant: Eat-TheWord isa: EnglishWord Mt: EnglishMt infinitive: eat pastTense: ate perfect: eaten agentive-Sg: eater (subcatFrame Eat-TheWord Verb 0 TransitiveNPCompFrame)

Cyc NL Lexicon English Words18,737 Syntactic Frame Links13,922 Single-word Denotation Mappings25,999 Multi-word Phrase Denotation Mappings43,508 Verbal Semantic Frame Links3,517 Noun Semantic Frame Links2,396 Pragmatic Assertions1,232 Names ( Includes chemical symbols, person/place/organizatioin names, acronyms, etc.) 171,093 Predicate-based Phrasal Links ( genTemplates for paraphrase) 10,327

March 14, Geospatial Classes 1100 Atomic types, 338 functionally specified ones CrabFishery LakeBed MonsoonForest MudFlat USCS-Code-CL Glacier Ridge Butte Cave MinedArea PostalCodeRegion Prefecture TownSquare Quarry Atoll Continent TrueContinent (FieldFn OliveTree) (CityInCountryFn Cuba) Protectorate IndependentCountry Colony SchoolDistrict Monarchy

March 14, Predicates of Geospatial Entities Over 500 terrainType maximumDepth cloudCeiling importsFrom regionalPastimes populationDensity trafficableForVehicle freightRailTrafficRate internetCountryCode hasClimateType languagesSpokenHere highestPointInRegion waterAreaOfRegion canopyClosureOfRegion

March 14, OpenCyc Open Source release of: 300k-term Cyc Ontology + 1M Simple Relns Inference Engines, NL Parser/Generator, Ontol Mappers ResearchCyc All of Cyc (free, for R&D purposes) FACTory Free online match game to check/add to the ontology and, more generally, to the Cyc KB

March 14, ,000 OpenCyc Users/Contributors, 73 Active ResearchCyc User Groups: Xerox PARC Daxtron Labs Lockheed Martin ATLD Government Government-related Commercial Houston VA Medical Center Air Force Rome Labs Institute for the Study Of Accelerating Change U of Maryland Language Computer Corporation NTT Communications Science Laboratories (Japan) Northwestern U Stanford NLP Dept. ANSER, Inc. LBJ School of Public Affairs Fraunhofer Institute U of Illinois Urbana-Champaign New Mexico Highlands Univ. Harvard U Linkoping U (Sweden) Radboud U (Netherlands) Tokyo Inst. of Technology Terra Incognita University Microfabrica, Inc. U of Stuttgart NPOs MIT Media Lab Witan International U of Pennsylvania SRI 21 st Century Technologies U of Minnesota Stones Throw Technologies ISI Trimtab Consulting U of Hawaii Rensselaer AI and Reasoning Lab TNO-DMV (Netherlands) Sapio Systems (Denmark) U of Toronto Knowledge Media Institute, Open University Austin Info Systems

March 14, To first order: I agree with the communiqué (apple pie) How/why I was forced into this field Upper ontology mostly just impacts efficiency –Of the vocabulary (lower ontology): fewer terms, simpler terms –Of the axioms: fewer, terser, less ambiguous –Of the various types of cross-ontology mapping axioms What needs to be shared No correct UO; and yet no need for separate indep. UOs –Have contexts (microtheories) and an ist relation Ontologies at that point seem to be normal 1 st -class objects As with any important region of the ontology, facet that –12 useful (categories of) facets or dimensions of ontology-space Just a few remarks about OpenCyc and ResearchCyc Upper Ontology Symposium

March 14, If theres a few more minutes… Parting sermon: resist temptation, brothers and sisters!

March 14, Eschew the 5 pitfalls (ways to cut ontological corners and end up with something that only appears to work) Ignorance-based: Have a small theory size (#terms, #instances, #rules) Static KB (can be massively tuned, optimized, cached, etc. ahead of time) Simple assertions (e.g., SAT constraints; propositional calculus; Horn;…) One global context (no contradictions, limited domain, simplified world) Dont do all the bookkeeping and forward inference required for justification maintenance (or, equivalently, dont ever have truth maintenance turned on)

March 14, Eschew the 5 pitfalls (ways to cut ontological corners and end up with something that only appears to work) Ignorance-based: Have a small theory size (#terms, #instances, #rules) Static KB (can be massively tuned, optimized, cached, etc. ahead of time) Simple assertions (e.g., SAT constraints; propositional calculus; Horn;…) One global context (no contradictions, limited domain, simplified world) Dont do all the bookkeeping and forward inference required for justification maintenance (or, equivalently, dont ever have truth maintenance turned on) As with pharmaceuticals, what is toxic in one dosage is beneficial in a lesser dosage. contexts lead to locally-consistent locally-small theories (faster inference/KE) often some (sub)problems can be represented/solved in a simpler repr. as a temporary placeholder (cf. Woods incremental simulation LUNAR)

March 14, Besides Those 5 Major Pitfalls there are many minor ones, related to taste and efficiency, not correctness Constant names should be unambiguous (Coral-Color Coral-Reef Coral-Polyp) 99.9…% of the meaning is in the assertions about the terms, not in the names E.g., if Rthagide-disjaks and Gracinimumples are only known to be Kitchen-Appliances

March 14, Besides Those 5 Major Pitfalls there are many minor ones, related to taste and efficiency, not correctness Constant names should be unambiguous (Coral-Color Coral-Reef Coral-Polyp) 99.9…% of the meaning is in the assertions about the terms, not in the names E.g., if Garbage-disposals and Microwave-ovens are only known to be Kitchen-Appliances

March 14, Besides Those 5 Major Pitfalls there are many minor ones, related to taste and efficiency, not correctness Constant names should be unambiguous (Coral-Color Coral-Reef Coral-Polyp) 99.9…% of the meaning is in the assertions about the terms, not in the names E.g., if Garbage-disposals and Microwave-ovens are only known to be Kitchen-Appliances This applies to variable names, not just constant names (implies (in-ContOpen ?BOAT ?RIVER) (in-Floating ?BOAT ?RIVER))

March 14, Besides Those 5 Major Pitfalls there are many minor ones, related to taste and efficiency, not correctness Constant names should be unambiguous (Coral-Color Coral-Reef Coral-Polyp) 99.9…% of the meaning is in the assertions about the terms, not in the names E.g., if Garbage-disposals and Microwave-ovens are only known to be Kitchen-Appliances This applies to variable names, not just constant names (implies (and (in-ContOpen ?BOAT ?RIVER) (isa ?BOAT Boat) (isa ?RIVER River)) (in-Floating ?BOAT ?RIVER))

March 14, Besides Those 5 Major Pitfalls there are many minor ones, related to taste and efficiency, not correctness Constant names should be unambiguous (Coral-Color Coral-Reef Coral-Polyp) 99.9…% of the meaning is in the assertions about the terms, not in the names E.g., if Garbage-disposals and Microwave-ovens are only known to be Kitchen-Appliances This applies to variable names, not just constant names (implies (and (in-ContOpen ?BT ?RVR) (isa ?BT Boat) (isa ?RVR River)) (in-Floating ?BT ?RVR))

March 14, Minor Pitfall: Over-generalization Every organism has a head Every person has some building or part of a building as their home All people speak some language Replace it by a few rules for vertebrates, insects,… State exceptions (infants, coma victims,…) Assert it only in some context(s) [culture, time period,…]

March 14, Minor Pitfall: Over-specialization Every person was born later than his mother ( implies (and (isa ?MST Mast-Device) (physicalParts ?BOT ?MST) (isa ?BOT Sailboat)) (rigidityOfObject ?MST Rigid)) animal & ancestor; created thing & creator; cause/effect (relationAllInstance rigidityOfObject Mast-Device Rigid)

March 14, Minor Pitfall: Independent Assertions Glommed Together Sailboats have masts and hulls. (implies (isa ?BOT Sailboat) (thereExists ?MST (thereExists ?HUL (and (isa ?MST Mast-Device) (isa ?HUL Hull-BoatPart) (physicalParts ?BOT ?MST) (physicalParts ?BOT ?HUL)))))

March 14, (implies (isa ?BOT Sailboat) (thereExists ?MST (thereExists ?HUL (and (isa ?MST Mast-Device) (isa ?HUL Hull-BoatPart) (physicalParts ?BOT ?MST) (physicalParts ?BOT ?HUL))))) Minor Pitfall: Independent Assertions Glommed Together (implies (isa ?BOT Sailboat) (thereExists ?MST (and (isa ?MST Mast-Device) (physicalParts ?BOT ?MST)))) (implies (isa ?BOT Sailboat) (thereExists ?H (and (isa ?MST Hull-Boat) (physicalParts ?BOT ?H)))) Boat (relationAllExists physicalParts Sailboat Mast-Device) (relationAllExists physicalParts Boat Hull-BoatPart) Sailboats have masts and hulls.

March 14, Minor Pitfall: Independent Assertions Glommed Together (relationAllExists physicalParts Sailboat Mast-Device) (relationAllExists physicalParts Boat Hull-BoatPart) Sailboats have masts. Boats have hulls.

March 14, (marriedIn ) Events are rich (no limit to the number of args) (groom Wedding0947 JoeSmith) (bride Wedding0947 JaneDoe) (dateOfEvent Wedding0947 (DayFn 13 (MonthFn May (YearFn 1999)))) Minor Pitfall: Predicates that lump independent properties together (non)Davidsonian choice: impact on link extraction/recognition Not all situations are rich! E.g., (nextInteger 87 88)

March 14, Problematic: (teamLineup DallasCowboys-1998 TroyAikman EmmittSmith MichaelErvin...) Instead, reify the positions: (positionOfPersonInOrganization TroyAikman DallasCowboys-1998 Quarterback) (positionOfPersonInOrganization EmmittSmith DallasCowboys-1998 RunningBack)... Every football team has >=1… The quarterbacks role in a play is … Minor Pitfall: Predicates that hide concepts in argument order

March 14, TheGovernmentOfFrance, TheGovernmentOfFranceIn1997, TheGovernmentOfSpain, TheGovernmentOfSpainIn1997,… Kilometer, Kilogram, Kilocalorie… Minor Pitfall: Over-reification Functions, such as: (GovernmentFn France) Contexts, such as: (DuringMt 1997 (GovernmentFn France)) (unitMultiplicationFactor (Kilo ?UNIT) ?UNIT 1000) (unitMultiplicationFactor (Kilo Meter) Meter 1000) (resultIsa (Kilo Meter) Distance) ((Kilo Meter) 8.3)

March 14, Minor Pitfall: Manual NL Generation Formal axioms about, e.g., employees, UN Informal English comment describing the intended meaning, scope, etc., of the term Danger: bugs creep in: mismatches between what the English and the axioms say about the employees relation, or about the UN. Solution (also faster): auto. generate the NL

March 14, End of sermon End of Talk#1