IBM T.J. Watson Research Center Foundations of Ontological Analysis Chris Welty, Vassar College

IBM T.J. Watson Research Center 2 What is Ontology? A discipline of Philosophy –Meta-physics dates back to Artistotle –Ontology dates back to 17th century –The science of what is Borrowed by AI community –McCarthy (1980) calls for “a list of things that exist” Evolution of meaning –Now refers to domain modeling, conceptual modeling, knowledge engineering, etc.

IBM T.J. Watson Research Center 3 What is an Ontology? complexity a catalog a set of general logical constraints a glossary a set of text files a thesaurus a collection of taxonomies a collection of frames with automated reasoning without automated reasoning

IBM T.J. Watson Research Center 4 Why Ontology? “Semantic Interoperability” –Generalized database integration –Virtual Enterprises –e-commerce Information Retrieval –Surface techniques hit barrier –Query answering over document sets –Natural Language Processing

IBM T.J. Watson Research Center 5 Need more knowledge about what the user wants “Can the user please be more specific?” Search for “Washington” (the person) –Google: 26,000,000 hits –45th entry is the first relevant –Noise: places Search for “George Washington” –Google: 2,200,00 hits –3rd entry is relevant –Noise: institutions, other people, places

IBM T.J. Watson Research Center 6 Solution Knowledge Domain Knowledge –Person George Washington George Washington Carver –Place Washington, D.C. –Artifact George Washington Bridge –Organization George Washington University Semantic Markup of question and corpora –What Washington are you talking about?

IBM T.J. Watson Research Center 7 Need more knowledge about the possible answers “ Can the user please put more of the answer in the question?” Search for “Artificial Intelligence Research” –Misses subfields of the general field –Misses references to “AI” and “Machine Intelligence” (synonyms) –Noise: non-research pages, other fields, Mensa types

IBM T.J. Watson Research Center 8 Solution Knowledge Domain Knowledge –Sub-fields (of AI) Knowledge Representation Machine Vision etc. Neural networks –Synonyms (for AI) Artificial Intelligence Machine Intelligence Query Expansion –Add disjuncted “general terms” to search –Add disjuncted “synonyms” to search Semantic Markup of question and corpora –Add “general terms” (categories) –Add “synonyms”

IBM T.J. Watson Research Center 9 Allies of my enemies are my…? What are all the enemies of Iraq in the Persian Gulf according to the CIA World Fact Book? –“Persian Gulf” appears as a region and a body of water. –Misses: allies of enemies –Noise: countries with interests in the Persian Gulf, companies, ships, oil platforms

IBM T.J. Watson Research Center 10 Solution: Knowledge + Reasoning (Cycorp/SRI HPKB) Some axioms –Enemy of a country is a country –Ally of an enemy is an enemy –Enemy is reflexive –Countries are located in regions Reformulate –  (country   located-in. (region  name = “Persian Gulf”)  enemy. (country  name = “Iraq”))

IBM T.J. Watson Research Center 11 Solution Theme: More Knowledge Ontologies - at least part of the solution “more semantics”, “richer knowledge” … ontologies Idealized view –Knowledge-enabled search engines act as virtual librarians Determine what you “really mean” Discover relevant sources Find what you “really want” Requires common knowledge on all ends –Semantic linkage between questioning agent, answering agent and knowledge sources Hence the “Semantic Web”

IBM T.J. Watson Research Center 12 Key Challenges Must build/design, analyze/evaluate, maintain/extend, and integrate/reconcile ontologies Little guidance on how to do this –In spite of the pursuit of many syntactic standards –Where do we start when building an ontology? –What criteria do we use to evaluate ontologies? –How are ontologies extended? –How are different ontological choices reconciled? Ontological Modeling and Analysis –Does your model mean what you intend? –Will it produce the right answers?

IBM T.J. Watson Research Center 13 Contributions Methodology to help analyze & build consistent ontologies –Formal foundation of ontological analysis –Meta-properties for analysis –“Upper Level” distinctions Standard set of upper-level concepts Standardizing semantics of ontological relations Common ontological modeling pitfalls –Misuse of intended semantics Specific recent work focused on clarifying the subsumption (is-a, subclass) relation

IBM T.J. Watson Research Center 14 Upper Level Particulars –Concrete Location, event, object, substance, … –Abstract information, story, collection, … Universals –Property (Class) –Relation Subsumption (subclass), instantiation, constitution, composition (part)

IBM T.J. Watson Research Center 15 Subsumption The most pervasive relationship in ontologies –Influence of taxonomies and OO AKA: Is-a, a-kind-of, specialization-of, subclass (Brachman, 1983) –“horse is a mammal” Capitalizes on general knowledge –Helps deal with complexity, structure –Reduces requirement to acquire and represent redundant specifics What does it mean?  x  (x)   (x) Every instance of the subclass is necessarily an instance of the superclass

IBM T.J. Watson Research Center 16 Overloading Subsumption Common modeling pitfalls Instantiation Constitution Composition Disjunction Polysemy

IBM T.J. Watson Research Center 17 Instantiation (1) T21 My ThinkPad (s# xx123) ThinkPad Model Ooops… Question: What ThinkPad models do you sell? Answer should NOT include My ThinkPad -- nor yours. Does this ontology mean that My ThinkPad is a ThinkPad Model?

IBM T.J. Watson Research Center 18 Instantiation (2) T Series My ThinkPad (s# xx123) ThinkPad Model model Notebook Computer T 21

IBM T.J. Watson Research Center 19 Composition (1) Memory Disk Drive Computer Question: What Computers do you sell? Answer should NOT include Disk Drives or Memory. Micro Drive

IBM T.J. Watson Research Center 20 Composition (2) Memory Disk Drive Computer Micro Drive part-of

IBM T.J. Watson Research Center 21 Disjunction (1) Memory Disk Drive Computer Micro Drive has-part Computer Part Flashcard-110 Camera-15 has-part Unintended model: flashcard-110 is a computer-part

IBM T.J. Watson Research Center 22 Disjunction (2) Computer has-part Disk Drive  Memory  …

IBM T.J. Watson Research Center 23 Polysemy (1) ( Mikrokosmos) Abstract Entity Physical Object Book Question: How many books do you have on Hemingway? Answer: 5,000 …..

IBM T.J. Watson Research Center 24 Polysemy (2) (WordNet) Abstract Entity Physical Object Book Sense 1 Book Sense 2 ….. Biography of Hemingway

IBM T.J. Watson Research Center 25 Constitution (1) (WordNet) Amount of Matter Physical Object Entity Computer Clay Metal Question: What types of matter will conduct electricity? Answer should NOT include computers.

IBM T.J. Watson Research Center 26 Constitution (2) Amount of Matter Physical Object Entity Computer ClayMetal constituted

IBM T.J. Watson Research Center 27 Technical Conclusions Subsumption is an overloaded relation –Influence of OO –Force fit of simple taxonomic structures –Leads to misuse of is-a semantics Ontological Analysis –A collection of well-defined knowledge structuring relations –Methodology for their consistent application Meta-Properties for ontological relations Provide basis for disciplined ontological analysis

IBM T.J. Watson Research Center 28 Applications of Methodology Ontologyworks Ontoweb TICCA, WedODE, Galen, … Strong interest from and participation in –Semantic web (w3c) –IEEE SUO –Wordnet –Lexical resources

IBM T.J. Watson Research Center 29 New opportunities Principled and rigorous upper level –All extensions are affected by a poor upper-level –Restructuring of WordNet nouns –Restructuring of CYC upper level –Softer lower levels Trade off speed and flexibility of statistical approaches ML and IR techniques for ontology seeding Query answering –confidence levels –explanations

IBM T.J. Watson Research Center Foundations of Ontological Analysis Chris Welty, Vassar College

IBM T.J. Watson Research Center 31 Ontological Properties Identity –How are instances of a class distinguished from each other Unity –How are all the parts of an instance isolated Essence –Can a property change over time Dependence –Can an entity exist without some others

IBM T.J. Watson Research Center 32 Example - Identity Is time-interval a subclass of time-duration? –Initial answer: yes IC for time-duration –Same-length IC for time-interval –Same start & end time-duration time-interval occurrent

IBM T.J. Watson Research Center 33 Example - Identity time-duration time-interval 3-4 PM Weds. 2-3 PM Tues. One hour occurrent

IBM T.J. Watson Research Center 34 Guidelines Examples of how to use meta properties –Automated system for checking constraints Formalizing and standardizing semantics of ontology structuring relations Examples of how to use upper level –Cataloguing common pitfalls –Early work has focused on subsumption

IBM T.J. Watson Research Center 35 Meta Properties Properties of properties Fully formalized Carries identity criteria Carries unity criteria Rigid Dependent

IBM T.J. Watson Research Center 36 Example - Rigidity

IBM T.J. Watson Research Center 37 Approach Draw fundamental notions from Formal Ontology Establish a set of useful meta-properties, based on behavior wrt above notions Explore the way these meta-properties combine to form relevant property kinds Explore the taxonomic constraints imposed by these property kinds.

IBM T.J. Watson Research Center 38 Basic Philosophical Notions ( taken from Formal Ontology) Essence Identity Unity Dependence

IBM T.J. Watson Research Center 39 Essence and Rigidity Certain entities have essential properties. –Hammers must be hard. –John must be a person. Certain properties are essential to all their instances (compare being a person with being hard). These properties are rigid - if an entity is ever an instance of a rigid property, it must always be.

IBM T.J. Watson Research Center 40 Formal Rigidity    is rigid (+R):  x  (x)   (x) –e.g. Person, Apple   is non-rigid (-R):  x  (x)  ¬  (x) –e.g. Red, Male    is anti-rigid (~R):  x  (x)  ¬  (x) –e.g. Student, Agent

IBM T.J. Watson Research Center 41 Rigidity Constraint +R  ~R Why?  x P(x)  Q(x) Q ~R P +R O10

IBM T.J. Watson Research Center 42 Identity and Unity Identity: is this my dog? Unity: is the collar part of my dog?

IBM T.J. Watson Research Center 43 Identity criteria Classical formulation:  (x)   (y)  (  (x,y)  x = y) Generalization:  (x,t)   (y,t’)  (  (x,y,t,t’)  x = y) (synchronic: t = t’ ; diachronic: t ≠ t’) In most cases,  is based on the sameness of certain characteristic features:  (x,y, t,t’) =  z (  (x,z,t)   (y,z,t’))

IBM T.J. Watson Research Center 44 A Stronger Notion: Global ICs Local IC:  (x,t)   (y,t’)  (  (x,y,t,t’)  x = y) Global IC (rigid properties only):  (x,t)  (  (y,t’)   (x,y,t,t’)  x = y)

IBM T.J. Watson Research Center 45 Identity Conditions along Taxonomies Adding ICs: –Polygon: same edges, same angles Triangle: two edges, one angle –Equilateral triangle: one edge Just inheriting ICs: –Person Student

IBM T.J. Watson Research Center 46 Identity meta-properties Supplying (global) identity (+O) –Having some “own” IC that doesn’t hold for a subsuming property Carrying (global) identity (+I) –Having an IC (either own or inherited) Not carrying (global) identity (-I)

IBM T.J. Watson Research Center 47 Identity Disjointness Constraint Properties with incompatible ICs are disjoint Besides being used for recognizing sortals, ICs impose constraints on them, making their ontological nature explicit: Examples: sets vs. ordered sets amounts of matter vs. assemblies

IBM T.J. Watson Research Center 48 Unity Criteria An object x is a whole under  iff  is an equivalence relation that binds together all the parts of x, such that P(y,x)  (P(z,x)   y,z)) but not  y,z)   x(P(y,x)  P(z,x)) P is the part-of relation  can be seen as a generalized indirect connection

IBM T.J. Watson Research Center 49 Unity Meta-Properties If all instances of a property  are wholes under the same relation  carries unity (+U) When at least one instance of  is not a whole, or when two instances of  are wholes under different relations,  does not carry unity (-U) When no instance of  is a whole,  carries anti-unity (~U)

IBM T.J. Watson Research Center 50 Unity Disjointness Constraint Properties with incompatible UCs are disjoint +U  ~U

IBM T.J. Watson Research Center 51 Property Dependence Does a property holding for x depend on something else besides x? (property dependence) –P(x)   y Q(y) –y should not be a part of x Example: Student/Teacher, customer/vendor

IBM T.J. Watson Research Center 52 Basic Property Kinds Table

IBM T.J. Watson Research Center 53 Sortals, categories, and other properties Sortals (horse, triangle, amount of matter, person, student...) –Carry identity –Usually correspond to nouns –High organizational utility –Main subclasses: types and roles Categories (universal, particular, event, substance...) –No identity –Useful generalizations for sortals –Characterized by a set of (only necessary) formal properties –Good organizational utility Other non-sortals (red, big, decomposable, eatable, dependent, singular...) –No identity –Correspond to adjectives –Span across different sortals –Limited organizational utility (but high semantic value)

IBM T.J. Watson Research Center 54 A formal ontology of properties Property Non-sortal -I Role ~R+D Sortal +I Formal Role Attribution -R-D Category +R Mixin -D Type +O Quasi-type -O Non-rigid -R Rigid +R Material role Anti-rigid ~R Phased sortal -D +L

IBM T.J. Watson Research Center 55 The Backbone Taxonomy Assumption: no entity without identity Since identity is supplied by types, every entity must instantiate a type The taxonomy of types spans the whole domain Together with categories, types form the backbone taxonomy, which represents the invariant structure of a domain (rigid properties spanning the whole domain)

IBM T.J. Watson Research Center 56 Taxonomic Constraints +R  ~R -I  +I -U  +U +U  ~U -D  +D Incompatible IC’s are disjoint Incompatible UC’s are disjoint Categories subsume everything Roles can’t subsume types

IBM T.J. Watson Research Center 57 Idealized view of an ontology

IBM T.J. Watson Research Center 58 An extended example

IBM T.J. Watson Research Center 59 Dealing with Ontological Relativism Deciding about the meta-properties carried by a given property… Is up to YOU ! But a common agreement must be achieved about the formal meaning (and practical utility) of meta-properties

IBM T.J. Watson Research Center 60 Property Analysis Entity, Location Entity –Everything is an entity –-I-U-D+R –Category Location –A generalized region of space. –+O: by its parts (mereologically extensional). –~U: no way to isolate a location –-D+R –Type

IBM T.J. Watson Research Center 61 Property Analysis Amount of Matter, Red Amount of Matter –unstructured /scattered “stuff” as lumps of clay or some bricks –+O: mereologically extensional –~U: intrinsically no unity –-D+R –Type Red –Really Red-thing, the set of all red-colored entities –-I-U-D-R –Formal Attribution

IBM T.J. Watson Research Center 62 Property Analysis Agent, Group Agent –An entity playing a part in some event –-I-U: no universal IC/UC –+D: on the event/action participating in –~R: no instance is necessarily an agent –Formal role Group –An unstructured collection of wholes –+O: same-members –~U: unstructured, no unity. – -D+R –Type

IBM T.J. Watson Research Center 63 Property Analysis Physical Object, Living Being Physical Object –Isolated material objects. –+O: same spatial location (only synchronic, no common diachronic IC). –+U: Topological –-D+R –Type Living Being –+O: same-DNA (only nec.) –+U: biological unity –-D+R –Type

IBM T.J. Watson Research Center 64 Property Analysis Food, Animal Food –+I-O~U: amt. of matter –+D: something that eats it. –~R: being food is not necessary... –Material Role Animal –+O: same-brain –+U: biological unity –-D+R –Type

IBM T.J. Watson Research Center 65 Property Analysis Legal Agent, Group of People Legal Agent –A legally recognized entity –+L: All legal systems have a defined IC, has- same-legal-ID –-U: no universal unity –+D: on the legal body that recognizes it –~R: not necessary –Material Role Group of People –See Group –+I-O~U-D+R –Quasi-type

IBM T.J. Watson Research Center 66 Property Analysis Social Entity, Organization Social Entity –A group of people together for social reasons –-I: no universal IC –+U: social-connection –-D+R –category Organization –A group of people together, with roles that define some structure –+O: same-mission and way of operating –+U: functional –-D+R –Type

IBM T.J. Watson Research Center 67 Property Analysis Fruit Fruit –An individual fruit, such as an orange or bannana –+O: same-plant, same- shape, etc. (only nec.) –+U: topological –-D+R –Type

IBM T.J. Watson Research Center 68 Property Analysis Apple, Red Apple Apple –+O: shape, color, skin pattern (only nec) –+U: topological –-D+R –Type Red-Apple –+I-O: from Apple –+U: from Apple –-D –~R: no red apple is necessarily red –type-attribution mixin

IBM T.J. Watson Research Center 69 Property Analysis Vertebrate, Person Vertebrate –Really vertebrate- animal –A biological classification that adds new membership criteria (has-backbone) –+I-O: from animal –+U: from animal –-D+R –quasi-type Person –+O: same-fingerprint –+U: from animal –-D+R –Type

IBM T.J. Watson Research Center 70 Property Analysis Butterfly, Caterpillar Butterfly –+L: same-wing-pattern –+U: biological –-D –~R: the same entity can be something else (a caterpillar) –Phased sortal Caterpillar –+L: spots, legs, color –+U: biological –-D –~R: caterpillars become butterflies and change their IC –Phased sortal

IBM T.J. Watson Research Center 71 Property Analysis Country Country –A place recognized by convention as autonomous –+L: government, sub-regions –+U: countries are countable (heuristic) –-D –~R: some countries do not exist as countries any more (e.g. Prussia) but are still places –Phased sortal

IBM T.J. Watson Research Center 72 Entity Fruit Physical object Group of people Country Food Animal Legal agent Amount of matter Group Living being Location Agent Red Red apple Person Vertebrate Apple Caterpillar Butterfly Organization Social entity assign meta-properties

IBM T.J. Watson Research Center 73 Remove non-rigid properties Entity -I-U-D+R Physical object +O+U-D+R Amount of matter +O~U-D+R Group +O~U-D+R Organization +O+U-D+R Location +O-U-D+R Living being +O+U-D+R Person +O+U-D+R Animal +O+U-D+R Social entity -I+U-D+R Agent -I-U+D~R Apple +O+U-D+R Fruit +O+U-D+R Food +I-O~U+D~R Country +L+U-D~R Legal agent +L-U+D~R Group of people +I-O~U-D+R Red apple +I-O+U-D~R Red -I-U-D-R Vertebrate +I-O+U-D+R Caterpillar +L+U-D~R Butterfly +L+U-D~R

IBM T.J. Watson Research Center 74 Entity -I-U-D+R Physical object +O+U-D+R Amount of matter +O~U-D+R Group +O~U-D+R Organization +O+U-D+R Location +O-U-D+R Living being +O+U-D+R Person +O+U-D+R Animal +O+U-D+R Social entity -I+U-D+R Apple +O+U-D+R Fruit +O+U-D+R Group of people +I-O~U-D+R Vertebrate +I-O+U-D+R Analyze taxonomic links ~U can’t subsume +U Living being can change parts and remain the same, but amounts of matter can not (incompatible ICs) Living being is constituted of matter

IBM T.J. Watson Research Center 75 Entity -I-U-D+R Physical object +O+U-D+R Amount of matter +O~U-D+R Group +O~U-D+R Organization +O+U-D+R Location +O-U-D+R Living being +O+U-D+R Person +O+U-D+R Animal +O+U-D+R Social entity -I+U-D+R Apple +O+U-D+R Fruit +O+U-D+R Group of people +I-O~U-D+R Vertebrate +I-O+U-D+R Analyze taxonomic links ~U can’t subsume +U Living being can change parts and remain the same, but amounts of matter can not (incompatible ICs) Living being is constituted of matter

IBM T.J. Watson Research Center 76 Entity -I-U-D+R Physical object +O+U-D+R Amount of matter +O~U-D+R Group +O~U-D+R Organization +O+U-D+R Location +O-U-D+R Living being +O+U-D+R Person +O+U-D+R Animal +O+U-D+R Social entity -I+U-D+R Apple +O+U-D+R Fruit +O+U-D+R Group of people +I-O~U-D+R Vertebrate +I-O+U-D+R Analyze taxonomic links ~U can’t subsume +U Physical objects can change parts and remain the same, but amounts of matter can not (incompatible ICs) Physical object is constituted of matter

IBM T.J. Watson Research Center 77 Entity -I-U-D+R Physical object +O+U-D+R Amount of matter +O~U-D+R Group +O~U-D+R Organization +O+U-D+R Location +O-U-D+R Living being +O+U-D+R Person +O+U-D+R Animal +O+U-D+R Social entity -I+U-D+R Apple +O+U-D+R Fruit +O+U-D+R Group of people +I-O~U-D+R Vertebrate +I-O+U-D+R Analyze taxonomic links ~U can’t subsume +U Physical objects can change parts and remain the same, but amounts of matter can not (incompatible ICs) Physical object is constituted of matter

IBM T.J. Watson Research Center 78 Entity -I-U-D+R Physical object +O+U-D+R Amount of matter +O~U-D+R Group +O~U-D+R Organization +O+U-D+R Location +O-U-D+R Living being +O+U-D+R Person +O+U-D+R Animal +O+U-D+R Social entity -I+U-D+R Apple +O+U-D+R Fruit +O+U-D+R Group of people +I-O~U-D+R Vertebrate +I-O+U-D+R Analyze taxonomic links Meta-properties fine Identity-check fails: when an entity stops being an animal, it does not stop being a physical object (when an animal dies, its body remains) Constitution again

IBM T.J. Watson Research Center 79 Entity -I-U-D+R Physical object +O+U-D+R Amount of matter +O~U-D+R Group +O~U-D+R Organization +O+U-D+R Location +O-U-D+R Living being +O+U-D+R Person +O+U-D+R Animal +O+U-D+R Social entity -I+U-D+R Apple +O+U-D+R Fruit +O+U-D+R Group of people +I-O~U-D+R Vertebrate +I-O+U-D+R Analyze taxonomic links Meta-properties fine Identity-check fails: when an entity stops being an animal, it does not stop being a physical object (when an animal dies, its body remains) Constitution again

IBM T.J. Watson Research Center 80 Entity -I-U-D+R Physical object +O+U-D+R Amount of matter +O~U-D+R Group +O~U-D+R Organization +O+U-D+R Location +O-U-D+R Living being +O+U-D+R Person +O+U-D+R Animal +O+U-D+R Social entity -I+U-D+R Apple +O+U-D+R Fruit +O+U-D+R Group of people +I-O~U-D+R Vertebrate +I-O+U-D+R Analyze taxonomic links ~U can’t subsume +U A group, and group of people, can’t change parts - it becomes a different group A social entity can change parts - it’s more than just a group (incompatible IC) Constitution again

IBM T.J. Watson Research Center 81 Entity -I-U-D+R Physical object +O+U-D+R Amount of matter +O~U-D+R Group +O~U-D+R Organization +O+U-D+R Location +O-U-D+R Living being +O+U-D+R Person +O+U-D+R Animal +O+U-D+R Social entity -I+U-D+R Apple +O+U-D+R Fruit +O+U-D+R Group of people +I-O~U-D+R Vertebrate +I-O+U-D+R Analyze taxonomic links ~U can’t subsume +U A group, and group of people, can’t change parts - it becomes a different group A social entity can change parts - it’s more than just a group (incompatible IC) Constitution again

IBM T.J. Watson Research Center 82 Entity -I-U-D+R Physical object +O+U-D+R Amount of matter +O~U-D+R Group +O~U-D+R Organization +O+U-D+R Location +O-U-D+R Living being +O+U-D+R Person +O+U-D+R Animal +O+U-D+R Social entity -I+U-D+R Apple +O+U-D+R Fruit +O+U-D+R Group of people +I-O~U-D+R Vertebrate +I-O+U-D+R Analyze Phased Sortals For phased sortals: what do they phase into? Country is anti-rigid because it is representing multiple senses of country: a geographical region and a political entity. Split the two senses into two concepts, both rigid, both types. Country +L+U-D~R

IBM T.J. Watson Research Center 83 Country +O+U-D+R Entity -I-U-D+R Physical object +O+U-D+R Amount of matter +O~U-D+R Group +O~U-D+R Organization +O+U-D+R Location +O-U-D+R Living being +O+U-D+R Person +O+U-D+R Animal +O+U-D+R Social entity -I+U-D+R Apple +O+U-D+R Fruit +O+U-D+R Group of people +I-O~U-D+R Vertebrate +I-O+U-D+R Analyze Phased Sortals There is a relationship between the two, but not subsumption. Country +L+U-D~R Geographical Region +O-U-D+R

IBM T.J. Watson Research Center 84 Country +O+U-D+R Entity -I-U-D+R Physical object +O+U-D+R Amount of matter +O~U-D+R Group +O~U-D+R Organization +O+U-D+R Location +O-U-D+R Living being +O+U-D+R Person +O+U-D+R Animal +O+U-D+R Social entity -I+U-D+R Apple +O+U-D+R Fruit +O+U-D+R Group of people +I-O~U-D+R Vertebrate +I-O+U-D+R Analyze Phased Sortals Geographical Region +O-U-D+R Caterpillar +L+U-D~R Butterfly +L+U-D~R Caterpillar phases into butterfly - a true phased sortal There must be some property from which a single entity can uniquely claim identity across phases Define a rigid property which subsumes only the phases of the same entity. Lepidopteran +O+U-D+R

IBM T.J. Watson Research Center 85 Country +O+U-D+R Entity -I-U-D+R Physical object +O+U-D+R Amount of matter +O~U-D+R Group +O~U-D+R Organization +O+U-D+R Location +O-U-D+R Living being +O+U-D+R Person +O+U-D+R Animal +O+U-D+R Social entity -I+U-D+R Apple +O+U-D+R Fruit +O+U-D+R Group of people +I-O~U-D+R Vertebrate +I-O+U-D+R Analyze Phased Sortals Geographical Region +O-U-D+R Caterpillar +L+U-D~R Butterfly +L+U-D~R Lepidopteran +O+U-D+R

IBM T.J. Watson Research Center 86 Country +O+U-D+R Entity -I-U-D+R Physical object +O+U-D+R Amount of matter +O~U-D+R Group +O~U-D+R Organization +O+U-D+R Location +O-U-D+R Living being +O+U-D+R Person +O+U-D+R Animal +O+U-D+R Social entity -I+U-D+R Apple +O+U-D+R Fruit +O+U-D+R Group of people +I-O~U-D+R Vertebrate +I-O+U-D+R Analyze Roles Geographical Region +O-U-D+R Caterpillar +L+U-D~R Butterfly +L+U-D~R Lepidopteran +O+U-D+R Agent -I-U+D~R ~R can’t subsume +R Really want a type restriction: all agents are animals or social entities. Subsumption is not disjunction!

IBM T.J. Watson Research Center 87 Country +O+U-D+R Entity -I-U-D+R Physical object +O+U-D+R Amount of matter +O~U-D+R Group +O~U-D+R Organization +O+U-D+R Location +O-U-D+R Living being +O+U-D+R Person +O+U-D+R Animal +O+U-D+R Social entity -I+U-D+R Apple +O+U-D+R Fruit +O+U-D+R Group of people +I-O~U-D+R Vertebrate +I-O+U-D+R Analyze Roles Geographical Region +O-U-D+R Caterpillar +L+U-D~R Butterfly +L+U-D~R Lepidopteran +O+U-D+R Agent -I-U+D~R ~R can’t subsume +R Really want a type restriction: all agents are animals or social entities. Subsumption is not disjunction!

IBM T.J. Watson Research Center 88 Country +O+U-D+R Entity -I-U-D+R Physical object +O+U-D+R Amount of matter +O~U-D+R Group +O~U-D+R Organization +O+U-D+R Location +O-U-D+R Living being +O+U-D+R Person +O+U-D+R Animal +O+U-D+R Social entity -I+U-D+R Apple +O+U-D+R Fruit +O+U-D+R Group of people +I-O~U-D+R Vertebrate +I-O+U-D+R Analyze Roles Geographical Region +O-U-D+R Caterpillar +L+U-D~R Butterfly +L+U-D~R Lepidopteran +O+U-D+R Agent -I-U+D~R ~R can’t subsume +R Another disjunction: all legal agents are countries, persons, or organizations Legal agent +L-U+D~R

IBM T.J. Watson Research Center 89 Country +O+U-D+R Entity -I-U-D+R Physical object +O+U-D+R Amount of matter +O~U-D+R Group +O~U-D+R Organization +O+U-D+R Location +O-U-D+R Living being +O+U-D+R Person +O+U-D+R Animal +O+U-D+R Social entity -I+U-D+R Apple +O+U-D+R Fruit +O+U-D+R Group of people +I-O~U-D+R Vertebrate +I-O+U-D+R Analyze Roles Geographical Region +O-U-D+R Caterpillar +L+U-D~R Butterfly +L+U-D~R Lepidopteran +O+U-D+R Agent -I-U+D~R ~R can’t subsume +R Another disjunction: all legal agents are countries, persons, or organizations Legal agent +L-U+D~R

IBM T.J. Watson Research Center 90 Country +O+U-D+R Entity -I-U-D+R Physical object +O+U-D+R Amount of matter +O~U-D+R Group +O~U-D+R Organization +O+U-D+R Location +O-U-D+R Living being +O+U-D+R Person +O+U-D+R Animal +O+U-D+R Social entity -I+U-D+R Apple +O+U-D+R Fruit +O+U-D+R Group of people +I-O~U-D+R Vertebrate +I-O+U-D+R Analyze Roles Geographical Region +O-U-D+R Caterpillar +L+U-D~R Butterfly +L+U-D~R Lepidopteran +O+U-D+R Agent -I-U+D~R Legal agent +L-U+D~R ~R can’t subsume +R Apple is not necessarily food. A poison-apple, e.g., is still an apple. ~U can’t subsume +U Caterpillars are wholes, food is stuff. Food +I-O~U+D~R

IBM T.J. Watson Research Center 91 Country +O+U-D+R Entity -I-U-D+R Physical object +O+U-D+R Amount of matter +O~U-D+R Group +O~U-D+R Organization +O+U-D+R Location +O-U-D+R Living being +O+U-D+R Person +O+U-D+R Animal +O+U-D+R Social entity -I+U-D+R Apple +O+U-D+R Fruit +O+U-D+R Group of people +I-O~U-D+R Vertebrate +I-O+U-D+R Analyze Roles Geographical Region +O-U-D+R Caterpillar +L+U-D~R Butterfly +L+U-D~R Lepidopteran +O+U-D+R Agent -I-U+D~R Legal agent +L-U+D~R ~R can’t subsume +R Apple is not necessarily food. A poison-apple, e.g., is still an apple. ~U can’t subsume +U Caterpillars are wholes, food is stuff. Food +I-O~U+D~R

IBM T.J. Watson Research Center 92 Country +O+U-D+R Entity -I-U-D+R Physical object +O+U-D+R Amount of matter +O~U-D+R Group +O~U-D+R Organization +O+U-D+R Location +O-U-D+R Living being +O+U-D+R Person +O+U-D+R Animal +O+U-D+R Social entity -I+U-D+R Apple +O+U-D+R Fruit +O+U-D+R Group of people +I-O~U-D+R Vertebrate +I-O+U-D+R Analyze Attributions Geographical Region +O-U-D+R Caterpillar +L+U-D~R Butterfly +L+U-D~R Lepidopteran +O+U-D+R Agent -I-U+D~R Legal agent +L-U+D~R No violations Attributions are discouraged, can be confusing. Often better to use attribute values (i.e. Apple Color red) Food +I-O~U+D~R Red -I-U-D-R Red apple +I-O+U-D~R

IBM T.J. Watson Research Center 93 Country +O+U-D+R Entity -I-U-D+R Physical object +O+U-D+R Amount of matter +O~U-D+R Group +O~U-D+R Organization +O+U-D+R Location +O-U-D+R Living being +O+U-D+R Person +O+U-D+R Animal +O+U-D+R Social entity -I+U-D+R Apple +O+U-D+R Fruit +O+U-D+R Group of people +I-O~U-D+R Vertebrate +I-O+U-D+R Geographical Region +O-U-D+R Caterpillar +L+U-D~R Butterfly +L+U-D~R Lepidopteran +O+U-D+R Agent -I-U+D~R Legal agent +L-U+D~R Food +I-O~U+D~R Red -I-U-D-R Red apple +I-O+U-D~R

IBM T.J. Watson Research Center 94 Country +O+U-D+R Entity -I-U-D+R Physical object +O+U-D+R Amount of matter +O~U-D+R Group +O~U-D+R Organization +O+U-D+R Location +O-U-D+R Living being +O+U-D+R Person +O+U-D+R Animal +O+U-D+R Social entity -I+U-D+R Apple +O+U-D+R Fruit +O+U-D+R Group of people +I-O~U-D+R Vertebrate +I-O+U-D+R Geographical Region +O-U-D+R Lepidopteran +O+U-D+R The backbone taxonomy

IBM T.J. Watson Research Center 95 Entity -I-U-D+R Physical object +O+U-D+R Amount of matter +O~U-D+R Group +O~U-D+R Organization +O+U-D+R Location +O-U-D+R Living being +O+U-D+R Person +O+U-D+R Animal +O+U-D+R Social entity -I+U-D+R Agent -I-U+D~R Apple +O+U-D+R Fruit +O+U-D+R Food +I-O~U+D~R Legal agent +L-U+D~R Group of people +I-O~U-D+R Red apple +I-O+U-D~R Red -I-U-D-R Vertebrate +I-O+U-D+R Caterpillar +L+U-D~R Butterfly +L+U-D~R Country +O+U-D+R Geographical Region +O-U-D+R Lepidopteran +O+U-D+R

IBM T.J. Watson Research Center 96 Entity Fruit Physical object Group of people Country Food Animal Legal agent Amount of matter Group Living being Location Agent Red Red apple Person Vertebrate Apple Caterpillar Butterfly Organization Social entity Before

IBM T.J. Watson Research Center 97 Entity -I-U-D+R Physical object +O+U-D+R Amount of matter +O~U-D+R Group +O~U-D+R Organization +O+U-D+R Location +O-U-D+R Living being +O+U-D+R Person +O+U-D+R Animal +O+U-D+R Social entity -I+U-D+R Agent -I-U+D~R Apple +O+U-D+R Fruit +O+U-D+R Food +I-O~U+D~R Legal agent +L-U+D~R Group of people +I-O~U-D+R Red apple +I-O+U-D~R Red -I-U-D-R Vertebrate +I-O+U-D+R Caterpillar +L+U-D~R Butterfly +L+U-D~R Country +O+U-D+R Geographical Region +O-U-D+R Lepidopteran +O+U-D+R After

IBM T.J. Watson Research Center 98 Use OntoClean for all your ontology cleaning needs!

IBM T.J. Watson Research Center 99 Ontology-driven conceptual modeling Formal Ontological Properties/Relations Useful Property Kinds Ontology-Driven Modeling Principles Minimal Top-Level Ontology User Conceptualization Conceptual Model Ontology Methodology