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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

INTRODUCTION TO ARTIFICIAL INTELLIGENCE. Massimo Poesio LECTURE 5: Ontologies and Formal Ontologies. LOGIC vs ONTOLOGIES. Logic is not ‘knowledge’: is just a language for encoding knowledge and inferences

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

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  1. INTRODUCTION TO ARTIFICIAL INTELLIGENCE Massimo PoesioLECTURE 5: Ontologies and Formal Ontologies

  2. LOGIC vs ONTOLOGIES • Logic is not ‘knowledge’: is just a language for encoding knowledge and inferences • Already Aristotle realized that we need a separate theory of what types of objects there are, what properties they have, and how they are related, and made a first attempt in his CATEGORIES • Now this area of research is known as (Formal) ontology

  3. PHILOSOPHICAL BACKGROUND • Aristotle’s Metaphysics: • A list of 10 categories • Criteria for definition of categories • Tree of Porphiry: Organize categories under SUBSTANCE in a hierarchy • Brentano: all ten categories • Kant: neutral as to whether these categories really reflect the world or merely our conception of it

  4. ARISTOTLE’s CATEGORIES • SUBSTANCE (man, horse) • QUALITY (white, heavy) • QUANTITY (four-foot, five-foot) • ACTIVITY (cutting, burning) • PASSIVITY (being cut, being burned) • SPATIALITY / LOCATION (in the Lyceum) • TEMPORALITY / LOCATION (yesterday, last year) • RELATION (double, half) • HAVING / STATE (has shoes on) • SITUATEDNESS / POSTURE (is lying, is sitting) NB: simultaneously a classification of what is there and what properties there may be

  5. DEFINITION OF CONCEPTS(Aristotle’s Metaphysics, Book Z) “a definition is an account, and every account has parts, and part of the account stands to part of the thing in just the same way that the whole account stands to the whole thing” = Most concepts encode necessary and sufficient conditions for their own application

  6. DEFINIENDUM DEFINITIONS BY GENUS AND DIFFERENTIA MAN = RATIONAL ANIMAL DEFINIENS

  7. Definition by genus and differentia • The ‘method of division’: • Begin with the broadest genus containing the species to be defined (‘ANIMAL’) • Divide the genus in two sub-parts by some differentia (‘FOOTED’) • Then divide the two sub-types again (CLOVEN-FOOTED)

  8. THE TREE OF PORPHIRY

  9. Other philosophers The greatest part of the Ideas, that make our complex Idea of GOLD, are YELLOWNESS, great WEIGHT, FUSIBILITY, and SOLUBILITY IN AQUA REGIA (Locke) In the case of many words … it is possible to specify their meaning by reference to other words. E.g., “ARTHROPODES” are ANIMALS with SEGMENTED BODIES and JOINTED LEGS. (Carnap)

  10. `BOTTOM-UP’ ONTOLOGIES IN AI • The interest of Artificial Intelligence researchers in these ideas was born out of attempts to model knowledge in specific domains

  11. THE BLOCKS WORLD

  12. A BLOCKS WORLD ONTOLOGY OBJECT GRASPABLE NON- GRASPABLE grasp(arm,x) ~grasp(arm,x) STACKABLE stack(x,y) PYRAMID CUBE ~stack(x,y)

  13. TODAY’s DOMAIN-SPECIFIC ONTOLOGIES • Protein Ontology: developed to codify in a systematic way our knowledge about proteins • http://pir.georgetown.edu/pro/ • Other ontologies listed on OPEN BIOMEDICAL ONTOLOGY • http://www.obofoundry.org/ • Gene ontology, C. elegans, etc • Medical domain: UMLS

  14. PROTEIN ONTOLOGY

  15. UPPER ONTOLOGIES • The work on domain-specific ontologies eventually led to the desire to develop ontologies that could ‘connect’ formalizations in one domain with formalizations in other domains • E.g., an ontology for biology with an ontology for medicine • But also an ontology of art with an ontology for tourism • Whether this is actually possible is a deep philosophical question

  16. CYC • One of the first attempts in AI to produce such an overarching ontology was done in the CYC project – an effort to produce an enCYClopedia of commonsense knowledge

  17. THE CYC ONTOLOGY http://www.cyc.com/

  18. PROBLEMS ENCOUNTERED IN CYC • The researchers working on CYC found themselves confronting every single issue in knowledge representation • E.g., how to define VIDEOTAPE? • A strip of coated plastic? (concrete) • The information contained on that strip? (abstract)

  19. FORMAL ONTOLOGIES • Work on formal ontologies is concerned with providing an inferential characterization of categories in terms of logic • A simple example of inference: • if X is a PHYSICAL OBJECT, then moving X from L1 to L2 implies that the LOCATION of X after the movement is L2 • A more complex inference: • Moving X with mass M from L1 to L2 implies that the total mass at L1 is reduced by M, whereas the total mass at L2 is increased by M (this is not true if X is an abstract object)

  20. UPPER ONTOLOGIES: DOLCE • Work on specifying the ‘categories of existence’ is exemplified by DOLCE, an upper ontology developed by the Lab for Applied Ontology of CNR (Povo)

  21. PT Particular ED Endurant PD Perdurant Q Quality AB Abstract PED Physical Endurant NPED Non-physical Endurant AS Arbitrary Sum EV Event STV Stative TQ Temporal Quality PQ Physical Quality AQ Abstract Quality … Fact Set R Region M Amount of Matter F Feature POB Physical Object … NPOB Non-physical Object ACH Achievement ACC Accomplishment ST State PRO Process TL Temporal Location … … SL Spatial Location … TR Temporal Region PR Physical Region AR Abstract Region … … … … … T Time Interval … S Space Region … APO Agentive Physical Object NAPO Non-agentive Physical Object MOB Mental Object SOB Social Object ASO Agentive Social Object NASO Non-agentive Social Object SAG Social Agent SC Society DOLCE’S TAXONOMY

  22. FUNDAMENTAL ONTOLOGICAL CHOICES IN DOLCE • CONCRETE: ‘rock’ • Exists in space / time • ABSTRACT: ‘law’ • Does not exists in space time

  23. FUNDAMENTAL ONTOLOGICAL CHOICES IN DOLCE • ENDURANT vs PERDURANT • ENDURANT OBJECTS: have a stable identity over a period of time (e.g., concrete objects) • PERDURANT OBJECTS: events that occur and then exist no more

  24. FUNDAMENTAL ONTOLOGICAL CHOICES IN DOLCE • QUALITIES • The particular qualities of specific objects (e.g., the specific color of this specific slide, the particular weight of this particular laptop, etc) • Each quality associated with a QUALITY SPACE that specifies the range of values that quality may take

  25. FORMALIZATIONS OF RELATIONS • Arguably most of the work on formal ontology is concerned with the formalization of RELATIONS • PARTS • SPACE • TIME

  26. PART-OF RELATION(S) • A great variety of relations between objects could be called ‘part’: • My hand is part of my body • The handle of the door • The top of the cupboard • This dish is made up of pepper and cod • This atom has one electron

  27. A SINGLE PART-OF RELATION? • In MEREOLOGY (Lesniewski, 1927-31; Link, 1983; Simons, 1987); a single transitive part-of relation is proposed • Problems: intransitivity • Marguerite’s tail is part of Marguerite the cow • Marguerite the cow is part of the herd • But: Marguerite’s tail is not part of the herd

  28. Winston et al’s classification • Winston et al (1987) distinguish between six types of part relation: • COMPONENT-INTEGRAL OBJECT (handle / cup) • PORTION-WHOLE(slice / pie) • SUBSTANCE-WHOLE(steel / bike) • MEMBER-COLLECTION (tree / forest) • FEATURE-ACTIVITY (paying / shopping) • PLACE-AREA (oasis / desert)

  29. VIEU & ARNAGUE 2007 • Vieu & Arnague show that many of the ‘part-of’ relations can be distinguished using a limited number of categories: • PLURALITY • Both ELEMENT-COLLECTION and SUB-COLLECTION –COLLECTION require one (or two) of the relata to be collections • SUBSTANCE • PORTION-WHOLE and SUBSTANCE-WHOLE require one of the relata to be a substance • This leaves out • ‘PART’ proper, Component-Integral Whole (CIW) • Temporal and spatial part

  30. COMPONENT-INTEGRAL-WHOLE • Main claim: an account of the ‘proper’ part relation requires an account of FUNCTIONALITY • Part of what makes a object a ‘hand’ or a ‘wheel’ is the function it performs • Previous accounts: Wright, Cummins, Searle • Wright: ‘proper function’ analyzed in terms of evolution • Problem: doesn’t apply to non-biological entities • Cummins: the function of a pigeon’s wing with respect to some analytical account of the pigeon’s capacity to fly is to generate lift and propulsion

  31. LEXICAL TYPES • Contrasts such as • The motor is part of the car • The motor is part of the vehicle • ?? The motor is part of the ARTEFACT • Suggest to Vieu & Arnague that CIW is a relation between LEXICAL TYPES not denotations • CIW-direct(x,X,y,Y,t)

  32. CIW: DEFINITION INDIVIDUAL FUNCTIONAL DEPENDENCE GENERIC FUNCTIONAL DEPENDENCE PHYSICAL PART CLASSIFIED AS

  33. OTHER AREAS OF RESEARCH IN FORMAL ONTOLOGY • Time • Space • Causality

  34. ONTOLOGIES ON THE WEB: THE SEMANTIC WEB • The Semantic Web (Berners-Lee et al, 2001) is a proposal to specify the type of objects mentioned in a Web page

  35. AN EXAMPLE OF SEMANTICALLY MARKED PAGE

  36. JIM HENDLER’S PAGE, SEMANTIC WEB INFO <BODY> <INSTANCE KEY="http://www.cs.umd.edu/users/hendler/"> <USE-ONTOLOGY ID="cs-dept-ontology" VERSION="1.0" PREFIX="cs" URL= "http://www.cs.umd.edu/projects/plus/SHOE/cs.html" /> <CATEGORY NAME="cs.Professor" FOR="http://www.cs.umd.edu/users/hendler/"/> <RELATION NAME="cs.member"> <ARG POS=1 VALUE="http://www.cs.umd.edu/projects/plus/"> <ARG POS=2 VALUE="http://www.cs.umd.edu/users/hendler/"> </RELATION> <RELATION NAME="cs.name"> <ARG POS=2 VALUE="Dr. James Hendler"> </RELATION> <RELATION NAME="cs.doctoralDegreeFrom"> <ARG POS=1 VALUE="http://www.cs.umd.edu/users/hendler/"> <ARG POS=2 VALUE="http://www.brown.edu"> </RELATION> <RELATION NAME="cs.emailAddress"> <ARG POS=2 VALUE="hendler@cs.umd.edu"> </RELATION> …..</INSTANCE> <b>As of January 1, 2007 Professor Hendler has moved from the University of Maryland to <a href="http://www.rpi.edu">Rensselaer Polytechnic Institute</a></b>.

  37. SEMANTIC WEB INGREDIENTS • XML as a language of markup • RDF as the basic tool for representing information • OWL (Web Ontology Language) to describe concepts, attributes, and relations • One or more ontologies

  38. RESOURCE DESCRIPTION FRAMEWORK (RDF) • A language to describe statements of the form: <RESOURCE, PROPERTY, VALUE> ‘Il presidente Ciampi vive a Roma’

  39. RDF EXAMPLE <?xml version='1.0'?> <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:wikipedia="http://it.wikipedia.org/wiki/" xmlns:wikidizionario="http://it.wiktionary.org/wiki/"> <rdf:Description rdf:about="http://www.quirinale.it/presidente/ciampi.htm"> <wikidizionario:vivere rdf:resource="http://www.comune.roma.it/index.asp"/> <wikipedia:codice_fiscale> CMPCLZ20T09E625V </wikipedia:codice_fiscale> </rdf:Description> </rdf:RDF>

  40. OWL: A LANGUAGE TO DESCRIBE ONTOLOGIES • A series of languages allowing increasingly more complex descriptions • OWL-LITE: taxonomies, restrictions • OWL-DL: Description Logics (see next week) • OWL-FULL: Maximum expressivity

  41. OWL <owl:Class rdf:ID="ProteinComplex"> <owl:disjointWith> <owl:Class rdf:ID="SiteGroup"/> </owl:disjointWith> <owl:disjointWith> <owl:Class rdf:about="#Chains"/> </owl:disjointWith> <owl:disjointWith> <owl:Class rdf:about="#Residues"/> </owl:disjointWith>

  42. READINGS • Sowa, Knowledge Representation, Brooks & Cole, chapter 2 • Vieu & Arnague (2007), Part-of Relations, Functionality and Dependence, In Aurnague, M.; Hickmann, M. and Vieu, L. (eds.), The Categorization of Spatial Entities in Language and Cognition, John Benjamins, p. 307-337.

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