Artificial Intelligence

Artificial Intelligence Giancarlo Guizzardi (guizzardi@acm.org ) http://nemo.inf.ufes.br Computer Science Department Federal University of Espírito Santo (UFES), Brazil

Ontological Engineering • There are currently several existing methods for addressing the many different activities that comprise the process of Ontological Engineering • METHONTOLOGY (Gomez-Perez et al.) • SKELETAL METHODOLOGY (Uschold and King) • TOVE Method (Gruninger and Fox) • On-to-Knowledge (Karlsruhe) • NeOn Methodology • SABiO (Falbo) • OntoClean (Guarino and Welty) • oQual (Gangemi et al.) • OntoQA (Sheth et al.)

Basic Development Methodology • Purpose and Scope Identification (Requirements Engineering) • Ontology Reuse • Ontology Conceptual Modeling • Ontology Integration • Ontology Design • Transformation Patterns and Guidelines • Ontology Codification • Ontology Evaluation

Basic Development Methodology • Purpose and Scope Identification (Requirements Engineering) • The objective here is to understand all the concepts which are central to the domain, i.e., the meaning of the relevant types, properties (instrinsic and relational), events and domain laws. • Define the scope of ontology, its purposes and expected applications (competence of the ontology) • A technique of particular relevance here is the definition of the so-called Competence Questions, i.e., the definition of what are the questions which ontology are expected to be able to answer • Competence Questions is a technique for Scope and Purpose Definition, but also for validation

Basic Development Methodology • Purpose and Scope Identification (Requirements Engineering) • Scope definition is a critical aspect since several different (correct) ontologies can be defined for the same domain with different scopes in mind • In any case, try to be as generic as possible. The role is capture the conceptualization of a domain in reality, not a database schema!

Basic Development Methodology • Purpose and Scope Identification (Requirements Engineering) • Scope definition is a critical aspect since several different (correct) ontologies can be defined for the same domain with different scopes in mind • In any case, try to be as generic as possible (Minimal Ontological Commitment). The role is capture the conceptualization of a domain in reality, not a database schema! • If the domain is two complex, an important technique is Domain Modularization so that different (ideally) orthogonal aspects of the domain can be capture in different subontologies

ECG Ontology Modularization UFO & OBO Relation Ontology Heart Electrophysiology subontology Anatomy subontoloty ECG subontology

Basic Development Methodology • Ontology Conceptual Modeling • The use of the support of a Foundational Ontology is fundamental in this phase. • The objective is to maximize expressiveness, truthfulness w.r.t. the domain at hand and conceptual clarity • In this course, we will use the UFO ontology and a particular language based on it termed OntoUML • Initially we will deal exclusively with Structural Aspects of the domain • Represent all elements in the Universe of Discourse • Model Types of Objects (both dependent and independent) • Including Higher-Order Types • Model Categories of Object Types • Model Types of Properties (both Relational and Intrinsic) and Property Value Spaces • Model Derived Types and Derivation Rules • Model Integrity Constraints

Basic Development Methodology • Ontology Conceptual Modeling • Define a Dictionary of Terms specifying in Natural Language and by using examples the primitive elements of the ontology • Analyze the Ontology using Analysis Patterns • The use of Visual Language is very important in this phase • The use of a Model-Based Editor can be of great help • Validate Conceptual Model • Model Simulation

Ontology Conceptual Model (OntoUML) Computational Independent Model (CIM) Platform Independent Model (PIM) Ontology Design Model1 Ontology Design Model1 Platform Specific Model (PSM) Ontology Codification3 Ontology Codification1 Ontology Codification2

A Whirlwind introduction to the uml 2.0 Class Fragment

Classes and Attributes • A class describes the common features (e.g., intrinsic and relational properties) shared by a (possible multitude of) entities which are then said to be the instances of that class • Instances of a class must contain values for each attribute that is defined for that class, in accordance with the characteristics of the attribute, for example its type and multiplicity. • Attributes represent (more or less intrinsic) properties of shared by members of a class

Classes and Attributes • A class describes the common features (e.g., intrinsic and relational properties) shared by a (possible multitude of) entities which are then said to be the instances of that class • Instances of a class must contain values for each attribute that is defined for that class, in accordance with the characteristics of the attribute, for example its type and multiplicity. • Attributes represent (more or less intrinsic) properties of shared by members of a class attribute name attribute type attribute multiplicity

Specialization • Classes can be related to each other via specialization relations forming a taxonomic structure • All properties of a class are inherited through a specialization chain • A class can be a specialization of multiple classes • The specialization relation is a anti-symmetric and transitive relation, i.e., If A specializes B then B cannot specialize A If A specializes B and B specializes C then A specializes C

Specialization • Classes can be related to each other via specialization relations forming a taxonomic structure • All properties of a class are inherited through a specialization chain • A class can be a specialization of multiple classes • The specialization relation is a anti-symmetric and transitive relation, i.e., If A specializes B then B cannot specialize A If A specializes B and B specializes C then A specializes C supertype subtype

Generalization Set • Classes sharing a common direct supertype can be group in what is called a generalization set • There are two meta-attributes that can be used ascribe a stronger semantics to a generalization set, namely, the complete and disjoint meta-attributes

Specialization (Set-Theoretical Semantics) PERSON Man PERSON Man Woman

Complete • If a generalization set is complete then the subclasses exhaust the instances of the common direct superclass. • In other words, in this case, there is no instance of the superclass which is not an instance of one of the subclasses participating in the generalization Man and Woman Man Woman PERSON

Disjoint • If a generalization set is disjoint if all the subclasses participating in the generalization are mutually exclusive • In other words, the intersection between these any of these subclasses pairwise is necessarily empty neither Man or Woman PERSON Man Woman

Disjoint and Complete • If a generalization set is disjoint and complete then the subclasses participating in this generalization set form a partition. • In other words, every instance of the common superclass is an instance of one and only one of the subclasses • In this case, the common superclass is an abstract class Man Woman PERSON

Associations • An association declares that there can be links between instances of the associated types. A link is a tuple with one value for each end of the association, where each value is an instance of the type of the end.

Associations • One specify multiplicity constraints for each end of the association • The possibilities for cardinality constraints are: max min

Associations • When one or more ends of the association are ordered, links carry ordering information in addition to their end values. • To each association end, one can specify a rolename definying the “role played by objects of that type in the association”

Associations • When one or more ends of the association are ordered, links carry ordering information in addition to their end values. • To each association end, one can specify a rolename definying the “role played by objects of that type in the association” rolename reading directionality tagged valued representing association end’s ordering meta-attribute

Associations • One can define type-reflexive associations, i.e., associations in which both association ends are connected to the same type

Datatypes • A Datatype represents the set of possible values that an attribute can take. • If attributes are seen as functions (mapping the extension of a class to a datatype) than they are the range of that function • Datatypes have no instances in the real sense, they are simply sets of values. They have members which are abstract individuals, i.e., they are not explicitly created or destroyed but are simply assumed to exist

Datatypes • There are simple and structured datatype. The “attributes” of a structured datatype are named datatype fields • The members of a datatype with n fields are n-uples. For instance, the members of the color datatype below are triples <h,s,b> of hue, saturation and brightness values

Enumeration • Since datatypes are sets we have that: (i) two datatypes are the same iff they have the same members • One can define a datatype by explicit enumeration, i.e., by explicitly defining its member values

Derived types and derivation constraints

Derived Types • We distinguish between Base Types and Derived Types • Derivability has to do with how the population of a type is generated • E.g., contrast Book with Borrowed Book, Person with Adult Person

Derived Types and Derivation Rule x AdultPerson(x) x AdultPerson(x) Person(x)  (Age(x)  18) Person(x)  (Age(x)  18) • For each derived types there is an associated derivation rule describing necessary and sufficient conditions for classification (instantiation) E.g., Every Adult Person is a Person who is older than 18

SVBR (Semantics of Business Vocabularies and Rules)

Derived Types and Derivation Rule x AdultPerson(x) x AdultPerson(x) Person(x)  (Age(x)  18) Person(x)  (Age(x)  18) • For each derived types there is an associated derivation rule describing necessary and sufficient conditions for classification (instantiation) E.g., Every Adult Person is a Person who is older than 18 x AdultPerson(x) =def Person(x)  (Age(x)  18)

Derived Types and Derivation Rule x AdultPerson(x) x AdultPerson(x) Person(x)  (Age(x)  18) Person(x)  (Age(x)  18) • For each derived types there is an associated derivation rule describing necessary and sufficient conditions for classification (instantiation) E.g., Every Borrowed Book is a Book with an associated Rental Object

Derived Types and Derivation Rule x AdultPerson(x) x AdultPerson(x) Person(x)  (Age(x)  18) Person(x)  (Age(x)  18) • For each derived types there is an associated derivation rule describing necessary and sufficient conditions for classification (instantiation) E.g., Every Borrowed Book is a Book with an associated Loan

Derived Types and Derivation Rule x AdultPerson(x) x AdultPerson(x) Person(x)  (Age(x)  18) Person(x)  (Age(x)  18) • For each derived types there is an associated derivation rule describing necessary and sufficient conditions for classification (instantiation) E.g., Every Borrowed Book is a Book with an associated Loan x BorrowedBook(x) =def Book(x) y(BookLoan(y)  associated-with(y,x))

Derived Types and Derivation Rule x AdultPerson(x) x AdultPerson(x) Person(x)  (Age(x)  18) Person(x)  (Age(x)  18) • For each derived types there is an associated derivation rule describing necessary and sufficient conditions for classification (instantiation) E.g., Every Available Book is a Book without an associated Loan

Derived Types Quadrilateral is a Polygon with exactly four sides

Derived Types A BorrowedBook is a Book associated to a Loan and a BorrowingClient is a Person associated to a Loan

Derivation by Specialization x AdultPerson(x) x AdultPerson(x) Person(x)  (Age(x)  18) Person(x)  (Age(x)  18) • A types is defined as a specialization of another type which has specific (intrinsic or relational) properties E.g., A Democratic President is a President elected by a Direct Election Procedure PRESIDENT Democratic President

Artificial Intelligence