Ontologies, Ontologies, Intelligent Intelligent

Software Agents on Software Agents on the Semantic Webthe Semantic Web

Oscar LinOscar LinAthabasca UniversityAthabasca University

June 26, 2006June 26, 2006ITS 2006, TaipeiITS 2006, Taipei

OutlineOutline 1. Overall of Ontologies1. Overall of Ontologies 2. Importance of ontologies in the 2. Importance of ontologies in the

Semantic WebSemantic Web 3. 3.

1. Overall of Ontologies1. Overall of Ontologies Ontologies are about vocabularies and Ontologies are about vocabularies and

their meanings, with explicit, expressive, their meanings, with explicit, expressive, and well-defined semantics --- possibly and well-defined semantics --- possibly machine-interpretable.machine-interpretable. What does this statement mean?What does this statement mean? What’s a vocabulary?What’s a vocabulary? What’s a meaning?What’s a meaning? What is semantics?What is semantics? What does machine-interpretable mean?What does machine-interpretable mean? What is ontology and what are ontologies?What is ontology and what are ontologies?

Ontology ExampleOntology ExamplePerson Organization

Employee

Staff_Employee

Group

Management_Employee

Division

DepartmentDirectorPresident ManagerVice_President

Company

isa

isa

isa

isa

Part_of

isaisa isaisa

isa

isa

isa

HR Ontology ExampleHR Ontology ExamplePerson Organization

Employee

Staff_Employee

Group

Management_Employee

Division

DepartmentDirectorPresident ManagerVice_President

Company

isa

isa

isa

isa

Part_of

isaisa isaisa

isa

isa

isa

manages

manages

manages

manages

manages

HR Ontology ExampleHR Ontology ExamplePerson Organization

Employee

Staff_Employee

Group

Management_Employee

Division

DepartmentDirectorPresident ManagerVice_President

Company

isa

isa

isa

isa

Part_of

isaisa isaisa

isa

isa

isa

manages

manages

manages

manages

manages

Part_of

Part_of

Part_of

Managed_byPart_ofemploys

Employee_of

Concepts: --- correspond to themental concepts that human beingshave when they understand a particularbody of knowledge, or subject matter area or domain.

HR Ontology HR Ontology ExampleExample

PersonAddress StringName StringBirthdate Stringssn String

Organization

Employee

Staff_Employee

Group

Management_Employee

Division

DepartmentDirectorPresident

----------------------------------Manages classes Company

ManagerVice_President

Company

isa

isa

isa

isa

Part_of

isaisa isaisa

isa

isa

isa

manages

manages

manages

manages

manages

Part_of

Part_of

Part_of

Managed_byPart_ofemploys

Employee_of

Concepts, Relations, Concepts, Relations, Properties/Attributes, Value Properties/Attributes, Value

RangeRange These concepts and the relationships between them are These concepts and the relationships between them are

usually implemented as classes, relations, properties, usually implemented as classes, relations, properties, attributes, and value (of the properties/attributes).attributes, and value (of the properties/attributes).

The relations between these entity-focused concepts, such The relations between these entity-focused concepts, such as employee-of, managed_by, and manages.as employee-of, managed_by, and manages.

Finally properties or attributes are depicted, examples Finally properties or attributes are depicted, examples include address, name, birth-date, and ssn under the include address, name, birth-date, and ssn under the Person class. Person class.

These properties or attributes have either explicit values These properties or attributes have either explicit values or, more often, have value ranges. or, more often, have value ranges.

The value range for the property/attribute of employee_of, The value range for the property/attribute of employee_of, a property of the class Employee, for example, is the class a property of the class Employee, for example, is the class Organization.Organization.

By range we mean that the only possible values for any By range we mean that the only possible values for any instances of the property employee_of defined for the class instances of the property employee_of defined for the class Employee must come from the class Organization. Employee must come from the class Organization.

Rendition of an OntologyRendition of an Ontology(Open Knowledge Base Connectivity (Open Knowledge Base Connectivity

Language Language – OKBC)– OKBC) (defclass(defclass

(is-a USER)(is-a USER) (role concrete)(role concrete) (single-sot managed_by(single-sot managed_by (type SYMBOL)(type SYMBOL) ;+ (allowed-classes Management_Employee);+ (allowed-classes Management_Employee) ;+ (cardinality 1 1);+ (cardinality 1 1) (create-accessor read-write))(create-accessor read-write)) (single-slot part_of(single-slot part_of (type SYMBOL)(type SYMBOL) ;+ (allowed-parents Organization);+ (allowed-parents Organization) ;+ (cardinality 0 1);+ (cardinality 0 1)

There is no logical difference between a There is no logical difference between a graphical and a textual rendition of an ontology graphical and a textual rendition of an ontology (or any other model, for that matter).(or any other model, for that matter).

An ontology is represented in a An ontology is represented in a knowledge knowledge representation languagerepresentation language (such as a Semantic (such as a Semantic Web language like RDF/S, DAML+OIL, OWL, or Web language like RDF/S, DAML+OIL, OWL, or in an ontology language that predates the in an ontology language that predates the Semantic Web, such as Semantic Web, such as Ontolingua/KIF/Common Logic, OKBC, CycL, or Ontolingua/KIF/Common Logic, OKBC, CycL, or Prolog). Prolog).

Furthermore, such ontology languages are in Furthermore, such ontology languages are in turn typically based on a particular turn typically based on a particular logiclogic, with , with the logic itself being a language with a syntax the logic itself being a language with a syntax and a semantics. and a semantics.

Sometimes, therefore, we call the Sometimes, therefore, we call the language in which the ontology is language in which the ontology is represented a logic-based language.represented a logic-based language.

So ultimately it does not matter So ultimately it does not matter whether you use a graphical or a whether you use a graphical or a textual rendition of an ontology; both textual rendition of an ontology; both are exactly equivalent.are exactly equivalent.

The important issue is that of the The important issue is that of the power of the underlying language power of the underlying language used to represent the ontology.used to represent the ontology.

Formal Logic --- The Value Formal Logic --- The Value of Ontologiesof Ontologies

High-end ontology languages are backed High-end ontology languages are backed by a rigorous formal logic, which thereby by a rigorous formal logic, which thereby makes the ontology machine-makes the ontology machine-interpretable.interpretable.

Machine-interpretable: the semantic of Machine-interpretable: the semantic of the model is semantically interpretable the model is semantically interpretable by the machine. by the machine. The computer and its software can interpret The computer and its software can interpret

the semantics of the model directly --- the semantics of the model directly --- without direct human involvement.without direct human involvement.

Interaction with Computers Interaction with Computers at the Human Levelat the Human Level

Software supported by ontologies Software supported by ontologies moves up to the human knowledge moves up to the human knowledge /conceptual level/conceptual level

Human do not have to move down to Human do not have to move down to the machine level.the machine level.

Interaction with computers takes place Interaction with computers takes place at our level, not theirs.at our level, not theirs.

This is extremely important point, and This is extremely important point, and it underscores the value of ontologies.it underscores the value of ontologies.

Ontology Definitions: Big O Ontology Definitions: Big O and Little oand Little o

Merriam-Webster Online:Merriam-Webster Online: A branch of metaphysics with the A branch of metaphysics with the

nature and relations of beingnature and relations of being A particular theory about the nature of A particular theory about the nature of

being or the kinds of existentsbeing or the kinds of existents

Big O: Philosophical disciplineBig O: Philosophical discipline Little o: IT engineering disciplineLittle o: IT engineering discipline

IT Definitions of IT Definitions of OntologyOntology

An ontology defines the common words and An ontology defines the common words and concepts (the meaning) used to describe concepts (the meaning) used to describe and represent an area of knowledge.and represent an area of knowledge.

An ontology is an engineering product An ontology is an engineering product consisting of “a specific vocabulary used to consisting of “a specific vocabulary used to describe [a part of] reality, plus a set of describe [a part of] reality, plus a set of explicit assumptions regarding the intended explicit assumptions regarding the intended meaning of that vocabulary (Guarino, 1998) meaning of that vocabulary (Guarino, 1998) --- in other words, the specification of a --- in other words, the specification of a conceptualization (Gruber, 1993).conceptualization (Gruber, 1993).

Two Parts of the first Two Parts of the first DefinitionDefinition

Describing Describing and representing an area and representing an area of knowledgeof knowledge

Defining the common words and Defining the common words and concepts of the descriptionconcepts of the description

DomainDomain A domain is a subject matter are or area of A domain is a subject matter are or area of

knowledgeknowledge Examples: Examples:

MedicineMedicine Automobile repairAutomobile repair Financial planningFinancial planning Machine toolingMachine tooling Business managementBusiness management Physics Physics TextilesTextiles GeopoliticsGeopolitics

Describing an area of Describing an area of knowledgeknowledge

Is the act of expressing, in either written or spoken words, Is the act of expressing, in either written or spoken words, the important points about a specific area of knowledge.the important points about a specific area of knowledge.

For example, in describing automobile repair, we would For example, in describing automobile repair, we would probably talk about the following:probably talk about the following:

The kinds of cars, there are sedans, station wagons, sports The kinds of cars, there are sedans, station wagons, sports cars, luxury cars, compacts, domestic and foreign cars)cars, luxury cars, compacts, domestic and foreign cars)

The types of engines (corresponding perhaps to the type of The types of engines (corresponding perhaps to the type of fuel used: gasoline, diesel, electric-powered, hybrid)fuel used: gasoline, diesel, electric-powered, hybrid)

The particular engines (for example, a 1995-96 V-6 Ford The particular engines (for example, a 1995-96 V-6 Ford Taurus 244/4.0 …)Taurus 244/4.0 …)

The manufacturer (Ford, General Motors, Chevrolet, …)The manufacturer (Ford, General Motors, Chevrolet, …) The things that constitute cars (engines, brake systems, The things that constitute cars (engines, brake systems,

cooling systems, electric systems, suspension, body, and so cooling systems, electric systems, suspension, body, and so on) and their properties (an engine has 4, 6, 8 or 12 on) and their properties (an engine has 4, 6, 8 or 12 cylinders; brake pads have different compositions such as cylinders; brake pads have different compositions such as semi-metallic or nonferrous material) semi-metallic or nonferrous material)

DescriptionDescription When describing an area of knowledge --- a When describing an area of knowledge --- a

domain, we describe the important things in domain, we describe the important things in the domain, their properties, and the the domain, their properties, and the relationships among the things.relationships among the things.

If we were to elaborate our description, we If we were to elaborate our description, we may even include rules about the domain, may even include rules about the domain, such as the following diagnosis rule, which such as the following diagnosis rule, which specifies how to determine what is wrong with specifies how to determine what is wrong with an automobile system in order to repair it:an automobile system in order to repair it: If the car won’t start and it doesn’t turn over, If the car won’t start and it doesn’t turn over,

check and clean the battery connections.check and clean the battery connections.

A Description is or can be A Description is or can be an Ontologyan Ontology

Classes (general things) in the many Classes (general things) in the many domain of interestdomain of interest

Instances (particular things)Instances (particular things) The relationships among those thingsThe relationships among those things The properties (and property values) The properties (and property values)

of those thingsof those things Constraints on those thingsConstraints on those things Rules involving those thingsRules involving those things

Represent a descriptionRepresent a description What does representation mean? Representing What does representation mean? Representing

means that we encode the description in a way means that we encode the description in a way that enables someone to use the description.that enables someone to use the description.

A description consists of A description consists of wordswords and and phrasesphrases in a in a natural language (such as English or Chinese), natural language (such as English or Chinese), that is, vocabulary/terminology and sentences that is, vocabulary/terminology and sentences that combine terminologies to express that combine terminologies to express relationships among the terms.relationships among the terms.

Use vocabulary and terminology as equivalent Use vocabulary and terminology as equivalent and use term for the individual wordand use term for the individual word

Representing an Area of Representing an Area of KnowledgeKnowledge

Representing means that we Representing means that we represent the description using represent the description using termsterms and and sentencessentences..

We define the terms and we combine We define the terms and we combine those defined terms in ways that those defined terms in ways that elaborate elaborate moremore of the of the meaningmeaning about about the area of knowledge.the area of knowledge.

Representing OntologiesRepresenting Ontologies We use the We use the terms of the natural-language terms of the natural-language

descriptiondescription as as labelslabels for the underlying for the underlying concepts --- that is, the meaning of the area of concepts --- that is, the meaning of the area of knowledge consisting of classes, properties, knowledge consisting of classes, properties, and relationships. and relationships.

Typically, we represent or codify the ontology Typically, we represent or codify the ontology in a in a logical, knowledge representation languagelogical, knowledge representation language rather than a natural language, because we rather than a natural language, because we want to represent our description as clearly, want to represent our description as clearly, precisely, and unambiguously as possible, and precisely, and unambiguously as possible, and natural language can be very ambiguous.natural language can be very ambiguous.

Description LogicDescription Logic A knowledge representation formalismA knowledge representation formalism Sometimes called a Sometimes called a

terminological logic, terminological logic, classification logic, classification logic, concept logic, or concept logic, or term subsumption logicterm subsumption logic

Based on a subset of first-order predicate Based on a subset of first-order predicate logic that is logic that is a declarative formalism for the representation and a declarative formalism for the representation and

expression of knowledge and expression of knowledge and sound, tractable reasoning methods founded on a sound, tractable reasoning methods founded on a

firm theoretical (logical) basis. firm theoretical (logical) basis.

Frame-based Knowledge Frame-based Knowledge RepresentationRepresentation

A knowledge representation A knowledge representation formalism for expressing ontological formalism for expressing ontological information derived originally from information derived originally from the AI language called KL-1, which the AI language called KL-1, which itself is one of the earliest itself is one of the earliest formalization of the notion of formalization of the notion of semantic network.semantic network.

Syntax, Structure, Syntax, Structure, Semantics, and PragmaticsSemantics, and Pragmatics

ObjectivesObjectives What makes one ontology better than another, What makes one ontology better than another, What features ontologies (especially those What features ontologies (especially those

characterized as conceptual models and characterized as conceptual models and logical theories) providelogical theories) provide

How they provide themHow they provide them The importance of ontologies from the The importance of ontologies from the

perspective of an IT manager or technical lead perspective of an IT manager or technical lead who must address emerging Semantic Web who must address emerging Semantic Web technologies for incorporation into the technologies for incorporation into the systems and practices of your company’s systems and practices of your company’s infrastructure and their impact on your infrastructure and their impact on your information strategies for the future.information strategies for the future.

SyntaxSyntax A program language, just like a A program language, just like a

natural language like English, has a natural language like English, has a formal syntax.formal syntax.

Syntax is about order and formatSyntax is about order and format In the Web work, XML has a syntaxIn the Web work, XML has a syntax A document that is marked up using A document that is marked up using

XML is either syntactically correct or XML is either syntactically correct or not, with respect to the syntax of XMLnot, with respect to the syntax of XML

StructureStructure

SemanticsSemantics Semantic interpretationSemantic interpretation is the mapping between some structured is the mapping between some structured

subset of data and a model of some set of objects in a domain with subset of data and a model of some set of objects in a domain with respect to the intended meaning of those objects and the respect to the intended meaning of those objects and the relationships between those objects.relationships between those objects.

Typically, the model lies in the mind of the human. We have the Typically, the model lies in the mind of the human. We have the semantics of (some part of) the world in our minds. It is very semantics of (some part of) the world in our minds. It is very structured and interpreted.structured and interpreted.

When we view a textual document, we see symbols on a page and When we view a textual document, we see symbols on a page and interpret those with respect to what they mean in our mental interpret those with respect to what they mean in our mental model; that is, we supply the semantics (meaning).model; that is, we supply the semantics (meaning).

If we wish to assist in the dissemination of the knowledge If we wish to assist in the dissemination of the knowledge embedded in a document, we make that document available to embedded in a document, we make that document available to other human beings, expecting that they will provide their own other human beings, expecting that they will provide their own semantic interpreter (their mental models) and will make sense out semantic interpreter (their mental models) and will make sense out of the symbols on the document pages.of the symbols on the document pages.

So, there is no knowledge in that document without someone or So, there is no knowledge in that document without someone or something interpreting the semantics of that document. something interpreting the semantics of that document.

Semantic interpretation makes knowledge out of otherwise Semantic interpretation makes knowledge out of otherwise meaningless symbols on a page. meaningless symbols on a page.

Automating Semantic Automating Semantic InterpretationInterpretation

To have the computer assist in the dissemination of the To have the computer assist in the dissemination of the knowledge embedded in a document – truly realize the knowledge embedded in a document – truly realize the Semantic Web – we need to at least Semantic Web – we need to at least partially automatepartially automate the semantic interpretation process.the semantic interpretation process.

We need to describe and represent in a computer-usable We need to describe and represent in a computer-usable way a portion of our mental models about specific way a portion of our mental models about specific domains.domains.

Ontologies provide us with that capability.Ontologies provide us with that capability. This is a large part of what the Semantic Web is all about.This is a large part of what the Semantic Web is all about. The software of the future (including intelligent agents, The software of the future (including intelligent agents,

Web services, and so on) will be able to use the Web services, and so on) will be able to use the knowledge encoded in ontologies to at least partially knowledge encoded in ontologies to at least partially understand, to semantically interpret, our Web understand, to semantically interpret, our Web documents and objects.documents and objects.

How are the other Model How are the other Model Types ?Types ?

In formal language theory, one has a syntax and a In formal language theory, one has a syntax and a semantics for the objects of that syntax (vocabulary). semantics for the objects of that syntax (vocabulary). E.g. the syntax of programming languages and E.g. the syntax of programming languages and database structure.database structure.

Ontologies try to Ontologies try to limitlimit the possible formal models of the possible formal models of interpretation (semantics) of those vocabularies to the interpretation (semantics) of those vocabularies to the set of meanings you intend.set of meanings you intend.

None of the other model types with limited semantics None of the other model types with limited semantics --- taxonomies, database schemas, thesauri, and so on --- taxonomies, database schemas, thesauri, and so on --- does that. --- does that.

These model types assume that humans will look at These model types assume that humans will look at the “vocabularies” and magically supply the semantic the “vocabularies” and magically supply the semantic via the built-in human semantic interpreter: via the built-in human semantic interpreter: your mind your mind using your mental models. using your mental models.

Ontologies want to shift some of that “semantic Ontologies want to shift some of that “semantic interpretative burden” to machines and have interpretative burden” to machines and have them eventually mimic our semantics --- that is, them eventually mimic our semantics --- that is, understand what we mean --- and so understand what we mean --- and so bring the bring the machine up to the human, not force the human machine up to the human, not force the human to the machine level.to the machine level.

That is why, for example, we are not still That is why, for example, we are not still programming in assembler. Software programming in assembler. Software engineering and computer science has evolved engineering and computer science has evolved higher-level languages that are much more higher-level languages that are much more aligned with the human semantic/conceptual aligned with the human semantic/conceptual level. Ontologies want to push it even farther. level. Ontologies want to push it even farther.

Machine Semantic Machine Semantic InterpretationInterpretation

We mean that by structuring (and We mean that by structuring (and constraining) in a logical, axiomatic constraining) in a logical, axiomatic language (i.e., a knowledge language (i.e., a knowledge representation language, which we representation language, which we discuss shortly) the symbols humans discuss shortly) the symbols humans supply, the machine will conclude via an supply, the machine will conclude via an inference process (again, built by the inference process (again, built by the human according to logical principles) human according to logical principles) roughly what a human would in roughly what a human would in comparable circumstances.comparable circumstances.

Given a formal vocabulary – alphabet, Given a formal vocabulary – alphabet, terms/symbols (logical and non-logical), terms/symbols (logical and non-logical), and statements/expressions (and, of and statements/expressions (and, of courses, rules by which to form courses, rules by which to form expressions from terms) --- one wants the expressions from terms) --- one wants the formal set of interpretation modelsformal set of interpretation models correlated with the symbols and correlated with the symbols and expressions (i.e., the semantics) to expressions (i.e., the semantics) to approximateapproximate those models that a human those models that a human would identify as those he or she intended. would identify as those he or she intended.

Mapping Between Syntax Mapping Between Syntax and Semanticsand Semantics

The syntax is addressed by proof theoryThe syntax is addressed by proof theory The semantics is addressed by model theoryThe semantics is addressed by model theory Symbols Symbols Rules Rules

Syntax Simple Semantics Complex Semantics More Complex Semantics------------------------------------------------------------------------------------------------------------------------zDLKIL String Constant {“zDLKFL” {“a”, “b”, “c”, …, Infinite “*S*”}12323 Integer Constant {12323} {1, 2, …, n} X Variable X | X Universe of Discourse 4+3 Addition( Integer Type Constant, Integer, Type Constant)Not (X Or Y) Negation Boolean Type (Boolean Type Variable Inclusive Or Boolean Type Variable)

An alphabet and its construction rules for forming words in the alphabet Is mapped to formal objects in the semantic model

A specific example of the A specific example of the mapping between the syntax mapping between the syntax

and semantics of a and semantics of a Programming LanguageProgramming Language

Syntactic objectsSyntactic objects are associated with are associated with their their semantic interpretationssemantic interpretations, each , each of which specifies a formal set-of which specifies a formal set-theoretic domain and a mapping theoretic domain and a mapping function that maps atomic and function that maps atomic and complex syntactic objects to complex syntactic objects to semantic semantic elementselements of the formal domain. of the formal domain.

Machine Semantics vs. Machine Semantics vs. Semantic WebSemantic Web

The machine semantics is very primitive, simple, and The machine semantics is very primitive, simple, and inexpressive with respect to the complex, rich semantics of inexpressive with respect to the complex, rich semantics of humans, but it’s a start and very useful for our information humans, but it’s a start and very useful for our information systems.systems.

The machine is not “aware” and cannot reflect.The machine is not “aware” and cannot reflect. It is a formal process of semantic interpretation that we have It is a formal process of semantic interpretation that we have

described --- everything is still bits.described --- everything is still bits. But by designing a logical knowledge representation system But by designing a logical knowledge representation system

(a language that we then implement) and ontologies (a language that we then implement) and ontologies (expressions in the KR language that are what humans want (expressions in the KR language that are what humans want to model about our world, its entities, and the relationships to model about our world, its entities, and the relationships among these entities), and getting the machine to infer (could among these entities), and getting the machine to infer (could be deduce, induce, adduce, and many other kinds of be deduce, induce, adduce, and many other kinds of reasoning) conclusions that are extremely close to what reasoning) conclusions that are extremely close to what humans would in comparable circumstance (assertions, facts, humans would in comparable circumstance (assertions, facts, and so on), we will have imbued our systems with much more and so on), we will have imbued our systems with much more human-level semantic responses than they have at present. human-level semantic responses than they have at present. We will have a functioning Semantic Web. We will have a functioning Semantic Web.

PragmaticsPragmatics --- sits above semantics and has to do with the intent of the --- sits above semantics and has to do with the intent of the

semantics and actual semantic usage.semantics and actual semantic usage. Become important in the Semantic Web, once intelligent Become important in the Semantic Web, once intelligent

agents begin to use the ontologies.agents begin to use the ontologies. Intelligent agents will have to deal with the pragmatics of Intelligent agents will have to deal with the pragmatics of

ontologiesontologies For example, some agent frameworks, such as that of the For example, some agent frameworks, such as that of the

Foundation of Intelligent Physical Agents (FIPA) standards Foundation of Intelligent Physical Agents (FIPA) standards consortium use an Agent Communication Language that is consortium use an Agent Communication Language that is based on speech act theory, which is a pragmatics theory based on speech act theory, which is a pragmatics theory about human discourse that states that human beings about human discourse that states that human beings express their utterances in certain ways that qualify as express their utterances in certain ways that qualify as acts, and that they have a specific intent for the meaning acts, and that they have a specific intent for the meaning of those utterances.of those utterances.

Intelligent agents are sometimes formalized in a Intelligent agents are sometimes formalized in a framework called BDI, for Belief, Desire, and Intent.framework called BDI, for Belief, Desire, and Intent.