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					2.2.1.3 Requirements Common to Both

• Solution must be
 ▫   Inexpensive
 ▫   Easy to implement
 ▫   Intuitive
 ▫   Evolutionary, not revolutionary
 ▫   Compatible with existing web standards
2.2.2 Satisfy Computer Requirements

• Structured distributed representations to enable
  applications

• Supporting language
2.2.2.1 Structured Representations

• Computers need
 ▫ Consistently structured information collections
 ▫ Inference rules to conduct automated reasoning
 ▫ Representations formal enough to detect
   inconsistencies and errors
 ▫ Network-distributed information to support
   scalability
2.2.2.2 Supporting Language
• Need a tagged markup language to provide
 ▫ Syntax
    Language format rules; open & vendor-neutral
 ▫ Semantics
    Meaning of concepts; formal, finite, & extensible
 ▫ Expressiveness
    Richness; able to express concepts & relationships
    Completeness, correctness, & efficiency (hardest!)
 ▫ Standards
    Common language for all
2.2.3 Compromise

• Must balance need for structure with need for
  human-friendly data representations
 ▫ True natural language processing not yet ready
 ▫ Humans don’t like to process raw structured data
• Proposed solution
 ▫ Humans must augment content with markup
 ▫ Must show an ROI payoff for extra effort
2.3 Semantic Web to the Rescue
• Next evolutionary generation of the web
 ▫ Structured information representations provide
   explicit meaning
 ▫ Information “marked up” according to language
   standards
 ▫ Software provides new functionality by
   interpreting, exchanging, & processing meaning
• Technologies focus on information
  representations tied to explicit meaning
2.3.1 Semantic Web History
• Term coined by Sir Tim Berners-Lee
• US Dept of Defense/DARPA created DAML
 ▫ DARPA Agent Markup Language
 ▫ Helped define critical concepts
• European Union created OIL
 ▫ Ontology Interface Layer
 ▫ Combined with DAML to create DAML+OIL
• W3C built on DAML+OIL to create OWL
 ▫ Web Ontology Language (yes, it’s out of order)
 ▫ First draft approved February 2004
2.3.2 Semantic Web Vision
• Next generation of the web
• Vast object-oriented integrated knowledge base
  that can be accessed and inferenced via
  machine-understandable schemas
• Transparent to the end-user
• Link documents and the information in them
• Leverage the current web infrastructure
• Reduce the cost of performing tasks
2.3.3 Populating the Semantic Web
• Developing representation standards
 ▫   Scope the domain/analyze requirements
 ▫   Define terms and relationships
 ▫   Encode vocabulary & relationships (ontology)
 ▫   Publish representation on servers
• Requires significant up-front effort, but
• Yields greater returns than current solutions
• Cost reduces as reuse grows
2.3.4 Use Cases

• Tactical level functionality
  ▫ Lower-level functions & basic operations
  ▫ Behind the scenes

• Strategic applications
  ▫ Higher-level compositions of tactical features
  ▫ Provide more complex functionality
  ▫ Customer-facing
2.3.4.1 Tactical Services
• Describe distributed information
 ▫ Harvest content, process, & exchange results
• Support queries
 ▫ Answer questions & explain reasoning
• Support searching
 ▫ Find information based on meaning, not keywords
• Support inferring
 ▫ Drawing conclusions from explicit facts
 ▫ Reduces size & complexity of knowledge bases
2.3.4.2 Strategic Applications
• Vertical applications
  ▫ Provide specialized services to a particular domain
  ▫ E-commerce (B2B, B2C)
• Agent software
  ▫ Autonomous; mobile; architecture-independent
  ▫ Find & interpret information, act, report results
• Information management
  ▫ Migrate intelligence from the software to the data
  ▫ Provide new functionality without modifying code
  ▫ Integrate repositories
2.3.5 Appropriate Applications
• Semantic web applications appropriate to:
 ▫ Publish content for both humans and computers
 ▫ Share information without understanding model
 ▫ Inferring new facts & joining information sources
• Characteristics of good candidate domains:
 ▫ Well-understood but dynamic domain
 ▫ Heterogeneous information sources
 ▫ Existing information interchange requirements
• Not suited to binary data, e.g. image processing
2.4 Semantic Web Intro Summary
• Existing challenges
 ▫ Humans want information in readable formats
 ▫ Computers need structured formats
 ▫ Solution must minimize human investment, but
   meet computer needs
• Semantic web is the solution
 ▫ Builds on the existing web
 ▫ Supplies new information representation features
 ▫ Presents information understandable to both
Chapter 3
3 Ontologies Enable the Semantic Web
• Ontology definitions

• Development issues

• Description methods

• Ontology features

• Language issues
3.1 Ontology Definitions
• Historical definition
  ▫ Studies of the science of being, and the nature and
    organization of reality
  ▫ Definitive classifications of objects & their
    relationships
• Other definitions
  ▫   Computer science definition SCOTT
  ▫   Types of ontologies
  ▫   Gruber definition
  ▫   OWL-specific ontology definitions
3.1.1 Computer Science Definition
• Popularized by AI community
• Tbox
  ▫   Terminogical components
  ▫   Equivalent to “schema”
  ▫   Define concepts
  ▫   Semantic Web equivalent
       Ontology
• Abox
  ▫   Assertional components
  ▫   Equivalent to “records”
  ▫   Assert facts
  ▫   Semantic Web equivalent
       Individuals
3.1.2 Types of Ontologies
• Many types
  ▫ Domain ontologies
  ▫ Metadata ontologies (Dublin Core)
  ▫ Method/task ontologies
• Many ways to classify ontologies
  ▫ Formality
  ▫ Regularity
  ▫ Expressiveness
• Simplest ontology: Taxonomy
  ▫ Hierarchy of concepts related with IS-A relationship
  ▫ Can’t express complex relationships
3.1.3 Gruber Definition

• “Formal specification of a
  conceptualization” – T. Gruber

• An ontology is a
  ▫ Formally-described
  ▫ Machine-readable
  ▫ Collection of terms & their
    relationships
  ▫ Expressed in a language
  ▫ Stored in a file
3.1.4 OWL-Specific Ontology Def’n
• Web Ontology Language (OWL) ontology
 ▫ “An OWL-encoded, web-distributed vocabulary of
   declarative formalisms describing a model of a
   domain”
• Domain
 ▫ A specific subject area or area of knowledge
 ▫ Typically the focus of a particular community of
   interest
• Encode a model of the domain, not all of it
3.2 Ontology Features
• Communicate a common understanding of a
  domain

• Declare explicit semantics

• Make expressive statements

• Support sharing of information
3.2.1 Domain Understanding
• Provided by communities of interest
 ▫ Example: restaurant association describes
   relationships between food items
• Ontology formally documents one common
  understanding of a domain
 ▫ Reduces misunderstanding
• Shared and common understanding
  communicated between humans and software
  systems
3.2.2 Explicit Semantics
• Semantics
 ▫ Formal descriptions of terms and relationships
 ▫ Traditionally coded into the software or schema
 ▫ Document concepts using modeling primitives
   and semantic relationships
 ▫ Make assumptions explicit
 ▫ Reduce ambiguity
 ▫ Enable interoperability
• Must be described formally to be processed
3.2.3 Expressiveness
• “Extensiveness” of the ontology
• Must be expressive enough to
 ▫ Represent formal semantics
 ▫ Have reasoning properties to support inferencing
• Support canonical granular representations
• Limited to keep reasoning
 ▫ Decidable
 ▫ Scaleable
3.2.4 Sharing Information

• OWL-compliant software can
 ▫ Manipulate information internally
 ▫ Interoperate with other software
 ▫ Do semantic mapping between information
   sources

• Need to have a shared language and access to
  information
3.3 Ontology Development Issues
• Authoring ontologies
  ▫ Can be developed by anyone, but
  ▫ Better if developed by consensus-based standards
    development groups
  ▫ Vertical ontologies describe a domain
  ▫ Horizontal ontologies span domains and describe
    basic concepts
• Separating ontologies from individuals
  ▫ Usually a good idea
  ▫ Sometimes not possible
• Committing to an ontology
  ▫ Makes applications easier to understand, modify,
    reuse
3.4 Describing Semantics

• Defining information representation building
  blocks

• Describing relationships between building
  blocks

• Describing relationships within building blocks
3.4.1 Building Blocks
• Three basic blocks
  ▫ Class constructs
  ▫ Property constructs
  ▫ Individual constructs

• Together, they describe a
  model of a domain

• Each type requires
  ▫ A computer-understandable
    representation
  ▫ Identifiers for referencing
    these representations
3.4.1.1 Class Construct
• Similar to
  ▫ “Class” in OO terminology
  ▫ “Table” in relational DB terminology
• Group or set of objects with similar properties or
  characteristics (explicit or implicit) in common
• General statements can be made that apply to all
  members of the class
• Examples
  ▫ Food
  ▫ Menu Item
  ▫ Person
3.4.1.2 Property Construct
• Similar to
  ▫ “Accessor method” in OO terminology
  ▫ “Columns” or “fields” in relational DB terms
• Binary association that relates an object
  (instance) to a value
• Examples
  ▫ Price
  ▫ Size
• Unlike OO accessors, properties can be
  associated with multiple unrelated classes!
3.4.1.3 Individuals
• Similar to
  ▫ “Objects” in OO terminology
  ▫ “Rows” or “records” in relational DB terminology
• Represent class object instances in the domain
  ▫ Physical things
  ▫ Virtual concepts
• Unlike objects, Individuals have no functionality
• Examples
  ▫ KnightOwlRestaurant
  ▫ Order456
• Difference b/w individuals & classes not always clear
• Literal values (“1”, “A”) are special case of individuals
3.4.2 Relating Constructs
• Need to describe relationships
  between building blocks

• “is an instance of”
  ▫ Individual to Class

• “has value for”
  ▫ Individual to Property

• Restrictions
  ▫ Between Class and Property
3.4.2.1 Relate Individuals & Classes
• Individuals are members of
  classes

• “Membership” or “is an
  instance of” relationship

• Must be explicitly stated

• Examples
  ▫ “KnightOwlRestaurant” is an
    instance of “Restaurant” class
  ▫ “Mark” is an instance of
    “Person” class
3.4.2.2 Relate Individuals & Properties
• Individuals have attributes
  described by properties

• “has value for” relationship

• Example
  ▫ “KeyLimePie” individual has
    value “$2” for the property
    “price”
  ▫ “Mark” individual has value
    “34” for the property “age”
3.4.2.3 Relate Classes & Properties
• Classes can restrict use of
  Properties in individuals
  ▫ “IsBrotherOf” property range restricted to “Male”s
• Properties can be used to define Classes by
  defining membership in the class
  ▫ Individual is member of class “Boy” iff Individual
    is in “Male” class and “Age” property value <= 18.
• Restrictions can constrain Property values
  ▫ To be of a certain class (range)
  ▫ To only describe particular classes (domain)
3.4.3 Semantic Relationships in Blocks
• Must be able to describe semantic relationships
  within classes, properties, and individuals

•   Synonymy
•   Antonymy
•   Hyponymy
•   Meronymy
3.4.3.1 Synonymy Relation
• Connects concepts with similar meaning
                                                   =
  ▫ equals() in Java – same meaning, different instance
• Stricter form is equivalence (identical)
  ▫ == in Java – same instance
• Class to Class
  ▫ Noodles & Pasta; Soda & Pop
• Instance to Instance
  ▫ Knight Owl Restaurant & franchiseProperty123
• Property to Property
  ▫ Cost & Price
• Allows merging concepts & linking heterogeneous
  knowledge bases
3.4.3.2 Antonymy Relation
•
•
    Opposite meaning
    Stricter form is disjointness
                                              ≠
•   Establishes dichotomy of meaning b/w terms
•   Class to Class
    ▫ Regular Price Menu Item & Sale Price Menu Item
• Instance to Instance
• Property to Property
                                                                Δ
3.4.3.3 Hyponymy Relation
• Specialization & generalization
• Creates taxonomic hierarchies
• Also called
  ▫ “is-a”
  ▫ “inheritance”
  ▫ “subsumption”
• Transitive downward
• Better for permanent relationships
• Class to Class
  ▫ Spaghetti “is-a” Pasta
  ▫ New York Style Pizzeria “is-a” Italian Restaurant “is-a” Restaurant
• Property to Property
  ▫ salePrice “is-a” price
Meronymy/Hyponymy Relation
• Aggregation & composition
• Also called
    ▫ “part-of”
    ▫ “component of”
•   Mereology (part-whole theory)
•   Holonymy (whole-part theory)
•   Closely related to “ownership”
•   Transitive downward
•   Class to Class
    ▫ Meatball “part-of” Spaghetti and Meatballs Dish
    ▫ Fork “part-of” Place Setting
• Individual to individual
    ▫ Drink Order 321 “part-of” Restaurant Bill 789
3.4.4 Semantics Summary
• Building Blocks                      • Relationships

Construct   Description               Functionality     Relationship     Summary

                                                        Individuals to
            A group or set of                           Classes
                                                                         Membership

            individual objects with   Relating blocks   Individuals to
                                                                         Attribute values
            similar characteristics   to each other     Properties
                                                        Classes to
                                                                         Restrictions
                                                        Properties
            Associates attrib/value
            pairs with individuals,                     Synonymy         Similarities

            restricts classes                           Antonymy         Differences
                                      Describing
                                                        Hyponymy         Specialization
            Represents a specific     relationships

            instance object of a                        Meronymy         Part/whole
            class                                       Holonymy         Whole/Part
3.5 Ontology Languages
•   Formal, parseable, & usable by software
•   Define semantics in context-independent way
•   Support some level of logic expression
•   OWL based on:
    ▫ Frame-based systems
    ▫ Description logics
3.5.1 Frame-based Systems
•   Modeling primitives called “frames” (classes)
•   Properties (attributes) are called “slots”
•   Property values are called “fillers”
•   Same slot name usable with different classes
    ▫ Can specify different range & value restrictions
3.5.2 Description Logics (DLs)
• Modeling primitives called “concepts” (classes)
• Properties (attributes) are called “roles”
• DLs also called “terminological logics” or
  “concept languages”
• Balance expressiveness with “decidability”
 ▫ Whether software can reach a conclusion or not
• DL concepts defined by their objects’
  membership constraints
 ▫ Used to automatically derive classification
   taxonomies (hierarchies)
3.5.2 Descriptions Logics cont’d
• DLs can specify
 ▫ Class constructors
 ▫ Property constructors
 ▫ Axioms relating classes & properties
• Allow composite descriptions
 ▫ E.g. restrictions on relationships between objects
• Use first-order logic
• Still decidable
• Support efficient inferencing
3.6 Ontologies Summary
• Various definitions (AI, Gruber, OWL)
• Purposes
 ▫ Communicate specification of domain
 ▫ Declare explicit semantics
 ▫ Support information sharing
• Different types; taxonomies most common
• Divided into Tbox & Abox
 ▫ Tbox: schema, definitions of concepts
 ▫ Abox: records, defintions of individuals/objects
3.6 Ontologies Summary cont’d
• Building blocks
 ▫ Class, Property, Individual
• Relationships between different block types
 ▫ Membership, Attribute Values, Restrictions
• Relationships between same block types
 ▫ Synonomy, Antonymy, Hyponymy, Meronymy,
   Holonymy
• Ontologies described using formal languages
Chapter 4
4 OWL Introduction


• OWL Features

• Semantic Web’s Layered Architecture
4.1 OWL Features
• Primary goals
    ▫ Intuitive for humans, minimal investment
    ▫ Expressive, with explicit semantics for software
• Can define and/or extend ontologies
• Supports scalability (needs some work)
• XML-based annotations
• Makes statements/assertions about classes,
  properties, & individuals
• Additional facts derived via inferencing
4.2 Layered Architecture
        Applications            } Implementation Layer
Ontology Languages (OWL Full,
   OWL DL, and OWL Lite)        } Logical Layer
 RDF Schema      Individuals    } Ontological Primitive Layer
      RDF and RDF/XML           } Basic Relational Language Layer
   XML and XMLS Datatypes       } Transport/Syntax Layer
    URIs and Namespaces         } Symbol/Reference Layer
4.2 Layered Architecture cont’d
• Layers illustrate rough
  dependencies                               Applications
  ▫ Each layer uses features of
                                     Ontology Languages (OWL Full,
    lower layers
                                        OWL DL, and OWL Lite)
• Implementation Layer
  ▫ Provides specific applications   RDF Schema       Individuals
• Logical Layer
  ▫ OWL supports formal                    RDF and RDF/XML
    semantics and reasoning
• Ontological Primitive Layer           XML and XMLS Datatypes
  ▫ RDFS defines vocabulary
  ▫ Individuals defined in RDF           URIs and Namespaces
4.2 Layered Architecture cont’d
• Relational Language Layer
  ▫ RDF’s simple data model &              Applications
    syntax for making statements
                                   Ontology Languages (OWL Full,
  ▫ Serialized as
                                      OWL DL, and OWL Lite)
     RDF/XML or
     N-triples                    RDF Schema       Individuals
• Transport/Syntax Layer
  ▫ Define primitive datatypes           RDF and RDF/XML
  ▫ Provide encoding format
• Symbolic/Reference Layer            XML and XMLS Datatypes
  ▫ Identify and reference
    classes, properties, and
                                       URIs and Namespaces
    individuals
4.3 Technology Support for Layers
• Symbol/Reference Layer
 ▫ Provides identifiers & references to objects
   described in ontologies and instance files
• Transport/Syntax Layer
 ▫ XML used to serialize OWL syntax
 ▫ XMLS defines standard datatypes
• Basic Relational Layer
 ▫ RDF makes statements using Attribute/Value
   pairs to describe objects
4.3 Tech Support for Layers, cont’d
• Ontological Primitive Layer
  ▫ RDFS provides basic vocabulary describing
     Classes and subclasses
     Properties and subproperties
  ▫ Instances & property values specified by RDF & XMLS
• Logical Layer
  ▫ OWL dialects (Full, DL, Lite) enhance RDFS
• Implementation Layer
  ▫ Applications built using OWL
• Additional layers being considered for rules & trust
4.4 OWL Introduction Summary
• Web Ontology Language (OWL)
 ▫ Defined by the W3C
 ▫ Used to make statements about
    Classes
    Properties
    Individuals
 ▫ Designed as a layered architecture built on
    URIs & Namespaces
    XML & XMLS
    RDF & RDFS

				
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