SEMANTIC WEB, OWL & PROTÉGÉ

Matthew J Wood

CS 570 – Topics in Artificial Intelligence

Spring 2013

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Today’s Web

Most of today’s Web content is suitable for human consumption Even Web content that is generated automatically from

databases is usually presented without the original structural information found in databases

Typical Web uses today people’s seeking and making use of information, searching for and getting

in touch with other people, reviewing catalogs of online stores and ordering products by filling out forms

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Keyword-Based Search Engines

Current Web activities are not particularly well supported by software tools Except for keyword-based search engines (e.g.

Google, AltaVista, Yahoo) The Web would not have been the huge

success it was, were it not for search engines

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Problems of Keyword-Based Search Engines

High recall, low precision. Low or no recall Results are highly sensitive to vocabulary Results are single Web pages Human involvement is necessary to interpret

and combine results Results of Web searches are not readily

accessible by other software tools

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The Key Problem of Today’s Web

The meaning of Web content is not machine-accessible: lack of semantics

It is simply difficult to distinguish the meaning between these two sentences:

I am a professor of computer science.

I am a professor of computer science, you may think. Well, . . .

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The Semantic Web Approach

Represent Web content in a form that is more easily machine-processable.

Use intelligent techniques to take advantage of these representations.

The Semantic Web will gradually evolve out of the existing Web, it is not a competition to the current WWW

What is Semantic Web? Semantic web is a term used more specifically

to refer to format and technologies that enable it.

It defines the collection, structuring and recovery of linked data that are enabled by the technologies that provide a formal description of concepts, terms, and relationships within a given knowledge domain.

What is Semantic Web? Sematic Web is the extension of the World Wide

Web that enables people to share content beyond the boundaries of applications and websites

Semantic web was term coined by Tim Berners-Lee, the inventor of World Wide Web and the director of the World Wide Web Consortium.

He defines it as “a web of data that can be processed directly and indirectly by machines .

Why Semantic Web?

Semantic Web is about common formats for integration and combination of data from diverse sources.

Secondly, it is about language for recording the relationship data and the real world objects.

Semantic Web Components Resource Description Framework (RDF) RDF Schema (RDFS) Simple Knowledge Organization System

(SKOS) SPARQL, an RDF query language Notation3 (N3) N-Triples Turtle (Terse RDF Triple Language) Web Ontology Language (OWL)

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Explicit Metadata

This representation is far more easily processable by machines

Metadata: data about data Metadata capture part of the meaning of data

Semantic Web does not rely on text-based manipulation, but rather on machine-processable metadata

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On HTML

Web content is currently formatted for human readers rather than programs

HTML is the predominant language in which Web pages are written (directly or using tools)

Vocabulary describes presentation

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An HTML Example<h1>Agilitas Physiotherapy Centre</h1>Welcome to the home page of the Agilitas Physiotherapy Centre. Do you feel pain? Have you had an injury? Let our staff Lisa Davenport,Kelly Townsend (our lovely secretary) and Steve Matthews take careof your body and soul.<h2>Consultation hours</h2>Mon 11am - 7pm<br>Tue 11am - 7pm<br>Wed 3pm - 7pm<br>Thu 11am - 7pm<br>Fri 11am - 3pm<p>But note that we do not offer consultation during the weeks of the <a href=". . .">State Of Origin</a> games.

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Problems with HTML

Humans have no problem with this Machines (software agents) do:

How distinguish therapists from the secretary, How determine exact consultation hours They would have to follow the link to the State Of

Origin games to find when they take place.

XML (eXtended Markup Language)

A better representation of data XML is a flexible text format that is widely

used to structure, store, and transport data. XML is different from HTML because it is not

about displaying data. In XML (differently from HTML) you create

your own tags to annotate data. XML is used to create other languages such

as: XHTML, RSS, RDF, OWL, etc.15

An XML Example

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<bookstore> <book category="COOKING"> <title lang="en">Everyday Italian</title> <author>Giada De Laurentiis</author> <year>2005</year>

<price>30.00</price> </book> <book category="CHILDREN">

<title lang="en">Harry Potter</title> <author>J K. Rowling</author>

<year>2005</year> <price>29.99</price>

</book></bookstore>

RDF (Resource Description Framework)

RDF: a standard for describing resources on the Web The meaning of data is encoded in sets of triples. Triples are “subject, predicate, object” statements. Each element of a triple is identified by a URI. URIs represent both resources and relations. RDF is written in XML RDF is to Semantic Web what HTML was to the Web.

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Harry Potter has as author J. K. Rowling.

An RDF Example

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http://en.wikipedia.org/wiki/J._K._Rowling

http://en.wikipedia.org/wiki/ Harry_Potter

dc:creator

<rdf:RDF xmlns:rdf=http://www.w3.org/1999/02/22-rdf-syntax-ns# xmlns:dc=http://purl.org/dc/elements/1.1/> <rdf:Description rdf:about=“http://en.wikipedia.org/wiki/Harry_Potter”> <dc:creator=“http://en.wikipedia.org/wiki/J._K._Rowling”> </rdf:Description></rdf:RDF>

Ontologies and OWL

An ontology is an explicit description of things and their relations.

OWL serves to write ontologies for the Web. OWL is written in XML and built on top of RDF. You can think of OWL as an object-oriented

language that defines classes, hierarchy of classes, attributes, relations, etc.

OWL is designed to support inference (subsumption and classification)

OWL is more expressive than RDF.19

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Typical Components of Ontologies

Terms denote important concepts (classes of objects) of the domain e.g. professors, staff, students, courses, departments

Relationships between these terms: typically class hierarchies a class C to be a subclass of another class C' if every object in C

is also included in C' e.g. all professors are staff members

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Further Components of Ontologies

Properties: e.g. X teaches Y

Value restrictions e.g. only faculty members can teach courses

Disjointness statements e.g. faculty and general staff are disjoint

Logical relationships between objects e.g. every department must include at least 10 faculty

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Example of a Class Hierarchy

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The Role of Ontologies on the Web

Ontologies provide a shared understanding of a domain: semantic interoperability overcome differences in terminology mappings between ontologies

Ontologies are useful for the organization and navigation of Web sites

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The Role of Ontologies in Web Search

Ontologies are useful for improving the accuracy of Web searches search engines can look for pages that refer to a precise concept

in an ontology

Web searches can exploit generalization/ specialization information If a query fails to find any relevant documents, the search engine

may suggest to the user a more general query. If too many answers are retrieved, the search engine may

suggest to the user some specializations.

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Web Ontology Languages (2)

OWL A richer ontology language relations between classes

e.g., disjointness cardinality

e.g. “exactly one” richer typing of properties characteristics of properties (e.g., symmetry)

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Software Agents

Software agents work autonomously and proactively They evolved out of object oriented and compontent-based

programming

A personal agent on the Semantic Web will: receive some tasks and preferences from the person seek information from Web sources, communicate with other

agents compare information about user requirements and preferences,

make certain choices give answers to the user

A Semantic Web Primer 27

Intelligent Personal Agents

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Semantic Web Agent Technologies

Metadata Identify and extract information from Web sources

Ontologies Web searches, interpret retrieved information Communicate with other agents

Logic Process retrieved information, draw conclusions

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Drawbacks of XML

XML is a universal metalanguage for defining markup It provides a uniform framework for interchange of data

and metadata between applications However, XML does not provide any means of talking

about the semantics (meaning) of data E.g., there is no intended meaning associated with the

nesting of tags It is up to each application to interpret the nesting.

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Requirements for Ontology Languages

Ontology languages allow users to write explicit, formal conceptualizations of domain models

The main requirements are: a well-defined syntax efficient reasoning support a formal semantics sufficient expressive power convenience of expression

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Tradeoff between Expressive Power and Efficient Reasoning Support

The richer the language is, the more inefficient the reasoning support becomes

Sometimes it crosses the border of noncomputability

We need a compromise: A language supported by reasonably efficient

reasoners A language that can express large classes of

ontologies and knowledge.

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Reasoning About Knowledge in Ontology Languages

Class membership If x is an instance of a class C, and C is a

subclass of D, then we can infer that x is an instance of D

Equivalence of classes If class A is equivalent to class B, and class B is

equivalent to class C, then A is equivalent to C, too

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Reasoning About Knowledge in Ontology Languages (2)

Consistency X instance of classes A and B, but A and B are

disjoint This is an indication of an error in the ontology

Classification Certain property-value pairs are a sufficient

condition for membership in a class A; if an individual x satisfies such conditions, we can conclude that x must be an instance of A

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Uses for Reasoning

Reasoning support is important for checking the consistency of the ontology and the knowledge checking for unintended relationships between classes automatically classifying instances in classes

Checks like the preceding ones are valuable for designing large ontologies, where multiple authors are involved integrating and sharing ontologies from various sources

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Reasoning Support for OWL Semantics is a prerequisite for reasoning support Formal semantics and reasoning support are usually

provided by mapping an ontology language to a known logical formalism using automated reasoners that already exist for those

formalisms OWL is (partially) mapped on a description logic, and

makes use of reasoners such as FaCT and RACER Description logics are a subset of predicate logic for

which efficient reasoning support is possible

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Three Species of OWL

W3C’sWeb Ontology Working Group defined OWL as three different sublanguages: OWL Full OWL DL OWL Lite

Each sublanguage geared toward fulfilling different aspects of requirements

OWL DL OWL DL is so named due to its

correspondence with description logic. Description logic (DL) is a family of formal

knowledge representation languages. It is more expressive than propositional logic

It models concepts, roles and individuals, and their relationships

It is used in AI for formal reasoning on the concept of application domain known as Terminological knowledge

Protege Tutorial

What is protege?

Protege is a free, open-source platform to construct domain models and knowledge-based applications with ontologies.

Ontologies range from taxonomies, classifications, database schemas to fully axiomatized theories.

Ontologies are now central to many applications such as scientific knowledge portals, information management and integration systems, electronic commerce and web services

What can you do with Protégé?

Create a new ontology from scratch Download and extend an existing ontology Export ontologies in a variety of formats

OWL RDF XML Schema

How it works

Objects in the domain are expressed through a series of interrelated classes

Class hierarchy is similar to that used by object-oriented languages such as Java Superclasses Subclasses Sibling Classes Ancestor Classes, etc.

Heavy reliance on inheritance Unlike Java, Protégé supports multiple inheritance

Individuals and Properties

Individual domain objects are expressed as class members

Class members have object properties that relate them to members of the same class or other classes

Also have data properties Expressed by data type – integer, String, etc. If particular data property does not fit a predefined

data type it can be entered as text Ex. Members of the class Soccer_Players have ‘has

fanpage’ data property that is expressed as a URL

Annotations

Provide definitions and comments on the ontology and its contents

Can provide annotations for the entire ontology, a specific class, a member of a class, etc.

How It Works (cont.)

Using Protégé in conjunction with GraphViz software allows users to view the ontology as a semantic network

Protégé relies on semantic reasoners to implement description logic

Semantic Reasoners

Inference engine for description logics Inference process is carried out via forward

and backward chaining Protégé supports various reasoners such as

FaCT++ and HermiT

FaCT++

Semantic reasoner used for OWL DL ontologies

Implemented in C++ and supported by Protege

Converts the KB into an internal representation using various optimization techniques

Uses dependency directed backtracking to check satisfiability of the KB

Install Protege

Go to http://protege.stanford.edu/doc/owl/getting-started.html to download protege (version 3.x)

Protege OWL editor is built with the full installation of protege platform. During the install process, choose the “Basic+OWL” option.

For more details: http://protege.stanford.edu/doc/owl/getting-started.html

Protege There are two main ways of modelling ontologies:

Frame-based OWL

Each has its own user interface Protege Frames editor: enables users to build and populate ontologies that

are frame-based, in accordance with OKBC (Open Knowledge Base Connectivity Protocol). Classes Slots for properties and relationships Instances for class

Protege OWL editor: enables users to build ontology for the Semantic Web, in particular to OWL Classes Properties Instances reasoning

Building an OWL Ontology

E2: Create a new OWL project Start protege File – New Project – OWL/RDF files – Ontology

URI (http://www.pizza.com/ontologies/pizza.owl) – OWL DL – Properties View

A new empty Protege-OWL project has been created.

Save it in your local file as pizza.owl

Named Classes

Go to OWL Classes tab The empty class tree contains one class called owl:Thing,

which is superclass of everything. E3: Create subclasses Pizza, PizzaTopping and

PizzaBase. They are subclasses of owl:Thing. Naming convention

no special naming convention consistency

Disjoint classes

E4: How to say that Pizza, PizzaTopping and PizzaBase classes are disjoint.

1. Select the class Pizza

2. Press “add siblings” button on the disjoint classes widget

3. Add PizzaBase and PizzaTopping

4. Select the class PizzaTopping,

5. Add Pizza and PizzaBase to the disjoint class

E5: Create group of classes

Create ThinAndCrisyBase and DeepPanBase as the subclasses of PizzaBase, and each of them are disjointed.

Select PizzaBase, right click the mouse, select “create subclasses”

Follow the wizard to create these two disjoint classes.

It will save lots of time when there is need to create lots of disjoint classes.

E6: Create some subclasses of PizzaTopping

Select PizzaTopping, Create subclaesses as MeatTopping, VegetableTopping,

CheeseTopping and SeafoodTopping. Make sure that these classes are disjoint to each other.

Select the class MeatTopping, Add disjoint subclasses: SpicyBeefTopping,

PepperoniTopping, SalamiTopping and HamTopping

Select VegetableTopping: Add disjoint subclasses: TomatoTopping, OliveTopping,

MushroomTopping, PepperTopping, OnionTopping, CaperTopping

E6: Creating disjoint subclasses

Select PepperTopping Add disjoint subclasses: RedPepperTopping,

GreenPepperTopping, JalapenoPepperTopping Select CheeseTopping

Add disjoint subclasses: MozzarellaTopping, ParmezanTopping

Select SeafoodTopping Add disjoint subclasses: TunaTopping,

AnchovyTopping and PrawnTopping

OWL Properties

OWL Properties represent relationships between two objects.

There are two main properties: Object properties: link object to object datatype properties: link object to XML Schema

datatype or rdf:literal OWL has another property – Annotation

properties, to be used to add annotation information to classes, individuals, and properties

E7: Create an object property

Switch to the “Properties” tab, Use “Create Object Property” button to create

a new object property. Rename it to hasIngredient

E8: Creating sub-properties

Select hasIngredient property Add hasTopping and hasBase as the

subproperties

Inverse Properties

Each object property may have a corresponding inverse property.

If some property links individual a to individual b, then its inverse property will link individual b to individual a.

E9: Create inverse properties Create a new object property called isIngredientOf

Press “Set inverse property” button, Select “hasIngredient” Then the inverse relation has been set up.

Select hasBase Create the isBaseOf as the inverse property of hasBase isBaseOf is the subproperty of isIngredientOf

Select hasTopping create isToppingOf as the inverse property. isToppingOf is the subproperty

of isIngredientOf

Functional Properties

If a property is functional, for a given individual, there can only be at most one individual to be related via this property. For a given domain, range must be unique

Functional properties are also known as single valued properties.

Inverse Functional Properties

If a property is inverse functional, then its inverse property is functional. For a given range, domain must be unique.

Functional vs. inverse functional properties

FunctionalProperty vs InverseFunctionalProperty

domain range example

Functional

Property

For a given domain

Range is unique

hasFather: A hasFather B, A hasFather C B=C

InverseFunctionalProperty

Domain is unique

For a given range

hasID: A hasID B, C hasID B A=C

Transitive Properties

If a property is transitive, and the property related individual a to individual b, and also individual b to individual c, then we can infer that individual a is related to individual c via property P.

Symmetric Properties

If a property P is symmetric, and the property relates individual a to individual b, then individual b is also related to individual a via property P.

E10: Make the hasIngredient property transitive

Select the hasIngredient property Tick the transitive tick box Select the isIngredientOf property, make sure

that the transitive tick box is ticked.

E11: Make the hasBase property functional

Select the hasBase property Tick the “functional” tick box OWL-DL does not allow datatype properties

to be transitive, symmetric or have inverse properties.

Property domains and ranges

Properties link individuals from the domain to individuals from the range.

OWL uses domain and range as axioms in reasoning.

E12: Specify the range of hasTopping

Select hasTopping Press range button Select PizzaTopping Press OK button PizzaTopping should be displayed in the range

list. When multiple classes are added to the

range, they represent the union of all classes.

E13: Specify Pizza as the domain of the hasTopping property

Select hasTopping property Press add domain button Select Pizza Press OK Pizza is displayed in the domain list.

When multiple classes are added as domain, they represent as the union of these classes.

E14: Specify the domain and range for the isToppingOf property

Select the isToppingOf property Set the domain of the isToppingOf property to

PizzaTopping Set the range of the isToppingOf property to

Pizza.

E15: Specify the domain and range for the hasBase property and its inverse property isBaseOf

Select the hasBase property Specify the domain as Pizza Specify the range as PizzaBase

Select the isBaseOf property Specify the domain as PizzaBase Specify the range as Pizza

Property restrictions

In OWL, properties are used to create restrictions. Restrictions are used to restrict the individuals that

belong to a class Three restrictions:

Quantifier restrictions Existential quantifier ( ) Universal quantifier ( )

Cardinality restrictions hasValue restrictions

E16: Add a restriction to Pizza

Add a restriction to Pizza that specifies a Pizza must have a PizzaBase Select Pizza Select Necessary header to create a necessary

condition Select create a restriction wizard

Select hasBase as restricted property Select someValueFrom as restriction Put PizzaBase into the filler

Add a restriction to Pizza

E18: Creating different kinds of Pizzas

Create a subclass of Pizza called NamedPizza, and a subclass of NamedPizza called MargheritaPizza.

Add comment to MargheritaPizza: A pizza that only has Mozarella and Tomato toppings

E19: Adding restrictions to MargheritaPizza

To specify that MargheritaPizza has at least one MozzarellaTopping. Select MargheritaPizza Go to “Asserted Conditions”, create new restriction. Select someValueFrom Select hasTopping as the property to be restricted. Enter MozzarellaTopping as the filler Press OK button

E20: Adding restrictions to MargheritaPizza

To specify that MargheritaPizza has at least one TomatoTopping. Select MargheritaPizza Go to “Asserted Conditions”, create new restriction. Select someValueFrom Select hasTopping as the property to be restricted. Enter TomatoTopping as the filler Press OK button

E21: Create AmericanPizza

Create AmericanPizza with toppings of pepperoni, mozzarella and tomato.

Through cloning and modifying the description of MargheritaPizza. Select MargheritaPizza Select create clone Add additional restriction to AmericanaPizza

Adding PepperoniTopping Press OK.

E22: Create an AmericanHotPizza and a SohoPizza

An AmericanHotPizza is almost the same as an AmericanaPizza, but has JalapenoPepperTopping on it.

A SohoPizza is almost the same as a MargheritaPizza, but has additional OliveTopping and ParmezanTopping

E23: Make subclasses of NamedPizza disjoint from each other

Select MargheritaPizza Press “add all siblings” button on the

“Disjoints widget” to make the pizzas disjoint from each other.

Using a reasoner Ontology described in OWL-DL can be processed by a reasoner.

Go to owl—preference, to make sure that OWL-DL is selected. The main services offered by a reasoner is to test whether or not

one class is a subclass of another class. By performing such tests on all of the classes, it is possible for a

reasoner to compute the inferred ontology class hierarchy. Another reasoning service is consistency checking – to check

whether or not it is possible for the class to have any instances. A class is deemed to be inconsistent if it cannot possibly have

any instances.

Using Racer

In order to reason over the ontology in Protege-OWL, a DIG compliant reasoner should be installed and started.

In this example, we use Racer, Download at:

http://www.racer-systems.com/products/download/index.phtml

Double click RacerPro to start Racer.

Invoking the reasoner Having started Racer, the ontology can be sent to the reasoner

to automatically compute the classification hierarchy, and also check the logical consistency of the ontology.

In Protege, the manually constructed class hierarchy is called the asserted hierarchy. The automatically computed by the reasoner is called the inferred hierarchy.

Go to OWL – classify taxonomy – to invoke the reasoner If a class has been reclassified, then the class name will appear

in a blue color in the inferred hierarchy. Go to OWL – Check consistency – to invoke the reasoner

If a class has been found to be inconsistent, it’s icon will be circled in red color.

Computing the inferred class hierarchy is also known as classifying the ontology.

Invoke the reasoner

E24: Inconsistent classes

In order to demonstrate the use of the reasoner to detect inconsistencies in the ontology, we will create a class ProbeInconsistentTopping, Which is the subclass of CheeseTopping Select ProbeInconsistentTopping, go to asserted condition to

add named classes, select VegetableTopping and then press OK.

Go to OWL – check consistency

E25: Classify the ontology again To see ProbeInconsistentTopping is

inconsistent.

E26: Remove the disjoint statement

Between CheeseTopping and VegetableTopping to see what happens Select CheeseTopping Go to Disjoint part Select VegetableTopping, right click and “Delete

the selected row”. Classify taxonomy The inconsistency no longer exists.

E27: Fix the ontology

By making CheeseTopping and VegetableTopping disjoint from each other.

SPORTS ONTOLOGICAL EXTENSION IN PROTÉGÉ

Project #1

Used Protégé to extend a general sports ontology (sptcsem.owl)

Focused on adding classes, individuals, and properties related to the game of soccer

Used Fact++ for DL queries