Building Ontologies
from the Ground Up
When users set out to model their
professional activity
Mark A. Musen
Professor of Medicine and Computer Science
Stanford University
v 1.00
1
“An ontology is a specification of
a conceptualization” (T. Gruber)
• A conceptualization is the way we think
about a domain
• A specification provides a formal way of
writing it down
2
Porphyry’s depiction of Aristotle’s
Categories
Supreme genus:
Differentiae:
SUBSTANCE
material
immaterial
Subordinate genera:
Differentiae:
BODY
animate
inanimate
Subordinate genera:
LIVING
Differentiae:
sensitive
Proximate genera:
ANIMAL
Differentiae:
rational
Species:
Individuals:
SPIRIT
MINERAL
insensitive
PLANT
irrational
HUMAN
Socrates
Plato
BEAST
Aristotle
…
3
4
Creating Ontologies
in Machine-Processable Form
• Provides a mechanism for developers to codify
salient distinctions about the world or some
application area
• Provides a structure for knowledge bases that can
enable
–
–
–
–
Information retrieval
Information integration
Automated translation
Decision support
5
The New Philosophers
• Categorizing “what exists” in machineunderstandable form
• Providing a structure that enables
– Developers to locate and update relevant
descriptions
– Computers to infer relationships and properties
• Creating new abstractions to facilitate the
creation of this structure
6
7
Part of the CYC Upper Ontology
8
There is a misconception …
• That people building ontologies are all well versed
in metaphysics, computer science, knowledge
representation, and the content domain
• That ontologies in the real world are as “clean” as
SUMO, DOLCE, and other upper-level ontologies
• That most people who are creating ontologies
understand all the ramifications of what they are
doing!
9
Lots of ontology builders are not
very good philosophers
• Nearly always, ontologies are created to address
pressing professional needs
• The people who have the most insight into
professional knowledge may have little
appreciation for metaphysics, principles of
knowledge representation, or computational logic
• There simply aren’t enough good philosophers to
go around
10
Practical Problems
BioInformatics
11
The pressing need to standardize
the names of human genes
12
But the human genome is only
part of the problem …
• Scientist maintain huge databases of gene
sequences and gene expression for a wide range of
“model organisms” (e.g., mouse, rat, yeast, fruit
fly, round worm, slime mold)
• Database entries are annotated with the entries
such as the name of a gene, the function of the
gene, and so on
• How do you ensure uniformity in the nature of
these annotations?
13
Gene Ontology Consortium
• Founded in 1998 as a collaboration among
scientists responsible for developing different
databases of genomic data for model organisms
(fruit fly, yeast, mouse)
• Now, essentially all developers of all modelorganism databases participate
• Goal: To produce a dynamic, controlled
vocabulary that can be applied to all organism
databases even as knowledge of gene and protein
roles in cells is accumulating and changing
14
Gene Ontology (GO)
• Comprises three independent “ontologies”
– molecular function of gene products
– cellular component of gene products
– biological process representing the gene product’s
higher order role.
• Uses these terms as attributes of gene products in the
collaborating databases (gene product associations)
• Allows queries across databases using GO terms,
providing linkage of biological information across species
15
GO = Three Ontologies
• Molecular Function
– elemental activity or task
– example: DNA binding
• Cellular Component
– location or complex
– example: cell nucleus
• Biological Process
– goal or objective within cell
– example: secretion
16
17
GO has been wildly successful!!
• Dozens of biologists around the world
contribute to GO on a regular basis
• The ontology is updated every 30 minutes!
• It’s now impossible to work in most areas of
computational biology without making use
of GO terms
18
But GO has real problems …
• Ontologies are represented in an idiosyncratic format that
is not compatible with standard knowledge-representation
systems
• The format is based on directed acyclic graphs of concepts,
without the general ability to specify machine interpretable
properties of concepts or definitions of concepts
• Because of the informal knowledge-representation system,
lots of errors have crept into GO
– Terms that are duplicated in different places
– Terms with no superclasses
– Uncertain relationships between terms
19
20
Tension in the GO Community
• Biologists around the world with pressing needs to
integrate research databases work together to add
terms to GO nearly continuously
– Using an impoverished, nonstandard knowledgerepresentation system
– Using no standards to assure uniform modeling
conventions from one part of GO to another
• Computer scientists bemoan all this
ad-hoc-ery and condemn GO as a hack that will
become increasingly unusable and unmaintainable
21
A wonderful keynote talk
from the recent meeting
on Standards and
Ontologies for Functional
Genomics
The Capulets and Montagues
A plague on both your houses?
Professor Carole Goble
University of Manchester, UK
Warning:
This talk contains sweeping generalisations
22
 Carole Goble
Prologue
Two households, both alike in dignity,
In fair genomics, where we lay our scene,
(One, comforted by its logic’s rigour,
Claims ontology for the realm of pure,
The other, with blessed scientist’s vigour,
Acts hastily on models that endure),
From ancient grudge break to new mutiny,
When “being” drives a fly-man to blaspheme.
From forth the fatal loins of these two foes
Researchers to unlock the book of life;
Whole misadventured piteous overthrows
Can with their work bury their clans’ strife.
The fruitful passage of their GO-mark'd love,
And the continuance of their studies sage,
Which, united, yield ontologies undreamed-of,
Is now the hours' traffic of our stage;
The which if you with patient ears attend,
What here shall miss, our toil shall strive to mend.
23
Based on an idea by Shakespeare
 Carole Goble
The Montagues
One, comforted by its logic’s rigour,
Claims ontology for the realm of pure
Computer Science, Knowledge engineering, AI
Logic and Languages
Theory
Top down, well-behaved neatness
Generic and lots of toys
Methodologies & patterns
Tools and standards
Technology push
Academic pursuit
24
 Carole Goble
The Capulets
The other, with blessed scientist’s vigour,
Acts hastily on models that endure
Life Scientists
Practice
Bottom up, real-world
Specific and many of them
Methodologies, community practice
Tools and standards
Application pull
Practical pursuit – build ‘n’ use it
25
 Carole Goble
The Philosophers
One, comforted by its logic’s rigour,
Claims ontology for the realm of pure
Philosophers
Theory
Truth
Generic – the one true ontology?
Methodologies, patterns & foundational ontologies
Not really into tools
No push or pull
Academic pursuit
26
 Carole Goble
Endurants, Perdurants,
Being, Substance, Event
Philosophers
KR
Montagues
The end
Mechanism providers
Life
Scientists
Capulets
A means to an end
Content providers
27
 Carole Goble
The Princes of Genomics
Rebellious subjects, enemies to peace,
Profaners of this neighbour-stained steel,-Will they not hear? What, ho! you men, you beasts,
That quench the fire of your pernicious rage
With purple fountains issuing from your veins,
On pain of torture, from those bloody hands
Throw your mistemper'd weapons to the ground,
And hear the sentence of your moved prince.
Three civil brawls, bred of an airy word,
By thee, old Capulet, and Montague,
Have thrice disturb'd the quiet of our streets,
And made genomics's ancient citizens
Cast by their grave beseeming ornaments,
To wield old partisans, in hands as old,
Canker'd with peace, to part your canker'd hate:
28
A tragedy?
As in Romeo and Juliette,
the threats are political
and sociological
29
Creating ontologies has become a
widespread cottage industry
• Professional Societies
– MGED: Microarray Gene Expression Data Society
– HUPO: Human Protein Organization
• Government
– NCI Thesaurus
– NIST: Process Specification Language
• Open Biological Ontologies
– GO
– Three dozen (and growing) other ontologies
– Mostly in DAG-Edit, some in Protégé format
30
31
Government Continues to be a
Major Driving Force
• Highly visible intramural initiatives to create
public ontologies at many agencies, including
NIST, NIH, VA, CDC
• Notable variation in these ontologies’
–
–
–
–
Scope
Representational sophistication
“Openness” of content
Opportunities for peer review
32
NCI Enterprise Vocabulary Services
1997: R. Klausner, Director NCI, wanted a “science
management system”
• Know about everything funded by NCI
• Goals and results – “bench to bedside”
- Thereby improve and speed translation of research
Approach:
1. Create integrative terminology
2. Evolve terminology scope from supporting grants
management to supporting science
3. Build Web-accessible infrastructure – caCORE
33
34
More than 37,000
concepts are
represented with
extremely
detailed
granularity in
many areas
35
Definitions may
include
considerable
detail with
respect to
properties that
establish
relationships
with other
concepts
36
NCI Thesaurus is in Active Use
nciterms.nci.nih.gov
ncicb.nci.nih.gov/core/EVS (more info)
Website: 1500-4000 page hits daily, 14K
unique visitors (2004)
• API: NCICB & external applications
• Fulfills NCI and collaborators’ needs
for controlled vocabulary
• Public domain, open content license
37
NCI Thesaurus Guidelines
• Develop content model (based on Ontylog
description logic from Apelon, Inc.)
• Leverage existing sources as appropriate
– MeSH, VA NDF-RT, MedDRA …
• Develop unique content where needed
– Cancer genes, gene products, cancer diagnoses, drugs,
chemotherapies, molecular abnormalities etc., and
relationships among them
• Link to other standards using URLs where
possible
– OMIM, Swissprot, GO
38
:
NCI uses an Elaborate Process
for Editing and Maintenance
39
The NCI Thesaurus is not
without its problems
• Upper level concepts are sometimes used
inconsistently or not at all
• Textual definitions of concepts may not always
reflect the meaning implied by the concepts’
position in the ontology
• Reliance on a proprietary knowledgerepresentation system
– Prevents the ability to disseminate the ontology freely
– Adds an unfortunate degree of uncertainty to the
semantics
40
Throughout this cottage industry
• Lots of ontology development, principally by
content experts with little training in conceptual
modeling
• Use of development tools and ontology-definition
languages that may be
– Extremely limited in their expressiveness
– Useless for detecting potential errors and guiding
correction
– Nonadherent to recognized standards
– Proprietary and expensive
41
But the world is beginning to change!
• The Montagues do want to get the modeling
right!
• The Capulets do want to see their work used
by others!
• Useful, open tools and standards are now
available that make it hard to justify closed,
proprietary approaches
42
Some signs the world is changing …
• Developers of several overlapping and incompatible
ontologies of anatomy suddenly are trying to understand
why their models do not agree
• Philosopher Barry Smith suddenly is camping out at
biomedical informatics meetings to get the attention of
ontology developers
• NCI is piloting the use of OWL and Protégé to encode and
manage the NCI thesaurus
• MGED and several other biomedical ontologies are being
authored in OWL and Protégé from the beginning
• Downloads of the Protégé system continue to escalate
43
44
Total Protege Registrations
Month/Year
Oct '04
Jul '04
Apr '04
Jan '04
Oct '03
Jul '03
Apr '03
Jan '03
Oct '02
Jul '02
Apr '02
Jan '02
Oct '01
Jul '01
Apr '01
22000
21000
20000
19000
18000
17000
16000
15000
14000
13000
12000
11000
10000
9000
8000
7000
6000
5000
4000
3000
2000
1000
0
Jan '01
Registrations
Through 10/13/04
45
Protégé’s main features
• Simplified editing of ontologies and knowledge bases
• Open-source distribution to encourage development by a
world-wide community of users
• A plug-in architecture that enables developers to add new
features easily
• Support for a wide range of representation formats
–
–
–
–
–
CLIPS/COOL
XML Schema
UML
RDF
OWL
46
Protégé is ecumenical in its support
for formal languages
• Open Knowledge Base Connectivity
Protocol
–
–
–
–
–
CLIPS/COOL
UML
XML Schema
RDF and RDFS
Topic Maps
• Ontology Web Language (OWL)
47
Protégé remains successful
because of its user community
• There are now 89 plug-ins available for use with Protégé
• Collaboration with our users enables rapid debugging and
code fixes
• Some development, such as the creation of extensions to
our basic OWL capabilities, has been a major collaborative
experience
• Annual users groups meetings provide great opportunities
for developers to share strategies, principles, and war
stories
• Members of the international Protégé community are a
huge support base for new users and for fledgling projects
48
The NCI Thesaurus
49
Moving from cottage industry
to the industrial age
• There must be widely available tools that
are open-source, that are easy to use, and
that adhere to knowledge representation
standards: Protégé certainly is a candidate
• There must be a large user user community
of developers who use the tools and who
can provide feedback to one another and to
the core team of tool builders
50
Moving from cottage industry
to the industrial age II
• Government and professional societies must set
expectations regarding the need for appropriate
standards
• Government and professional societies must invest
in educational programs to teach Montagues to
identify with Capulets, and vice versa
• Demonstration projects must communicate to the
potential developers of future ontologies the
strengths and weaknesses of the guidelines, tools,
and languages that facilitated the development
work
51
A thousand flowers are blooming
from every corner of the landscape
• Ontologies are being developed by interested groups from
every sector of academia, industry, and government
• Many of these ontologies have been proven to be
extraordinarily useful to wide communities
• Many of these same ontologies have been shown to be
structurally flawed and of uncertain semantics
• We finally are at the stage where we have tools and
representation languages that can lift us out of the grass
roots to create durable and maintainable ontologies with
rich semantic content
52
An infrastructure is now in place
• The need to build new ontologies in
environmental health, phenotypic expression in
model organisms, developmental biology, and
many, many other domains is getting wide
attention
• We finally have the tools and the languages to do
things right
• Now all we need now is the will, the educational
opportunities, and the community feedback to help
developers at the grass roots to reemerge as
philosophers and princes.
53
Let’s have a happy ending.
54
Editing OWL Ontologies
with Protégé
Holger Knublauch
Stanford University
July 06, 2004
55
This Tutorial
• Introduction to OWL, the Semantic
Web, and the Protégé OWL Plugin
• Theory + Walkthrough
• Also available: Tutorial by Matthew
Horridge (http://www.co-ode.org)
– Similar content but more details on logic
– Other example scenario (Pizzas)
• ... Workshop (this afternoon)
• ... Talks (tomorrow morning)
56
Overview
The Semantic Web and OWL
Basic OWL
Interactive: Classes, Properties
Advanced OWL
Interactive: Class Descriptions
Creating Semantic Web Contents
57
The Semantic Web
Shared ontologies help to exchange data
and meaning between web-based services
58
(Image by Jim Hendler)
Wine Example Scenario
Tell me what wines I
should buy to serve with
each course of the
following menu.
Books Agent
Wine Agent
I recommend
Chardonney or
DryRiesling
Grocery Agent
59
Ontologies in the Semantic Web
• Provide shared data structures to
exchange information between agents
• Can be explicitly used as annotations in
web sites
• Can be used for knowledge-based
services using other web resources
• Can help to structure knowledge to build
domain models (for other purposes)
60
OWL
• Web Ontology Language
• Official W3C Standard since Feb 2004
• Based on predecessors (DAML+OIL)
• A Web Language: Based on RDF(S)
• An Ontology Language: Based on logic
61
OWL Ontologies
• What’s inside an OWL ontology
– Classes + class-hierarchy
– Properties (Slots) / values
– Relations between classes
(inheritance, disjoints, equivalents)
– Restrictions on properties (type, cardinality)
– Characteristics of properties (transitive, …)
– Annotations
– Individuals
• Reasoning tasks: classification,
consistency checking
62
OWL Use Cases
• At least two different user groups
– OWL used as data exchange language
(define interfaces of services and agents)
– OWL used for terminologies or knowledge
models
• OWL DL is the subset of OWL (Full) that
is optimized for reasoning and
knowledge modeling
63
Protégé OWL Plugin
• Extension of Protégé for handling OWL
ontologies
• Project started in April 2003
• Features
– Loading and saving OWL files & databases
– Graphical editors for class expressions
– Access to description logics reasoners
– Powerful platform for hooking in customtailored components
64
Tutorial Scenario
• Semantic Web for Tourism/Traveling
• Goal: Find matching holiday
destinations for a customer
I am looking for a
comfortable destination
with beach access
Tourism Web
65
Scenario Architecture
• A search problem: Match customer’s
expectations with potential destinations
• Required: Web Service that exploits
formal information about the available
destinations
– Accomodation (Hotels, B&B, Camping, ...)
– Activities (Sightseeing, Sports, ...)
66
Tourism Semantic Web
• Open World:
– New hotels are being added
– New activities are offered
• Providers publish their services
dynamically
• Standard format / grounding is needed
→ Tourism Ontology
67
Tourism Semantic Web
OWL
Metadata
(Individuals)
Tourism Ontology
OWL
Metadata
(Individuals)
Destination
Activity
Accomodation
OWL
Metadata
(Individuals)
OWL
Metadata
(Individuals)
Web Services
68
OWL (in Protégé)
• Individuals (e.g., “FourSeasons”)
• Properties
– ObjectProperties (references)
– DatatypeProperties (simple values)
• Classes (e.g., “Hotel”)
69
Individuals
• Represent objects in the domain
• Specific things
• Two names could represent the same
“real-world” individual
Sydney
SydneysOlympicBeach
BondiBeach
70
ObjectProperties
• Link two individuals together
• Relationships (0..n, n..m)
BondiBeach
Sydney
FourSeasons
71
Inverse Properties
• Represent bidirectional relationships
• Adding a value to one property also
adds a value to the inverse property
BondiBeach
Sydney
72
Transitive Properties
• If A is related to B and B is related to C
then A is also related to C
• Often used for part-of relationships
NewSouthWales
Sydney
BondiBeach
hasPart (derived)
73
DatatypeProperties
• Link individuals to primitive values
(integers, floats, strings, booleans etc)
• Often: AnnotationProperties without
formal “meaning”
Sydney
hasSize = 4,500,000
isCapital = true
rdfs:comment = “Don’t miss the opera house”
74
Classes
• Sets of individuals with common
characteristics
• Individuals are instances of at least one
class
Beach
City
Sydney
Cairns
BondiBeach
CurrawongBeach
75
Range and Domain
• Property characteristics
– Domain: “left side of relation” (Destination)
– Range: “right side” (Accomodation)
Accomodation
Destination
BestWestern
Sydney
FourSeasons
76
Domains
• Individuals can only take values of
properties that have matching domain
– “Only Destinations can have
Accomodations”
• Domain can contain multiple classes
• Domain can be undefined:
Property can be used everywhere
77
Superclass Relationships
• Classes can be organized in a hierarchy
• Direct instances of subclass are also
(indirect) instances of superclasses
Cairns
Sydney
Canberra
Coonabarabran
78
Class Relationships
• Classes can overlap arbitrarily
RetireeDestination
City
Cairns
BondiBeach
Sydney
79
Class Disjointness
• All classes could potentially overlap
• In many cases we want to make sure
they don’t share instances
disjointWith
UrbanArea
Sydney
Sydney
City
RuralArea
Woomera
CapeYork
Destination
80
(Create a new OWL project)
81
(Create simple classes)
82
(Create class hierarchy and set disjoints)
83
(Create Contact class with datatype properties)
84
(Edit details of datatype properties)
85
(Create an object property hasContact)
86
(Create an object property with inverse)
87
(Create the remaining classes and properties)
88
Class Descriptions
• Classes can be described by their
logical characteristics
• Descriptions are “anonymous classes”
Things with three star accomodation
RetireeDestination
SanJose
Sydney
BlueMountains
89
Things with sightseeing opportunities
Class Descriptions
• Define the “meaning” of classes
• Anonymous class expressions are used
– “All national parks have campgrounds.”
– “A backpackers destination is a destination
that has budget accomodation and offers
sports or adventure activities.”
• Expressions mostly restrict property
values (OWL Restrictions)
90
Class Descriptions: Why?
• Based on OWL’s Description Logic
support
• Formalize intentions and modeling
decisions (comparable to test cases)
• Make sure that individuals fulfill
conditions
• Tool-supported reasoning
91
Reasoning with Classes
• Tool support for three types of
reasoning exists:
– Consistency checking:
Can a class have any instances?
– Classification:
Is A a subclass of B?
– Instance classification:
Which classes does an individual belong to?
• For Protégé we recommend RACER
(but other tools with DIG support work too)
92
Restrictions (Overview)
• Define a condition for property values
–
–
–
–
–
–
allValuesFrom
someValuesFrom
hasValue
minCardinality
maxCardinality
cardinality
• An anonymous class consisting of all
individuals that fulfill the condition
93
Cardinality Restrictions
• Meaning: The property must have at least/at
most/exactly x values
•
is the shortcut for
and
• Example: A FamilyDestination is a Destination
that has at least one Accomodation and at
least 2 Activities
94
allValuesFrom Restrictions
• Meaning: All values of the property must
be of a certain type
• Warning: Also individuals with no values
fulfill this condition (trivial satisfaction)
• Example: Hiking is a Sport that is only
possible in NationalParks
95
someValuesFrom Restrictions
• Meaning: At least one value of the
property must be of a certain type
• Others may exist as well
• Example: A NationalPark is a RuralArea
that has at least one Campground and
offers at least one Hiking opportunity
96
hasValue Restrictions
• Meaning: At least one of the values of
the property is a certain value
• Similar to someValuesFrom
but
with Individuals and primitive values
• Example: A PartOfSydney is a
Destination where one of the values of
the isPartOf property is Sydney
97
Enumerated Classes
• Consist of exactly the listed individuals
OneStarRating
ThreeStarRating
TwoStarRating
BudgetAccomodation
98
Logical Class Definitions
• Define classes out of other classes
–
–
–
unionOf (or)
intersectionOf (and)
complementOf (not)
• Allow arbitrary nesting of class
descriptions (A and (B or C) and not D)
99
unionOf
• The class of individuals that belong to
class A or class B (or both)
• Example: Adventure or Sports activities
Adventure
Sports
100
intersectionOf
• The class of individuals that belong to
both class A and class B
• Example: A BudgetHotelDestination is a
destination with accomodation that is a
budget accomodation and a hotel
BudgetAccomodation
Hotel
101
Implicit intersectionOf
• When a class is defined by more than
one class description, then it consists of
the intersection of the descriptions
• Example: A luxury hotel is a hotel that is
also an accomodation with 3 stars
Hotel
LuxuryHotel
AccomodationWith3Stars
102
complementOf
• The class of all individuals that do not
belong to a certain class
• Example: A quiet destination is a
destination that is not a family
destination
Destination
QuietDestination (grayed)
FamilyDestination
103
Class Conditions
• Necessary Conditions:
(Primitive / partial classes)
“If we know that something is a X,
then it must fulfill the conditions...”
• Necessary & Sufficient Conditions:
(Defined / complete classes)
“If something fulfills the conditions...,
then it is an X.”
104
Class Conditions (2)
NationalPark
(not everything that fulfills these
conditions is a NationalPark)
QuietDestination
(everything that fulfills these
105
conditions is a QuietDestination)
Classification
NationalPark
• A RuralArea is a
Destination
• A Campground is
BudgetAccomodation
• Hiking is a Sport
• Therefore:
Every NationalPark is a
Backpackers-Destiantion
BackpackersDestination
106
(Other BackpackerDestinations)
Classification (2)
• Input: Asserted class definitions
• Output: Inferred subclass relationships
107
(Create an enumerated class out of individuals)
108
(Create a hasValue restriction)
109
(Create a hasValue restriction)
110
(Create a defined class)
111
(Classify Campground)
112
(Add restrictions to City and Capital)
113
(Create defined class BackpackersDestination)
114
(Create defined class FamilyDestination)
115
(Create defined class QuietDestination)
116
(Create defined class RetireeDestination)
117
(Classification)
118
(Consistency Checking)
119
Visualization with OWLViz
120
OWL Wizards
121
Putting it All Together
•
•
•
•
•
Ontology has been developed
Published on a dedicated web address
Ontology provides standard terminology
Other ontologies can extend it
Users can instantiate the ontology to
provide instances
– specific hotels
– specific activities
122
Ontology Import
• Adds all classes, properties and
individuals from an external OWL
ontology into your project
• Allows to create individuals, subclasses,
or to further restrict imported classes
• Can be used to instantiate an ontology
for the Semantic Web
123
Tourism Semantic Web (2)
OWL
Metadata
(Individuals)
Tourism Ontology
Destination
Activity
Accomodation
Web Services
124
Ontology Import with Protégé
• On the Metadata tab:
– Add namespace, define prefix
– Check “Imported” and reload your project
125
Individuals
126
Individuals
127
OWL File
<?xml version="1.0"?>\
<rdf:RDF
xmlns="http://protege.stanford.edu/plugins/owl/owl-library/heli-bunjee.owl#"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
xmlns:owl="http://www.w3.org/2002/07/owl#"
xmlns:dc="http://purl.org/dc/elements/1.1/"
xmlns:travel="http://protege.stanford.edu/plugins/owl/owl-library/travel.owl#"
xml:base="http://protege.stanford.edu/plugins/owl/owl-library/heli-bunjee.owl">
<owl:Ontology rdf:about="">
<owl:imports rdf:resource="http://protege.stanford.edu/plugins/owl/owl-library/travel.owl"/>
</owl:Ontology>
<owl:Class rdf:ID="HeliBunjeeJumping">
<rdfs:subClassOf rdf:resource="http://protege.stanford.edu/plugins/owl/owl-library/travel.owl#BunjeeJumping"/>
</owl:Class>
<HeliBunjeeJumping rdf:ID="ManicSuperBunjee">
<travel:isPossibleIn>
<rdf:Description rdf:about="http://protege.stanford.edu/plugins/owl/owl-library/travel.owl#Sydney">
<travel:hasActivity rdf:resource="#ManicSuperBunjee"/>
</rdf:Description>
</travel:isPossibleIn>
<travel:hasContact>
<travel:Contact rdf:ID="MSBInc">
<travel:hasEmail rdf:datatype="http://www.w3.org/2001/XMLSchema#string">msb@manicsuperbunjee.com
</travel:hasEmail>
<travel:hasCity rdf:datatype="http://www.w3.org/2001/XMLSchema#string">Sydney</travel:hasCity>
<travel:hasStreet rdf:datatype="http://www.w3.org/2001/XMLSchema#string">Queen Victoria St</travel:hasStreet>
<travel:hasZipCode rdf:datatype="http://www.w3.org/2001/XMLSchema#int">1240</travel:hasZipCode>
</travel:Contact>
</travel:hasContact>
<rdfs:comment rdf:datatype="http://www.w3.org/2001/XMLSchema#string">Manic super bunjee now offers nerve
wrecking jumps from 300 feet right out of a helicopter. Satisfaction guaranteed.</rdfs:comment>
</HeliBunjeeJumping>
</rdf:RDF>
128