Developing Ontologies based on
RDF-OWL Semantic Web languages
(for information sharing & knowledge representation)
[email protected] : 2006-04-13
David George
How do we share data, information & knowledge?
Demonstrated in several dimensions:
•
we share amongst people – using HTML, dynamically with DBs
•
share between database systems – linking DBs:DBs
•
between organisations – exchanging data via XML and XSL transforms.
•
in searches – autonomous & collaborative intelligent software agents.
but . . . sharing data requires understanding of the context of terms
. . . “the semantics of data” using metadata.
Hence Semantic Web would provide shared understanding using
metadata vocabularies (using an ontological approach).
2
We have the Web: a Global Information Space
Some current Web statistics
• Approx. 40m web sites?
• Circa 10-15 billion pages? (Google)
Semantic Web share <http://swoogle.umbc.edu/>
• 0.001% usable Semantic Web files
• 0.00006% are Ontologies
3
Result: effective query (precision) compromised
Example: Query about Cook
discovering New Zealand?
Cook
New Zealand
4
What is the Semantic Web?
•
A project aimed to make web pages machine understandable.
“An extension of the current Web, … information given well-defined meaning,
…enabling computers and people to work in co-operation”
(Berners-Lee et al, 2001)
•
A universal medium for information exchange; where Ontologies are viewed as a
pivotal component in giving meaning or semantics.
•
A solution based on “XML-based” RDF (Resource Description Framework) and
OWL Ontology languages (W3C, 2004).
•
Expected that Semantic Web will have a role in Web Services and Grid
Computing.
5
Descriptions of Ontology
Socrates & Aristotle 400-360 BC - philosophy of being: “Onto”
Some definitions of Ontologies:
• “An ontology is an explicit [formal] specification of a [shared] conceptualisation”
(Gruber, 1993, [Borst, 1997])
• “A logical theory which gives an explicit, partial account of a conceptualisation”
(Guarino & Giaretta, 1995)
• “Conceptualisation refers to abstract model, . . formal refers to machine-readable, .
. and shared reflects notion that ontology captures consensual knowledge shared by
the group”
(Studer et al, 1998)
6
Ontology Examples
“Ontology” covers a range of things
•
Term lists - Catalogues for on-line shopping e.g. Amazon.
•
Dublin Core meta standards for the Web.
•
Linguistic structures – e.g. Thesauri like WordNet.
•
Informal hierarchies or Taxonomies e.g. Yahoo & DMOZ directories.
•
Detailed formal classifications e.g. UNSPSC
•
Formal subsumption hierarchies like Gene Ontology.
•
OWL DL based ontologies
•
Domain-independent or philosophically inspired:
Cyc, Sowa, IEEE SUMO
Glossaries &
Data Dictionaries
Thesauri &
Taxonomies
Formal Ontologies
&
Inferencing
7
Why develop Ontologies?
•
Makes domain descriptions and
assumptions explicit by defining:
– Concepts
– relationships and attributes of
concepts
– constraints on properties
– Instances
•
Enables re-use of terms and
relationships to avoid reinventing
descriptions.
•
Allows domain knowledge to be
separated from operational
information.
•
Helps to manage the information
explosion caused by the Web.
8
What
do yet
we have at
present? Web!
Well, we
don’t
a Semantic
9
But we do have HTML and XML!
10
HTML Document
Document
header
table
<h3>
para
<table>
Dept. of
Computing
<b>
Subject
<tr>
<td>
<tr>
<td>
<td>
text
Ontology
<b>
<link>
email
mailto:
dgeorge@
uclan.ac.uk
<td>
• HTML syntax describes layout
Name
David
George
Room
CM222
• Simply a presentation of content
• Good for humans; not for machines
11
XML Document Object Model
<person>
<name>
<subject>
<locn>
Ontology
<lastname>
David
<firstname>
<dept>
<room>
George
Dept. of
Computing
CM222
<email>
mailto:
dgeorge@
uclan.ac.uk
Improve the
• XML structures information
not page.
description
for
understanding?
• Nested elements in tree hierarchy.
• Uses syntax to differentiate data.
• Universal standard for data exchange.
• Good for machines (and humans).
12
How can Semantic Web languages improve our
interpretation of information?
13
My Research
•
Using Semantic Web technologies to demonstrate that RDFbased language and Ontology can be used to integrate and share
information.
•
Examining the way in which different Ontology structures can be
developed and mapped together.
•
Motivating example will relate to Geographical (or Cosmological)
domain – some early work.
•
Developed an interface to query an Ontology.
•
Some of the following slides relate to these domain concepts.
14
Geographic Ontology Layers
rivers
demographics
economic
Water Utility
L.A. Planning
pipelines
settlements
relief
15
Cosmological Ontologies
16
RDF Building Block
17
RDF (Resource Description Framework)
•
W3C standard (2004) for content (resource) description.
•
RDF is machine-processable; but not for humans, as we’ll see!
•
subject
RDF parser interpretes common structures to convey semantics.
predicate
object
•
Built on subject, predicate, object triples [a statement]
•
A statement may say: <student> <lastname> is <George>
•
For example:
• RDF uses the URI references like <http://someurl>for describing s, p, o “resources”
• Resources are anything that can be identified on the Web.
18
RDF Model
•
Previous RDF example represents a Directed Acyclic
Graph (directed graph with no directed cycles v a tree)
•
statement triple (Subject, predicate, object) allows
nodes to be linked across the Web, e.g. student URL
and computing/semanticweb URL.
http://www.uclan.ac.uk/people/member
19
RDF nodes
20
RDF nodes

RDF is useful for describing data.

Basis for Ontology structures using OWL Web Ontology Language.

RDF graphs form complex directed graphs of linked triples, across the Web.
21
Semantics through more Metadata
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
Current Web
a
a
a
a
a
Semantic Web?
(Kiryakov et al,
22 2004)
Semantic (Shadow) Web
a
a
a
a
a
a
23
How do we define metadata?
Vocabulary
•
Data/Information
•
Metadata
Ontology
used by
described by
specified by
•
Vocabularies
•
Semantic Web languages
Terms
Metadata
formalised by
described by
Content
Data
24
OWL
(Web Ontology Language)
RDF Schema layer
rdfs:Resource
rdfs:subClassOf
rdfs:subClassOf
rdfs:subClassOf
rdfs:Class
rdf:Property
rdfs:Class
OWL Ontology layer
rdf:type
rdf:type
rdf:type
owl:ObjectProperty
owl:PopGroup
rdfs:Domain
rdfs:subClassOf
rdf:type
owl:connectedTo
owl:City
owl:Highway
rdfs:Range
rdfs:subClassOf
owl:Motorway
Instance layer
Manchester
owl:connectedTo
M62
25
Role of Ontology in a Semantic Web
DB
KB
DB
KB
DB
26
Hierarchy of Ontologies
Imprecise – Abstract - Generalised
Upper-level: domain
independent, general
concept terms and
relationships like space,
time, matter, objects and
events.
Upper-level
Ontology
Generic domain concepts,
e.g. medical, pharmaceutical,
travel;
Generic tasks like buying or
selling.
Domain-level
Ontology
Task-level
Ontology
Application-level
Ontology
specialisations of both
domain and task, e..g.
flight travel by a specific
travel organisation.
Precise – Real - Specialised
[Ontology classification (Guarino, 1998)]
27
“Upper-level” Ontologies
(Chandrasekaran et al., 1999)
• Can represent the “starting points” for a field of study.
• Required when working in large groups, i.e. generalisation is required to gain
consensus on agreed terms
28
Mapping Ontology Levels
Thing
Object
Concrete
Physical
Object
Solar
System
Galaxy
Systems
Sun
Planetary
Exploration
Domain
Celestial
Mechanics
Cosmic
Microwaves
Planetary
System
Upper level
Information
Process
Astromomy
Nuclear
Fusion
Stellar
Systems
Abstract
Information
Object
Physical
Process
Solar
Physics
Cosmology
Process
Planetary
Characteristics
Manned
Exploration
Application
29
Mapping Geographical Layers (1)
30
Mapping Geographical Layers (2)
31
• Potentially many
translating functions
• Complexity, scalability &
maintenance
• No consensus issues
Ontology Mapping
Ontology A
Ontology B
Ontology C
Top-level
Ontology
• Resource ontologies are
clustered on the basis of
similarity.
• General concepts are
shared at a higher level.
• Flexible and scalable
Ontology
A, B, C, D
Ontology D
One-to-one mapping
Ontology
A, B
Ontology A
Ontology B
Ontology
A, B, C, D
Ontology C
Ontology
C, D
Ontology A
Ontology D
Shared Ontology
Ontology B
Ontology C
Ontology D
Clustered Ontologies
Potential consensus problems in
agreeing a standard between
many users
32
Importing Ontology Structures
OWL ontology imports
<rdf:RDF
xmlns:owl="http://www.w3.org/2002/07/owl#"
xmlns=http://www.owl-ontologies.com/unnamed.owl#>
<owl:Ontology rdf:about="">
<owl:imports rdf:resource="http://193.61.241.101/union/british.owl"/>
<owl:imports rdf:resource="http://193.61.241.101/union/american.owl"/>
</owl:Ontology>
<owl:Class rdf:ID="RetailOperation">
<rdfs:subClassOf>
<owl:Class rdf:ID="CorporateEntity"/>
</rdfs:subClassOf>
</owl:Class>
<owl:Class rdf:ID="DistributionOperation">
<rdfs:subClassOf rdf:resource="#CorporateEntity"/>
</owl:Class>
</rdf:RDF>
Complexity in mapping Equivalence
Different specifications of descriptions of relationships, when importing ontologies, will
produce differing degrees of mappings, e.g. using equivalence, disjoint, and sub-class
relations. This can superimpose additional complexity, for example recursive relations in
equivalence.
35
OWL Web Ontology Language
•
Three species of OWL:
– OWL Lite – class, object & property terms, inc. inverse, transitive, equivalence,
difference.
– OWL DL – greater expressivity.
• inc. disjoint, min/max cardinality, union, complement, intersection
• complex but computationally decideable.
– OWL Full – most expressive but computationally problematic, e.g. answers not
in finite time.
•
OWL based on “Open World Assumption” (OWA):
(If not exists, will say NO only if can prove false).
•
DBs based on “Closed World Assumption” (CWA):
(If not exists, will say NO).
36
Description Logic Expressions
OWL Constructor
Protégé-OWL
Example
intersectionOf
C
⊓D
Person
unionOf
C
⊔D
Male
⊓ Employee
⊔ Female
Meaning
AND
OR
complementOf
¬C
¬Male
NOT
oneOf
{x y z}
{Fiat BMW Ford}
the set of
someValuesFrom
∃RC
∃ hasVehicle Car
SOME (from)
allValuesFrom
∀RC
∀ hasVehicle Car
ONLY (from)
minCardinality
R≥N
hasVehicle ≥ 3
MIN
maxCardinality
R≤N
hasVehicle ≤ 3
MAX
cardinality
R=N
hasVehicle = 3
EXACTLY
hasValue
R∋I
hasVehicle ∋ Ford
HAS (specific indiv.)
Ref: C,D = Class, I = Individual, R = Restriction
37
OWL RDF/XML-based Ontology Graph
<owl:Class rdf:ID="PopulationGroup"/>
<owl:Class rdf:about="#Town">
<rdfs:subClassOf rdf:resource="#PopulationGroup"/>
</owl:Class>
<Town rdf:ID="Nelson">
<gridRef rdf:datatype="#string">2E52N</gridRef>
</Town>
<owl:Class rdf:ID="City">
<rdfs:subClassOf rdf:resource="#PopulationGroup"/>
</owl:Class>
<City rdf:ID="Liverpool">
<gridRef rdf:datatype=“#string">3E52N</gridRef>
</City>
<owl:DatatypeProperty rdf:ID="gridRef">
<rdfs:domain rdf:resource="#PopulationGroup"/>
</owl:DatatypeProperty>
38
Specifying Descriptions & Constraints
39
Ontology Development
40
Methodology
• Cyc Method (Lenat & Guha, 1990)
• Uschold & King (1995)
• TOV Project (Gruninger & Fox, 1995)
• Methontology (Fernandez-Lopez et al, 1997)
• SWBP & Patterns (Rector, 2004)
41
Application-independent Modelling
Generalisation or
Super class
MDA (Model Driven architecture) using UML-based modelling
(Miller and Mukerji, 2003)
42
Protégé OWL Ontology Editor
(Knublauch, 2003)
43
Using Reasoners in Classification
Before classification: a Tree
After: a Directed Acyclic Graph
(Rector, 2004)
44
Jena-based Ontology Query Interface
(George, 2006)
45
References
BERNERS-LEE, T., HENDLER, J. & LASSILA, O. (2001) The Semantic Web. Scientific American, 284(5), pp. 34-43.
BORST, W. N. (1997) Construction of Engineering Ontologies for Knowledge Sharing and Reuse. Ph.D. Thesis, SIKS - Dutch
Graduate School for Information and Knowledge Systems.
CHANDRASEKARAN, B., JOSEPHSON, J. R. & BENJAMINS, V. R. (1999) What Are Ontologies, and Why Do We Need
Them? IEEE Intelligent Systems, 14(1), pp. 20-26.
GEORGE, D. (2006) Developing Ontologies based on RDF-OWL Semantic Web languages [online]. Available from:
[email protected] [Accessed 13 April 2006].
GRUBER, T. R. (1993) A Translation Approach to Portable Ontology Specifications. Knowledge Acquisition, 5(2), pp. 199-220.
GUARINO, N. (1998) Formal Ontology and Information Systems. In: Proceedings of 1st International Conference on Formal
Ontologies in Information Systems (FOIS'98). Trento, Italy, 6-8 June 1998. IOS Press, pp. 3-15.
KNUBLAUCH, H. (2003) An AI tool for the real world - Knowledge modeling with Protégé [online]. JavaWorld. Available from:
http://www.javaworld.com/javaworld/jw-06-2003/jw-0620-protege_p.html. [Accessed 23 December 2004].
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Report KSL-01-02, Knowledge Systems Laboratory, Stanford University, CA. Available from:
http://www.ep.liu.se/ea/cis/2001/005/cis01005.pdf. [Accessed 12 July 2005].
LENAT, D. B. (1995) CYC: A Large-Scale Investment in Knowledge Infrastructure. Communications of the ACM, 38(11), pp. 3238.
MILLER, J. & MUKERJI, J. (2003) Model Driven Architecture [online]. Object Management Group, Inc. Available from:
http://www.omg.org/docs/omg/03-06-01.pdf. [Accessed 29 September 2005].
RECTOR, A., NOY, N., KNUBLAUCH, H., SCHREIBER, G. & MUSEN, M. (2004) Ontology Design Patterns and Problems:
Practical Ontology Engineering using Protege-OWL [online]. Available from: http://www.cs.man.ac.uk/~rector/tutorials/iswctutorial-2004/ISWC-Tutorial-Best-Practice.pdf. [Accessed 2 November 2005].
STUDER, R., BENJAMINS, V. R. & D.FENSEL (1998) Knowledge Engineering: Principles and Methods. Data & Knowledge
Engineering, 25(1-2), pp. 161-197.
USCHOLD, M. F. & JASPER, R. J. (1999) A Framework for Understanding and Classifying Ontology Applications. In:
Proceedings of Proceedings of the IJCAI-99 workshop on Ontologies and Problem-Solving Methods (KRR5). Stockholm, Sweden,
August 2 1999. pp. 11.1-11.12.
46
Ontology Spectrum
No specific hierachy
Glossaries &
Data Dictionaries
Thesauri & Taxonomies
Formal hierarchy & increasing expressiveness
Formal Ontologies
Inferencing
(Lassila & McGuinness, 2001, Uschold & Gruninger, 2004)
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