1. Developing Ontologies based on RDF-OWL Semantic Web languages (for information sharing & knowledge representation) :
David George

2. 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).

3. We have the Web: a Global Information Space
Some current Web statistics
Approx. 40m web sites?
Circa billion pages? (Google)
Semantic Web share <
0.001% usable Semantic Web files
% are Ontologies

4. Result: effective query (precision) compromised
Example: Query about Cook
discovering New Zealand?
New Zealand
Cook

5. 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.

6. Descriptions of Ontology
Socrates & Aristotle 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)

7. 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

8. 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.

9. What do we have at present?
Well, we don’t yet have a Semantic Web!

10. But we do have HTML and XML!

11. HTML Document para Document table header text
Dept. of
Computing
mailto:
uclan.ac.uk
CM222
David
George
Name
Room
Subject
Ontology
<h3>
<table>
<tr>
<b>
<link>
<td>
HTML syntax describes layout
Simply a presentation of content
Good for humans; not for machines

12. XML Document Object Model
<person>
<name>
<locn>
<room>
<firstname>
<lastname>
<subject>
< >
<dept>
David
mailto:
uclan.ac.uk
CM222
George
Ontology
Dept. of
Computing
Improve the description for understanding?
XML structures information not page.
Nested elements in tree hierarchy.
Uses syntax to differentiate data.
Universal standard for data exchange.
Good for machines (and humans).

13. How can Semantic Web languages improve our interpretation of information?

14. My Research
Using Semantic Web technologies to demonstrate that RDF-based 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.

15. Geographic Ontology Layers
Water Utility
relief
pipelines
rivers
L.A. Planning
settlements
economic
demographics

16. Cosmological Ontologies

17. RDF Building Block

18. RDF (Resource Description Framework)
W3C standard (2004) for content (resource) description.
RDF is machine-processable; but not for humans, as we’ll see!
RDF parser interpretes common structures to convey semantics.
Built on subject, predicate, object triples [a statement]
A statement may say: <student> <lastname> is <George>
For example:
subject
object
predicate
RDF uses the URI references like < describing s, p, o “resources”
Resources are anything that can be identified on the Web.

19. 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.

20. RDF nodes

21. 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.

22. Semantics through more Metadata
Current Web
Semantic Web? a a
(Kiryakov et al, 2004)

23. Semantic (Shadow) Web a

24. How do we define metadata?
Vocabulary
Ontology
used by
Terms
Metadata
described by
Content
Data
Data/Information described by
Metadata specified by
Vocabularies formalised by
Semantic Web languages

25. OWL (Web Ontology Language)
RDF Schema layer
rdfs:Resource
rdf:Property
rdfs:Class
rdfs:subClassOf
OWL Ontology layer
owl:Highway
owl:PopGroup
owl:ObjectProperty
owl:connectedTo
rdf:type
rdfs:Domain
rdfs:Range
owl:City
owl:Motorway
rdfs:subClassOf
Instance layer
Manchester
M62
owl:connectedTo

26. Role of Ontology in a Semantic WebDB KB

27. Hierarchy of Ontologies
Upper-level
Ontology
Domain-level
Task-level
Application-level
Imprecise – Abstract - Generalised
Precise – Real - Specialised
Upper-level: domain independent, general concept terms and relationships like space, time, matter, objects and events.
Generic domain concepts, e.g. medical, pharmaceutical, travel;
Generic tasks like buying or selling.
specialisations of both domain and task, e..g. flight travel by a specific travel organisation.
[Ontology classification (Guarino, 1998)]

28. “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

29. Mapping Ontology Levels
Thing
Object
Process
Abstract
Concrete
Physical
Information
Upper level
Manned
Exploration
Galaxy
Systems
Planetary
Characteristics
Solar
System
Sun
Cosmic
Microwaves
Application
Solar
Physics
Astromomy
Planetary
Exploration
Cosmology
Nuclear
Fusion
Celestial
Mechanics
Stellar
Systems
Domain

30. Mapping Geographical Layers (1)

31. Mapping Geographical Layers (2)

32. Ontology Mapping One-to-one mapping Clustered Ontologies
Potentially many translating functions
Complexity, scalability & maintenance
No consensus issues
Ontology Mapping
Ontology A
Ontology B
Ontology C
Ontology D
One-to-one mapping
Ontology
A, B, C, D
Top-level
A, B
C, D
Resource ontologies are clustered on the basis of similarity.
General concepts are shared at a higher level.
Flexible and scalable
Ontology A
Ontology B
Ontology C
Ontology D
Ontology
A, B, C, D
Shared Ontology
Ontology D
Ontology B
Ontology A
Ontology C
Clustered Ontologies
Potential consensus problems in agreeing a standard between many users

33. Importing Ontology Structures

34. OWL ontology imports <rdf:RDF
xmlns:owl="
xmlns=
<owl:Ontology rdf:about="">
<owl:imports rdf:resource="
<owl:imports rdf:resource="
</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"/>
</rdf:RDF>

35. 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.

36. 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).

37. Description Logic Expressions
OWL Constructor
Protégé-OWL
Example
Meaning
intersectionOf
C ⊓ D
Person ⊓ Employee
AND
unionOf
C ⊔ D
Male ⊔ Female OR
complementOf ¬C
¬Male
NOT
oneOf
{x y z}
{Fiat BMW Ford}
the set of
someValuesFrom
∃ R C
∃ hasVehicle Car
SOME (from)
allValuesFrom
∀ R C
∀ 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

38. OWL RDF/XML-based Ontology Graph
<owl:Class rdf:ID="PopulationGroup"/>
<owl:Class rdf:about="#Town">
<rdfs:subClassOf rdf:resource="#PopulationGroup"/>
</owl:Class>
<owl:Class rdf:ID="City">
<rdfs:subClassOf rdf:resource="#PopulationGroup"/>
</owl:Class>
<Town rdf:ID="Nelson">
<gridRef rdf:datatype="#string">2E52N</gridRef>
</Town>
<City rdf:ID="Liverpool">
<gridRef rdf:datatype=“#string">3E52N</gridRef>
</City>
<owl:DatatypeProperty rdf:ID="gridRef">
<rdfs:domain rdf:resource="#PopulationGroup"/>
</owl:DatatypeProperty>

39. Specifying Descriptions & Constraints

40. Ontology Development

41. Methodology Cyc Method (Lenat & Guha, 1990) Uschold & King (1995)
TOV Project (Gruninger & Fox, 1995)
Methontology (Fernandez-Lopez et al, 1997)
SWBP & Patterns (Rector, 2004)

42. Application-independent Modelling
Generalisation or
Super class
MDA (Model Driven architecture) using UML-based modelling
(Miller and Mukerji, 2003)

43. Protégé OWL Ontology Editor
(Knublauch, 2003)

44. Using Reasoners in Classification
Before classification: a Tree
After: a Directed Acyclic Graph
(Rector, 2004)

45. Jena-based Ontology Query Interface
(George, 2006)

46. References
BERNERS-LEE, T., HENDLER, J. & LASSILA, O. (2001) The Semantic Web. Scientific American, 284(5), pp
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
GEORGE, D. (2006) Developing Ontologies based on RDF-OWL Semantic Web languages [online]. Available from: [Accessed 13 April 2006].
GRUBER, T. R. (1993) A Translation Approach to Portable Ontology Specifications. Knowledge Acquisition, 5(2), pp
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 IOS Press, pp
KNUBLAUCH, H. (2003) An AI tool for the real world - Knowledge modeling with Protégé [online]. JavaWorld. Available from: [Accessed 23 December 2004].
LASSILA, O. & MCGUINNESS, D. (2001) The Role of Frame-Based Representation on the Semantic Web [online]. Technical Report KSL-01-02, Knowledge Systems Laboratory, Stanford University, CA. Available from: [Accessed 12 July 2005].
LENAT, D. B. (1995) CYC: A Large-Scale Investment in Knowledge Infrastructure. Communications of the ACM, 38(11), pp
MILLER, J. & MUKERJI, J. (2003) Model Driven Architecture [online]. Object Management Group, Inc. Available from: [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: [Accessed 2 November 2005].
STUDER, R., BENJAMINS, V. R. & D.FENSEL (1998) Knowledge Engineering: Principles and Methods. Data & Knowledge Engineering, 25(1-2), pp
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 pp

47. Ontology Distinctions
Information and data exchange
Could simply be delivered by basic specification of classes, properties and instances.
Knowledge Sharing
Describing classes like City (constraints and restrictions) by using logical expressions such as via necessary and sufficient conditions e.g. ∀hasBuilding.Cathedral.

48. Ontology Spectrum Formal hierarchy & increasing expressiveness
No specific hierachy
Glossaries &
Data Dictionaries
Thesauri & Taxonomies
Formal Ontologies
Inferencing
(Lassila & McGuinness, 2001, Uschold & Gruninger, 2004)