1. Department of Artificial Intelligence
Ontologies
Charlie Abela
Department of Artificial Intelligence

2. Last Lecture
Introduction to Description logics and its relevance to knowledge representation
Problems?
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3. Lecture Outline What is an Ontology? Components Ontology development
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4. What is an Ontology?
An ontology is an explicit specification of a conceptualization (Gruber 1993)
Ontologies are defined as a formal specification of a shared conceptualization (Borst 1997)
Studer explains:
Conceptualization: refers to an abstract model of some phenomenon in the world identified by concepts of that phenomenon
Explicit: both type of concepts and constraints on these concepts are explicitly defined
Formal: refers to the fact that the ontology should be machine-readable
Shared: reflects the notion that the ontology captures consensual knowledge, accepted by a group.
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5. Why develop an Ontology?
To share common understanding of the structure of information among people or software agents
facilitating information extraction and aggregation by software agents
To enable reuse of domain knowledge
reuse, extend and integrate existing ontologies
To make domain assumptions explicit
explicit definition of such knowledge (instead of hard-coding it) makes it easier to change
To separate domain knowledge from operational knowledge
E.g. use different ontologies but same algorithm (to solve different problems)
To analyse domain knowledge
possible once a declarative specification of the terms is available (valuable when reusing and extending ontologies)
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6. Existing Domain-Specific Ontologies
Medical domain: Cancer ontology from the National Cancer Institute in the United States
Cultural domain:
Art and Architecture Thesaurus (AAT) with 125,000 terms in the cultural domain
Union List of Artist Names (ULAN), with 220,000 entries on artists
Iconclass vocabulary of 28,000 terms for describing cultural images
Geographical domain: Getty Thesaurus of Geographic Names (TGN), containing over 1 million entries
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7. Integrated Vocabularies
Merge independently developed vocabularies into a single large resource
E.g. Unified Medical Language System integrating 100 biomedical vocabularies
The UMLS meta-thesaurus contains 750,000 concepts, with over 10 million links between them
The semantics of a resource that integrates many independently developed vocabularies is rather low
But very useful in many applications as starting point
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8. Upper-Level Ontologies
Some attempts have been made to define very generally applicable ontologies
Describe very general concepts that are common across the domains
Provide general notions under which all the terms in existing ontologies should be linked
OpenCyc, with 60,000 assertions on 6,000 concepts
DOLCE: a Descriptive Ontology for Linguistic and Cognitive Engineering
Suggested Upper Merged Ontology (SUMO)
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9. Linguistic Resources
Describes semantic constructs rather then model specific domains
Used mostly in nlp
They are bound to the semantics of grammatical units (words, adjectives etc)
E.g. WordNet (focus on word meaning)
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10. Main Components of an Ontology
Concepts or Classes
related to a domain of discourse
Slots (Roles or Properties)
properties of concepts describing various features and attributes of concepts
Facets (Slot/Property Restrictions )
restrictions on slots/properties
Instances
Individual instances of classes (constitute a knowledge base)
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11. Classes Represent concepts in a particular domain
E.g. locations (cities, villages), lodgings (hotels, hostels), means of transportation (planes, trains)
Classes are organised in taxonomies through which inheritance mechanisms can be applied
E.g. a taxonomy of travel packages (economy, business)
Classes can represent both abstract and specific concepts
abstract (beliefs, intentions)
specific (people, computers)
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12. Properties or Slots Describe properties of classes and instances
E.g subClassOf(Hotel,Lodging) for classification
They represent binary relations
E.g. arrivalPlace(flight, location)
Slots are inherited by subclasses
E.g. arrivalPlace slot can be applied to the class Travel but it also applies to its subClass Flight
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13. Facets or Property Restrictions
Facets describe the value type, allowed values, cardinality, domain and range of slots
slot:flightNumber
domain: Flight
range: string
cardinality:1
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14. Building Ontologies No one correct way to model a domain
Alternatives will always exist
Best solution depends on application and anticipated extensions
Development is an iterative process
Start with a rough draft, then revise and refine, filling details along the way
Concepts should be close to objects and relationships in the domain
Most likely to be nouns (objects) or verbs (relationships) in sentences that describe the domain
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15. Ontology Development Noy, McGuinness method: 7 steps identified
Determine the domain and scope of the ontology
Consider reusing existing ontologies
List important terms in the ontology
Define the classes and class hierarchy
Define the properties of the classes- slots
Define the facets for the slots
Create Instances
8th step: Check for anomalies
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16. 1. Determine the domain and scope
Answer basic questions to identify scope and intended use.
What is the domain to be covered?
What is the use of the ontology?
Which terms are relevant?
Define a set of competency questions that the ontology will have to answer.
These questions are just a sketch and need not be exhaustive
Which characteristics of travelling need to be considered?
Which types of lodging to consider?
Which transportation methods need to be considered?
E.g.
domain: travelling,
purpose: knowledge used by travel agencies or tourists catalogue system
relevant terms: places, lodging, types of lodging, transport, types of transport
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17. 2. Consider Reuse Look for libraries of reusable ontologies
Search the Web or use Swoogle
Some libraries:
Ontolingua ontology library
Open Directory Project (
Almost always worth checking if its possible to refine and extend existing ontologies.
There is almost always an ontology available from a third party that provides at least a useful starting point for our own ontology
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18. 3. Enumerate important terms
Elaborate the list of important terms
List the properties related to these terms
Initially the list should be comprehensive without caring about
overlap between concepts
relations among terms
properties related to concepts
whether concepts are classes or slots
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19. 4. Define classes and class hierarchy
Several approaches
Top-down: start with the most general concepts
Bottom-up: start with the most specific
A combination: define the more important concepts and then generalise and specialise as required.
Organise classes into a hierarchal taxonomy by following the rule:
If class A is a superclass of class B, then every instance of B is also an instance of A
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20. Classes in the Travelling Domain
Lodging
Hotel
LuxuryHotel
Transport
Train
Class Hierarchy
Hotel is-a-kind of Lodging
LuxuryHotel is-a-kind of Hotel
Train is-a-kind of Transport
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21. 5. Define Properties
After identifying classes from the list of terms and defined them formally, choose the terms that define properties of these classes.
These properties become attached to these classes
Since properties are inherited by subclasses, these should be attached to the most general class.
Examples of properties:
departureDate, arrivalDate: attached to class Travel
flightNumber: attached to class Airplane
hasRating, hasLocation: attached to class Lodging
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22. 6. Define Facets (Restrictions)
Several common facets can be defined
Property cardinality
Only one departureDate (cardinality =1)
Property-value type
flightNumber is of type string
Domain and range properties
transportMeans: Travel (domain), Transport(range)
singleFare: Travel(domain), currencyQuantity( range)
hasRating: Lodging(domain), Rating(range)
hasLocation: Lodging(domain), Location(range)
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23. 7. Create Instances Defining an individual instance requires
Choosing a class
Creating an individual instance of that class
Filling in the property values
A flight instance
KM2561 is a type of flight
Fare currency for KM2561 is in Euros
Departure date for KM2561 is
Destination of KM2561 is Madrid
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24. 8. Check Anomalies Detect possible inconsistencies
In ontology or ontology+ instances
Examples of common inconsistencies
incompatible domain and range definitions for transitive, symmetric, or inverse properties
cardinality properties
requirements on property values can conflict with domain and range restrictions
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25. Ontology Mapping Problem:
Many Ontologies available that describe the same domain
Different Ontologies emphasis different aspects
Different vocabulary for same Classes/Properties
Different degree of detail
Applications that are based on different Ontologies need to collaborate
We need to translate one Ontology into another one!
Manual mapping has a high precision, but it does not scale!
Large Ontologies require an automatic mapping.
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26. Ontology Mapping II
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27. Ontology Mapping - Techniques
tag name mapping: names of XML tags are mapped
Problem: Same expressions for different Classes/Properties
element content: searches for same/similar content in two Instances
Problem: Text in elements has to be unique and expressive
structure analyzer: considers XML document as graph, tries to map subgraphs with each other
Problem: Is structure sufficient to extract the semantics?
Overall problem: Precision
Combination of techniques possible, increases precision.
Other approaches to increase precision:
Semi-automatic mapping: Tools that support an efficient manual mapping by recommending mapping candidates.
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28. Ontology Mapping –Related Techniques
Ontology merging: Join two different Ontologies into one coherent Ontology. Final Ontology consists of the full, unchanged source Ontologies.
Ontology alignment: Generate links between two Ontologies (with usually complementary domains)
Ontology integration: Integrate two or more Ontologies into one (with some changes)
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29. Merging Example
Two ontologies, one about the ear and another about the lungs are merged.
Concepts from the second ontology are added to the preferred ontology one at a time.
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30. Suggested Reading Jena RDF tutorial,
Ontology languages for the Semantic Web,
Ontology development 101: A guide to creating your first ontology,
OWL guide,
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31. Next Lecture
OWL: Web Ontology Language
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32. Ontology Building Methodologies
Uschold and King method: uses the following process
Identify the purpose of the ontology
Build it
Evaluate it
Document it
Gruninger and Fox method:uses the following process
Identify the main scenarios (ie. possible applications for the ontology)
Identify a set of competency questions (to determine scope)
Use answers to extract the main concepts, properties, relations and axioms
Formally express the knowledge in first order logic
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33. Ontology Building Methodologies
Methontology: Based on the IEEE standard for software development
Three categories of activities are advised
Ontology management activities, such as
Scheduling, Control, Quality assurance
Ontology development oriented activities
Pre-development: environment study, feasibility study
Development: specification, conceptualisation, formalisation and implementation
Post development: Maintenance
Ontology support activities
Knowledge acquisition, Evaluation, Integration, Merging, Alignment
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34. Ontology Languages Ontolingua and KIF (Knowledge Interchange Format)
Ontolingua is an ontology language based on KIF and Frame Ontology
KIF is based on first-order predicate calculus
OCML (Operational Conceptual Modelling Language)
Frame-based language with Lisp-like syntax
Provides primitives to define classes, relations, functions, axioms and instances
Provides also primitives to define rules (for backward and forward chaining)
F-Logic (Frame Logic)
Integrates features from: OO, frame-based KR and first order logic
Features: inheritance, polymorphic types, query methods and encapsulation
Can be combined with HiLog to improve ontology reasoning
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