1. Part 5: Ontologies

2. Logic and Ontologies
Up to now we have studied Logic Languages for Knowledge Representation and Reasoning:
in both static and dynamic domains
with possibly incomplete knowledge and nonmonotonic reasoning
interacting with the environment and completing the knowledge, possibly contracting previous assumptios
All of this is parametric with a set of predicates and a set of objects
The meaning of a theory depends, and is build on top of, the meaning of the predicates and objects

3. Choice of predicates
We want to represent that trailer trucks have 18 wheels. In 1st order logics:
 x trailerTruck(x)  hasEighteenWheels(x) or
 x trailerTruck(x)  numberOfWheels(x,18) or
x ((truck(x) y(trailer(y)  part(x,y))) 
s (set(s)  count(s,18) 
w (member(w,s)  wheel(w)  part(x,w)))
The choice depends on which predicates are available
For understanding (and sharing) the represented knowledge it is crucial that the meaning of predicates (and also of object) is formally established

4. Ontologies
Ontologies establish a formal specification of the concepts used in representing knowledge
Ontology: originates from philosophy as a branch of metaphysics
Ontologia studies the nature of existence
Defines what exists and the relation between existing concepts (in a given domain)
Sought universal categories for classifying everything that exists

5. An Ontology
An ontology, is a catalog of the types of things that are assumed to exist in a domain.
The types in an ontology represent the predicates, word senses, or concept and relation types of the language when used to discuss topics in the domain.
Logic says nothing about anything, but the combination of logic with an ontology provides a language that can express relationships about the entities in the domain of interest.
Up to now we have implicitly assumed the ontology
I assumed that you understand the meaning of predicates and objects involved in examples

6. Aristotle’s Ontology Being Substance Accident Property Relation
Inherence
Directedness
Containment
Movement
Intermediacy
Quality
Quantity
Activity
Passivity
Having
Situated
Spatial
Temporal

7. The Ontology Effort to defined and categorize everything that exists
Agreeing on the ontology makes it possible to understand the concepts
Efforts to define a big ontology, defining all concepts still exists today:
The Cyc (from Encyclopedia) ontology (over 100,000 concept types and over 1M axioms
Electronic Dictionary Research: 400,00 concept types
WordNet: 166,000 English word senses

8. Cyc Ontology

9. Cyc Ontology Thing Object Intangible Represented Thing Event Stuff
Intangible Object
Collection
Relationship
Process
Intangible Stuff
Attribute value
Slot
Occurrence
Internal machine thing
Attribute

10. Small Ontologies Designed for specific application
How to make these coexist with big ontologies?

11. 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

12. Ontologies and the Web
In the Web ontologies provide shared understanding of a domain
It is crucial to deal with differences in terminology
To understand data in the web it is crucial that an ontology exists
To be able to automatically understand the data, and use in a distributed environment it is crucial that the ontology is:
Explicitly defined
Available in the Web
The Semantic Web initiative provides (web) languages for defining ontologies (RDF, RDF Schema, OWL)

13. Defining an Ontology
How to define a catalog of the types of things that are assumed to exist in a domain?
I.e. how to define an ontology for a given domains?
What makes an ontology?
Entities in a taxonomy
Attributes
Properties and relations
Facets
Instances
Similar to ER models in databases

14. Main Stages in Ontology Development
Determine scope
Consider reuse
Enumerate terms
Define taxonomy
Define properties
Define facets
Define instances
Check for anomalies
Not a linear process!

15. Determine Scope There is no correct ontology of a specific domain
An ontology is an abstraction of a particular domain, and there are always viable alternatives
What is included in this abstraction should be determined by
the use to which the ontology will be put
by future extensions that are already anticipated

16. Determine Scope (cont)
Basic questions to be answered at this stage are:
What is the domain that the ontology will cover?
For what we are going to use the ontology?
For what types of questions should the ontology provide answers?
Who will use and maintain the ontology?

17. Consider Reuse
One rarely has to start from scratch when defining an ontology
In these web days, there is almost always an ontology available that provides at least a useful starting point for our own ontology
With the Semantic Web, ontologies will become even more widely available

18. Enumerate Terms
Write down in an unstructured list all the relevant terms that are expected to appear in the ontology
Nouns form the basis for class names
Verbs form the basis for property/predicate names
Traditional knowledge engineering tools (e.g. laddering and grid analysis) can be used to obtain
the set of terms
an initial structure for these terms

19. Define the Taxonomy
Relevant terms must be organized in a taxonomic is_a hierarchy
Opinions differ on whether it is more efficient/reliable to do this in a top-down or a bottom-up fashion
Ensure that hierarchy is indeed a taxonomy:
If A is a subclass of B, then every object of type A must also be an object of type B

20. Define Properties Often interleaved with the previous step
Attach properties to the highest class in the hierarchy to which they apply:
Inheritance applies to properties
While attaching properties to classes, it makes sense to immediately provide statements about the domain and range of these properties
Immediately define the domain of properties

21. Define Facets Define extra conditions over properties
Cardinality restrictions
Required values
Relational characteristics
symmetry, transitivity, inverse properties, functional values

22. Define Instances
Filling the ontologies with such instances is a separate step
Number of instances >> number of classes
Thus populating an ontology with instances is not done manually
Retrieved from legacy data sources (DBs)
Extracted automatically from a text corpus

23. Check for Anomalies Test whether the ontology is consistent
For this, one must have a notion of consistency in the language
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

24. Protégé Java based Ontology editor
It supports Protégé-Frames and OWL as modeling languages
Frames is based on Open Knowledge Base Connectivity protocol (OKBC)
It exports into various formats, including (Semantic) Web formats
Let’s try it

25. The newspaper example (part)
:Thing
Author
Person
Content
News Service
Employee
Article
Advertisement
Editor
Reporter
Salesperson
Manager
Properties (slots)
Persons have names which are strings, phone number, etc
Employees (further) have salaries that are positive numbers
Editor are responsible for other employees
Articles have an author, which is an instance of Author, and possibly various keywords
Constraints
Each article must have at least two keywords
The salary of an editor should be greater than the salary of any employee which the editor is responsible for
Attribute