1. Knowledge Representation Part II Description Logic & Introduction to Protégé
Jan Pettersen Nytun

2. Knowledge Representation Part II, JPN, UiA
The Semantic Web
"The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation.“
Ref: "The Semantic Web" by  Tim Berners-Lee, James Hendler, and Ora Lassila, Scientific American, 2001
Knowledge Representation Part II, JPN, UiA

3. Linked Data/Semantic Web From Wikipedia
…a method of publishing structured data so that it can be interlinked... …builds upon standard Web technologies such as HTTP, RDF and URIs… it extends them to share information in a way that can be read automatically by computers. This enables data from different sources to be connected and queried.
Knowledge Representation Part II, JPN, UiA

4. Some Semantic Web Technologies are Based on Description Logic (DL)
DL is used in AI - modern ontology languages are based on description logics, e.g., OWL.
Provide a logical formalism for ontologies and the Semantic Web.
Much used in biomedical informatics codification of medical knowledge.
Knowledge Representation Part II, JPN, UiA

5. Description Logic (DL) Continues…
A description logic is used to describe classes, properties, and individuals.
The knowledge base contains:
Tbox (model): A terminological part which should remain constant as the domain being modelled changes.
Abox (data): An assertional part describing what is true in some domain at some point in time.
Knowledge Representation Part II, JPN, UiA

6. Description Logic Continues…
Terminology part (Tbox or Model):
Defines concepts (also called classes), e.g., vital sign, blood pressure, patient.
Defines properties (also called roles or property types), e.g., hasBloodPressure.
Knowledge Representation Part II, JPN, UiA

7. Description Logic Continues…
Assertion part (ABox or Model Instance):
Descriptions of individuals (also called objects) with their properties, e.g., description of a patient and the patients blood pressure.
Not all individuals in the assertion part may have been classified and this differs from ordinary object-oriented program development.
Knowledge Representation Part II, JPN, UiA

8. Knowledge Representation Part II, JPN, UiA
DL in Short
T-Box: Definition of Concepts (“Classes”), Roles (“Properties”) and Constraints. Subsumption Hierarchy (class-subclass hierarchies).
A-Box: Assertions about individuals (instances)
Unary predicates = concepts (e.g., Person, Boat)
Binary predicates = roles
Necessary and Sufficient conditions on classes.
Knowledge Representation Part II, JPN, UiA

9. Knowledge Base User Interface Rules Application Software Sensors
Terminology (TBox) - Model
Rules
Sensor Handlers
Atomic
Complex
Classes
(Concepts)
Classes
(Concepts)
Actuator Handlers
Property Types
Property Types
Actuators
User Interface
Assertions (ABox) - Model Instance
Asserted
Inferred
Application Software
Named Individuals
Named Individuals
Query Engine
Properties
Properties
Reasoner

10. Protégé
A free, open-source OWL ontology editor and framework for building intelligent systems

11. Protégé
Class hierarchy (Subsumption hierarchy/taxonomy): Patient is subclass of Person which is subclass of Thing.
Property hierarchy: Properties are modeled separately from Classes hasSSN is sub property of topDataProperty.

12. Protégé
Property hasSSN has Person as domain. This means that an individual having this property must be of type Person, i.e., it is an axiom stating that given an individual with this property then it can be inferred that this individual is of type Person.
Property hasSSN has string as Range. I.e., the value of the property must be a text string, e.g., “ ”.

13. Defining an Individual
Individual has property hasSSN with value “ ”.
Id is janPN (complete id: which we can assume is a globally unique id).
The type of the individual is “generic” (i.e., type is Thing).

14. Starting the Reasoner
Since janPN has property hasSSN then it must be a Person (i.e., the domain is Person for hasSSN).
inferred

15. Type and Subclass as Properties
Type of an individual is stated as a property - . a property predefined in RDF called rdf:type. E.g.: ( Tom rdf:type Person )
Subclass is a property between classes. a property predefined in RDFS called rdfs:subClassOf. E.g.: ( Employee rdfs:subClassOf Person )
Knowledge Representation Part II, JPN, UiA

16. Knowledge Base Rules Terminology (TBox) - Model Atomic Complex Classes
(Concepts)
Classes
(Concepts)
Property Types
Property Types
Assertions (ABox) - Model Instance
Asserted
Inferred
Named Individuals
Named Individuals
Properties
Properties

17. Complex Class An atomic class is somewhat like an “ordinary class”.
A Complex class is built with the help of description logic constructors, properties and other classes (atomic or complex).

18. Complex Class Continues…
Example using intersectionOf: Informally: A man is a human that is also a male
Formally: Class Man is the intersection of class Human and Male In a more formal syntax: EquivalentClass(Man intersectionOf(Human Male))

19. Example: Complex Class In Protégé
(Alternatively you may specify that Man is subclass of Human and Man)
Run reasoner
Reasoner infer that Tom is a Man
Asserted

20. Reasoner infers that Tom is a HumanParent
Example: To be a parent you need to be human and additionally parent to at least one child.
Run reasoner
Reasoner infers that Tom is a HumanParent

21. To be a sick human you need to suffer from at least one sickness
Tom and TomsDiabetes2 are individuals
Run reasoner
Reasoner infers that Tom is a SickHuman

22. Knowledge Representation, Part II, JPN, UiA
Example of rule using The Semantic Web Rule Language (SWRL): 
hasParent(?x1,?x2) ∧ hasBrother(?x2,?x3) ⇒ hasUncle(?x1,?x3)
Also SPARQL can be used as a rule language.
Knowledge Representation, Part II, JPN, UiA

23. References
[1] Book: David Poole and Alan Mackworth, Artificial Intelligence: Foundations of Computational Agents, Cambridge University Press, 2010,
[2]
[3]
[4]
[5]
[6]
Sowa, John F. (2000) Knowledge Representation: Logical, Philosophical, and Computational Foundations, Brooks/Cole Publishing Co., Pacific Grove, CA.
Artificial Intelligence: Structures and Strategies for Complex Problem Solving (Addison-Wesley), George F. Luger
Smith Barry. Accessed 24th of March, 2013, Ontology: Philosophical and Computational. http: //ontology.buffalo.edu/smith/articles/ontologies.htm
Quine WVO. On What There Is. Review of Metaphysics 1948;p. 21–38.