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

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

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

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

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

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

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

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

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

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

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.

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., “17106575561”.

Defining an Individual Individual has property hasSSN with value “17106575561”. Id is janPN (complete id:http://www.semanticweb.org/janpn/ontologies/2014/7/untitled-ontology-2#janPN) which we can assume is a globally unique id). The type of the individual is “generic” (i.e., type is Thing).

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

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

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

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).

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))

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

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

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

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

References [1] Book: David Poole and Alan Mackworth, Artificial Intelligence: Foundations of Computational Agents, Cambridge University Press, 2010, http://artint.info/ [2] http://dsg.harvard.edu/courses/hst952/lecture12.ppt%E2%80%8E [3] http://www.jfsowa.com/logic/math.htm#Propositional [4] http://www.cs.ubc.ca/~kevinlb/teaching/cs322%20-%202009-10/Lectures/Logic2.pdf [5] http://www.cs.ubc.ca/~kevinlb/teaching/cs322%20-%202009-10/Lectures/Logic1.pdf [6] http://artint.info/slides/ch05/lect2.pdf 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.