Semantic Web Ontology Design Pattern Li Ding Department of Computer Science Rensselaer Polytechnic Institute October 3, 2007 Class notes for CSCI-6962 Semantic Web

Outline Ontology design principles Ontology design procedure with examples References

Ontology Design Principles (Noy and McGuinness, 2001) There is no one correct way to model a domain— there are always viable alternatives. The best solution almost always depends on the application that you have in mind and the extensions that you anticipate. Ontology development is necessarily an iterative process. Concepts in the ontology should be close to objects (physical or logical) and relationships in your domain of interest. These are most likely to be nouns (objects) or verbs (relationships) in sentences that describe your domain. Source:

Ontology Design Procedure 1. Determine domain and scope of an ontology 2. Design competence test 3. Enumerate important terms in the ontology 4. Design the ontology  Reusing existing ontologies  Creating new ontology 5. Verify fitness of the ontology Note: this procedure is a modified version of (Noy and McGuinness, 2001)

1. Determine Domain and Scope of an Ontology We need requirements to the ontology We need to focus on clarified domain and scope Example questions and answers :  What is the domain that the ontology will cover? E.g. personal profile information  For what we are going to use the ontology? E.g. for sharing personal profile with friends  For what types of questions the information in the ontology should provide answers? E.g. for “what is my ”, “who are my classmates”  Who will use and maintain the ontology? E.g. myself will do the maintenance and all my friends may run queries.

2. Design Competence Test A competence test offers “real world” instance data and query to be supported by the ontology It helps  Checking domain/scope of the ontology  Identifying inference to be offered by the ontology  verifying fitness of the designed ontology

Example Competence Test Domain/scope: to describe something about person English statements to represented  E.g. Professor Jim Hendler works at RPI.  E.g. Li Ding is colleague of Jim.  E.g. Photography is one of Li’s hobbies. English queries to be answered  E.g. Find all who work at RPI? no inference  E.g. List the names of all persons mentioned in data may need rdfs:subClassOf inference to find all instances of person

3. Enumerate Important Terms of the Ontology Convert complex English sentences to simple ones Map simple English sentence to RDF triple  Identify nodes – usually nouns, e.g. RPI, Li Ding  Identify arcs – usually verbs, e.g. name, age Refine node classification  A thing - Resource/instance, e.g RPI  A set of things - Class/type, e.g. person, airport, course  Text to be preserved - Literal, e.g. “Jim Hendler” Review translation  If English statements fully translated into an RDF graph  if English statements can be restored from the RDF graph

Simplify Complex English Sentences The input English statement  Professor Jim Hendler works at RPI. Revision 1: the actual semantics  (There is a person, who is a) Professor (and has name) Jim Hendler(,) works at RPI. Final Revision consists of three statements  A person works at RPI.  The person is a professor  The person has name Jim Hendler.

Map Simple English Sentence to RDF Triple Professor Jim Hendler works at RPI. A person works at RPI. The person is a professor The person has name Jim Hendler. Professor Jim HendlerRPI works at a personProfessor Jim Hendler Is a RPI has name works at

Refine Node Classification and Definition A person works at RPI. The person is a professor The person has name Jim Hendler. ex:JHex:Professor ex:RPI “Jim Hendler” rdf:type ex:JH identified resource Legends ex:Professor “Jim Hendler”identified literal identified class ex:name ex:worksAt “RPI” “Professor” ex:label ex:name identified arc

Review Translation Is the translation complete?  Can we translate it back to the original English statements?  Why “ex:name” is added? Why identify literal? Why use “ex:” as namespace? Some resources such as ex:RPI do not have type, is that ok? Will there be any other translations? Note the semantics of “Professor” can also be captured by “a person whose title is professor”.

4. Design the Ontology An early mobile computing prototype Reusing existing ontologies Creating new ontology I know the terms, but how to get my owl ontology ? swoop protégé

Reuse Existing Ontology Finding ontologies  Search Swoogle or Google using identified terms as keywords  Go to well-known ontology repositories Evaluate fitness of existing ontology  Check if most identified terms are covered by the ontology  Run competence test Hints  Semantic matching is recommended because one concept may corresponds to multiple English words  We may reuse a set of existing ontologies instead of only one  A big comprehensive ontology is useful but also costs non- trivial learning time.  Good ontologies can be either well-defined or widely-used.

Create New Ontology A simplified procedure  Define classes and class hierarchy  Define properties  Associate properties with classes Domain and range of property Property-cardinality restriction Property-value restriction  Using complex classes constructs Hints on how to make choices

Legends Class space Instance space owl:Thing Jim HendlerLi Ding RPI subClassOf type ex:Person ex:Professor owl:Class Define class and Class Hierarchy

Legends Class space Instance space Define Properties owl:Thing “Jim Hendler” “Li Ding” subClassOf type ex:worksAt ex:isColleagueOf ex:name ex:Person ex:Professor owl:Class “RPI” rdfs:label

Define Properties (Cont’d) ex:ex:isColleagueOf ex:nameOwl:DatatypeProperty Owl:ObjectProperty “works at” “has name” rdfs:label rdf:type ex:name identified property Legends “Jim Hendler”identified literal predefined concept Owl:Thing Owl:InverseFunctionalProperty rdf:type Differentiate properties  owl:DatatypeProperty  owl:ObjectProperty  Predefined properties, e.g. rdf:type

Why the domain of ex:isColleagueOf is ex:Person instead of ex:Professor ? ex:isColleagueOfOwl:ObjectProperty “works at” rdfs:label ex:People ex:Person rdfs:domain rdfs:range rdf:type ex:name identified property Legends ex:Professor “Jim Hendler”identified literal identified class predefined concept Owl:Thing Owl:InverseFunctionalProperty rdf:type Associate Properties with Classes

ex:Professorowl:Class ex:title ex:professor-title owl:Restriction owl:OnProperty rdf:type owl:hasValue foaf:Person rdfs:subClassOf owl:Class rdf:type rdfs:subClassOf A simple class definition A descriptive class definition Reusing external class definition Complex Class Construct - “Professor”

Hints for Making Choices Ensuring that the class hierarchy is correct  “A single wine is not a subclass of all wines“ Analyzing siblings in a class hierarchy  “How many is too many and how few is too few?” Multiple inheritance When to introduce a new class (or not)  “Subclasses of a class usually (1) have additional properties that the superclass does not have, or (2) restrictions different from those of the superclass, or (3) participate in different relationships than the superclasses “  “Classes in terminological hierarchies do not have to introduce new properties” A new class or a property value?  Do we create a class White wine or do we simply create a class Wine and fill in different values for the slot color? An instance or a class?  Individual instances are the most specific concepts represented in a knowledge base.  If concepts form a natural hierarchy, then we should represent them as classes More… (please read the referenced article)

5. Verify Fitness of Ontology This test is necessary, do not skip The fitness of ontology can be justified if the following conditions are met:  the above English statements can be represented using the designed ontology  the above English queries can be answered by the represented data and the designed ontology

This talk offers basics on building an ontology for a certain domain/application  Several principles  A five-step procedure The competence test is the most critical part  by filtering out unnecessary definition  by identifying an ontology’s inference potential  by verifying fitness of ontology Summary

References Natalya F. Noy and Deborah L. McGuinness. ``Ontology Development 101: A Guide to Creating Your First Ontology''. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05, March ksl.stanford.edu/people/dlm/papers/ontology1 01/ontology101-noy-mcguinness.html Natalya F. Noy Deborah L. McGuinnessKSL-01-05http://www- ksl.stanford.edu/people/dlm/papers/ontology1 01/ontology101-noy-mcguinness.html