1. An Overview of Ontology and Its Applications
ดร.มารุต บูรณรัช
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2. Outline The Semantic Web What is Ontology? Ontology Languages
Philosophy, Linguistic and Computer Science
Ontology Commitment
Ontology vs. Knowledge Base
Ontology Languages
Some Ontology Applications
Knowledge Management and Engineering
NLP, Question-answering

3. The Semantic Web

4. The Semantic Web
The Semantic Web is a vision of a future by Tim Berners-Lee, creator of the World Wide Web (Berners-Lee et al., 2001):
Web will be more than just a collection of web pages
In the future, computers will be able to consider the meaning, or semantics, of information sources on the Web
This will enable computer programs to perform complex tasks autonomously and to communicate amongst one another, and with humans, by being able to meaningfully interpret the wealth of knowledge that is available on the Web
T. Berners-Lee, J. Hendler, and O. Lassila, The Semantic Web, Scientific American, 2001

5. The Semantic Web (2)
Agents are expected to run everyday tasks for human users, e.g.
Searching
Making an appointment e.g., with a medical doctor
In making an appointment, an agent must process various information in the background, including:
the schedules of both parties
the practitioner’s professional profile
health insurance issues
the location of meetings
The patient is only informed by the agent of the date and time of the appointment that he or she may then confirm or decline.

6. The Semantic Web (3)
(Berners-Lee, et al., 2001)

7. Semantics and the Web
The Semantic Web is intended as a solution that can potentially make the above example possible.
The Semantic Web is basically an attempt to enhance the current Web with a semantic organizational foundation that makes information semantically accessible to machines by considering its meaning

8. Semantics and the Web (2)
An information resource on the Semantic Web will not only contain data, but will also consist of metadata which describe what the data are about.
This will allow agents to identify, collect and process suitable information sources by interpreting the semantic metadata based on the given task.
The semantic foundation will be provided by ontologies.

9. What is Ontology?

10. Ontology in Philosophy
The philosophical discipline that studies the nature of being.
It is an old discipline introduced by Aristotle, which attempts to address questions such as:
‘What is being?’
‘What characteristics do all beings have in common?’

11. Ontology in Philosophy (2)
Ontology refers to a system of categories (or frames of reference) that account for a certain view of the world.
“systematic account of existence” (Gruber, 1993)
An ontology is independent of natural language, but reliant on a particular philosophical view
T. Gruber, “A Translation Approach to Portable Ontology Specifications”, Knowledge
Acquisition, 5(2), , 1993.

12. Ontology and Linguistics
An individual has some concepts in his or her mental model
For example, the concept of a ‘Mouse’, could be perceived as cute or intimidating, depending on individual’s point of view which are independent of language (‘Mouse’ in English, ‘หนู’ in Thai; all refer to the same concept).
This distinction is often made explicit by referring to:
lexical knowledge (the term used to refer to the concept)
non-lexical knowledge (the concept)

13. Ontology and Linguistics (2)
Concept of
TANK
activates
Referent
Relates to
Stands for
Symbol
"Tank"
A concept, is the mediator that relates the symbol to its object

14. Ontologies in Computer Science
The concept of ontology was borrowed from Philosophy by Artificial Intelligence researchers and has become a subject of interest to computer and information scientists in general.
In Computer Science, an ontology is generally regarded as a designed artifact consisting of a shared vocabulary used to describe entities in some domain of interest, as well as a set of assumptions about the intended meaning of the terms in the vocabulary (Guarino, 1998).
N. Guarino, “Formal Ontology in Information Systems”, Proceedings of Formal Ontology in Information Systems (FOIS’98), 1998.

15. Ontologies in Computer Science (2)
The most frequently cited definition of ontology is “an explicit specification of a conceptualisation” (Gruber, 1993).
Ontology often concerned with the formal symbolic representation of knowledge.
Formal logical languages, such as first-order predicate calculus, are used to declaratively describe models of the world
Natural languages are too ambiguous for machine interpretation.
T. Gruber, “A Translation Approach to Portable Ontology Specifications”, Knowledge
Acquisition, 5(2), , 1993.

16. Ontologies in Computer Science (3)
The notion of conceptualisation is an abstract and simplified view of that world, or domain of interest.
Such a conceptualisation consists of objects or entities that are assumed to exist in the domain of interest as well as the relationships (or roles) between them.
The set of objects which knowledge is expressed about is referred to as the universe of discourse.
An explicit specification of such a conceptualisation is called an ontology.

17. Ontological Commitment
In order for knowledge to be shared amongst agents, agreement must exist on the topics which are being communicated.
Ontological commitment (Gruber, 1993) is described as “the agreements about the objects and relations being talked about among agents”.

18. Ontological Commitment (2)
A common ontology serves as a knowledge- level specification of the ontological commitments of a set of participating agents.
Typically, it is very difficult to define absolute meaning or semantics; the results are most often agreement.

19. Ontology vs. Knowledge Base
A shared ontology typically describes a vocabulary for communicating about a domain.
Conceptual structures of a domain
State-independent information (Guarino, 1998)
A knowledge-base contains the knowledge needed to solve problems or answer queries about such a domain by committing to an ontology
Concrete state of the domain
State-dependent information (Guarino, 1998)

20. Light-weight vs. Heavy-weight Ontology
Light-weight ontology
Consists of a topic hierarchy with little consideration of rigorous definition of a concept, principle of concept organization, distinction between word and concept, e.g., Yahoo, DMOZ categories
Heavy-weight ontology
Ontologies developed with much attention paid to rigorous meaning of each concept, organizing principles developed in philosophy, semantically rigorous relations between concepts

21. Ontology Languages

22. Ontology Languages
There are a wide variety of languages for “Explicit Specification”
Graphical Notations
Semantic Networks
Topic Maps
UML
RDF

23. Ontology Languages (2) Logic Based Notations Description Logics Rules
First Order Logic
Conceptual Graphs

24. Languages for the Semantic Web
The Semantic Web efforts has concentrated on defining a collection or “stack” of languages.
These languages are then used to support the representation and use of metadata.
Major standards to represent the semantic information for the Semantic Web
XML
RDF (Resource Description Framework)
RDF(S) (RDF Schema)
OWL (Web Ontology Language) …

25. RDF Schema Expressions
Classes:
<#Student, rdf:type, #rdfs:Class>
Class hierarchies:
<#Student, rdfs:subClassOf, #Person>
Properties:
<#hasName, rdf:type, rdf:Property>
Property hierarchies:
<#hasMother, rdfs:subPropertyOf, #hasParent>
Associating properties with classes (a):
“The property #hasName only applies to #Person”
<#hasName, rdfs:domain, #Person>
Associating properties with classes (b):
“The type of the property #hasName is #xsd:string”
<#hasName, rdfs:range, xsd:string>
Reference:

26. Limitations of RDF(s) Expression
Only binary relations
Do not support
Characteristics of Properties, e.g. inverse, transitive, symmetric
Local range restrictions, e.g. for class Person, the property hasName has range xsd:string
Complex concept descriptions, e.g. Person is defined by Man and Woman
Cardinality restrictions, e.g. a Person may have at most 1 name
Disjointness axioms, e.g. nobody can be both a Man and a Woman
etc.
Reference:

27. RDF and RDFS Vocabulary
RDF Vocabulary
Classes:
rdf:Property, rdf:Statement, rdf:XMLLiteral
rdf:Seq, rdf:Bag, rdf:Alt, rdf:List
Properties:
rdf:type, rdf:subject, rdf:predicate, rdf:object,
rdf:first, rdf:rest, rdf:_n
rdf:value
Resources:
rdf:nil
RDFS Vocabulary
RDFS Classes
rdfs:Resource
rdfs:Class
rdfs:Literal
rdfs:Datatype
rdfs:Container
rdfs:ContainerMembershipProperty
RDFS Properties
rdfs:domain
rdfs:range
rdfs:subPropertyOf
rdfs:subClassOf
rdfs:member
rdfs:seeAlso
rdfs:isDefinedBy
rdfs:comment
rdfs:label

28. OWL Vocabulary Complex relationships among classes Properties Examples
intersectionOf
:Mother    owl:intersectionOf (  :Woman   :Parent  )
unionOf
:Parent   owl:unionOf (  :Mother   :Father  )
complementOf
:Parent   owl:complementOf :NonParent
disjointWith
:Man   owl:disjointWith   :Woman
equivalentClass
:AdultFemaleHuman    owl:equivalentClass    :Woman
sameAs
:JimFromWork    owl:sameAs    :MyNeighborJim
differentFrom
:BobFromWork  owl:differentFrom    :MyNeighborBob
Reference:

29. OWL Vocabulary (2) Characteristics of Property Property Types Examples
DatatypeProperty
ex:hasBirthday 
ObjectProperty
ex:hasSpouse
TransitiveProperty
ex:tallerThan 
SymmetricProperty
AsymmetricProperty
ex:tallerThan
ReflexiveProperty
ex:hasIDNumberOf
IrreflexiveProperty
FunctionalProperty
ex:hasBirthday
InverseFunctionalProperty
ex:has
inverseOf
:hasChild owl:inverseOf :hasParent
equivalentProperty
:hasBirthPlace owl:equivalentProperty :hasBirthLocation

30. OWL Vocabulary (3) Constraints of properties Parameter Examples
cardinality min-cardinality max-cardinality
:Automobile  owl:equivalentclass  [
   rdf:type         owl:Restriction ;
   owl:cardinality  "4"^^xsd:int ;
   owl:onProperty   :hasWheel ] .
oneOf
:BobsChildren owl:equivalentClass  [
   rdf:type   owl:Class ;
   owl:oneOf  ( :Bill  :John  :Mary ) ] .
hasValue
:BobsChildren  owl:equivalentClass  [
   rdf:type        owl:Restriction ;
   owl:onProperty  :hasParent ;
   owl:hasValue    :Bob ] .
someValuesFrom
:GrandParent  owl:equivalentClass  [
   rdf:type            owl:Restriction ;
   owl:onProperty      :hasChild ;
   owl:someValuesFrom  :Parent ] .
allValuesFrom
:Vegetarian owl:equivalentClass  [
   owl:onProperty      :eats ;
   owl:allValuesFrom  :NonMeat ] .

31. Ontology Applications

32. Some Ontology Applications

33. Some Ontology Applications (2)
Libraries of reusable knowledge components and knowledge-based services that can be invoked over networks (Gruber, 1993)
The Semantic Web + Web services

34. Some Ontology Applications (3)
Natural language applications include:
Knowledge acquisition from text (or Information Extraction)
Semantic information retrieval (or Semantic Search)
WORDNET, an online lexical reference system, is an example of a natural language application based on some ontology concepts

35. Some Ontology Applications (4)
Share and Reuse Knowledge across Information Systems
Different systems typically use different terms to refer to the same concepts
Ontology helps to define a common vocabulary that represents a shared knowledge that can be integrated with information systems

36. Some Ontology Applications (5)
Knowledge Management
Ontology can be used to model business semantics and business processes
Ontology can be used to model scientific knowledge, e.g. Gene ontology

37. Our Applications: Healthcare Knowledge Management for Diabetes Mellitus
Structure-based Ontology Modeling

38. Our Applications: Healthcare Knowledge Management for Diabetes Mellitus (2)
Procedure-based Ontology Modeling

39. Our Applications: Healthcare Knowledge Management for Diabetes Mellitus (3)
Ontology-based Clinical Reminder System applied to a Diabetes Patient Registry

40. Our Applications: Biographical Question-Answering System
Web-based Question-Answering UI

41. Our Applications: Biographical Question-Answering System (2)
OWL Export
# Ex. 2. [REL: ลูกสาว] [Q: =ชื่ออะไร]
PREFIX mark: <
SELECT ?z
WHERE
{ mark:Abhisit mark:has_daughter ?x .
?x mark:name ?z}
SPARQL Querying
Person Ontology Modeling

42. Decision Supported System (DSS) Framework to Support Rice Research Policy Planning
Buranarach, M., Porkaew, P., and Supnithi, T.,A Decision Support System Development to Support Rice Research Policy Planning using an Ontology-based Framework, Proceedings of the International Symposium on Natural Language Processing and Agriculture Ontology Service (SNLP-AOS2011), February 2012.

43. Ontology-based Development Framework
Rice Research Database
Rice Research Ontology
Define concept structure
Input Form Templates
Reusable Knowledge
Other Information Systems
Analysis/ Report System
Extraction/ Search System

44. Ontology Development – Rice Research Project

45. Ontology Development – Rice Research Areas

46. Analysis/ Report System to Support Rice Research Policy Planning

47. Home Recovery Ontology
Knowledge as a Service for Home Recovery
Home Recovery Ontology
(แสดงบางส่วน)

48. Resource to Ontology Mapping

49. Profile to Ontology Mapping

50. System Architecture

51. Example – Creating Homeowner Profile

52. Example – Personalized recommendation

53. Example – Recommended resources
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