An Overview of Ontology and Its Applications ดร.มารุต บูรณรัช หน่วยปฏิบัติการวิจัยเทคโนโลยีภาษาธรรมชาติและความหมาย ศูนย์เทคโนโลยีอิเล็คทรอนิกส์และคอมพิวเตอร์แห่งชาติ marut.bur@nectec.or.th

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

The Semantic Web

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

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.

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

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

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.

What is Ontology?

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?’

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), 199-220, 1993.

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)

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

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.

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), 199-220, 1993.

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.

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”.

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.

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)

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

Ontology Languages

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

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

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) …

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: http://www.sti-innsbruck.at/teaching/curriculum/semantic-web

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: http://www.sti-innsbruck.at/teaching/curriculum/semantic-web

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

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: http://www.cambridgesemantics.com/semantic-university/owl-reference-humans

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:hasEmail inverseOf :hasChild owl:inverseOf :hasParent equivalentProperty :hasBirthPlace owl:equivalentProperty :hasBirthLocation

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 ] .

Ontology Applications

Some Ontology Applications

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

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

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

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

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

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

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

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

Our Applications: Biographical Question-Answering System (2) OWL Export # Ex. 2. [REL: ลูกสาว] [Q: =ชื่ออะไร] PREFIX mark: <http://semantic.nectec.or.th/ontology/abhisit360.owl#> SELECT ?z WHERE { mark:Abhisit mark:has_daughter ?x . ?x mark:name ?z} SPARQL Querying Person Ontology Modeling

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.

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

Ontology Development – Rice Research Project

Ontology Development – Rice Research Areas

Analysis/ Report System to Support Rice Research Policy Planning

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

Resource to Ontology Mapping

Profile to Ontology Mapping

System Architecture

Example – Creating Homeowner Profile

Example – Personalized recommendation

Example – Recommended resources บทความที่เกี่ยวข้อง แยกหมวดตาม Tag หัวข้อที่เกี่ยวข้อง ร้านค้าวัสดุ, บริษัทรับซ่อมแซมที่อยู่ใกล้เคียง http://technology.in.th/rebuild/