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1 TDT4215 Web Intelligence TDT4215 Web Intelligence Main topics: Introduction to ontologies Introduction to ontologies Ontology applications classification framework Presenter: Presenter: Stein L. Tomassen TDT4215 -

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TDT4215 Web Intelligence TDT4215 Web Intelligence

Main topics:

  • Introduction to ontologies
  • Introduction to ontologies
  • Ontology applications classification framework

Presenter: Presenter:

  • Stein L. Tomassen

TDT4215 - Ontologies TDT4215 - Ontologies

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Outline Outline

  • Ontology ABC

– Terminology – Motivation

  • Semantic web
  • RDF and RDFS

– Brief introduction to state-of-the-art ontology languages (except OWL -> next lesson).

  • Classification of ontology applications
  • Classification of ontology applications

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Introduction to ontologies

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Communication between people Communication between people

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Ontology ABC Ontology ABC

  • Ontology attracts attentions across many fields in computer science

recently.

  • The term ontology originates from philosophy and its current usage in
  • The term ontology originates from philosophy and its current usage in

computer science (first introduced in AI) is far from its philosophical

  • rigin.
  • There exists no consensus definition about ontology (some definitions
  • There exists no consensus definition about ontology (some definitions

next).

  • In many cases, the term ontology is another name denoting the result
  • f familiar activities like conceptual analysis and domain modeling
  • f familiar activities like conceptual analysis and domain modeling.
  • The roles of ontology vary from knowledge management to semantic

interoperability.

  • One important reason for that ontology attracts so much attention

recently is the Semantic Web, since ontology is considered the key enabler of Semantic Web.

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Some definitions Some definitions

  • “That department of the science of metaphysics which

investigates and explains ontology as the nature and essential properties and relations of all beings, as such,

  • r the principles and causes of being”

“O t l i b t t d l hi h t

  • “Ontology is an abstract model which represents a

common and shared understanding of a domain” “An ontology is a [formal explicit] specification of a

  • “An ontology is a [formal explicit] specification of a

[shared] conceptualization” (one of the most cited)

  • “Computer ontologies are formally specified models of
  • Computer ontologies are formally specified models of

known knowledge in a given domain”

Ref: (Akerkar, 2011)

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( )

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What are ontologies?

F l li i ifi i f h d li i

What are ontologies?

Studer(98): Formal, explicit specification of a shared conceptualization

Machine readable Consensual knowledge readable i knowledge b d l f Concepts, properties, functions, axioms are explicitly defined Abstract model of some phenomena in the world are explicitly defined in the world

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More terminology More terminology

  • Ontology: (engineering artefact)

– Constituted by a vocabulary (concepts, relations) – Assumptions about intended meaning

  • Formalization:

Logical theory accounting for the intended meaning of a formal – Logical theory accounting for the intended meaning of a formal vocabulary – Committed to a particular conceptualization of the world

  • Ontology vs. conceptualization

– Conceptualization is language-independent Ontology is language dependent – Ontology is language-dependent

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Ex#1: SUMO Ex#1: SUMO

  • Suggested Upper Merged Ontology (SUMO)

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Ref: http://www.ontologyportal.org 10

Ex#2: OpenCyc Ex#2: OpenCyc

Upper Ontology: Abstract Concepts Know ledge Upper Ontology Core Upper Ontology: Abstract Concepts Core Theories: Space, Time, Causality, … g Base Layers Core Theories Domain-Specific Theories Core Theories: Space, Time, Causality, … Domain-Specific Theories Facts (Database) Theories Facts: Instances

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Ref: http://www.opencyc.org

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Ex#3: Hierarchical Categories? Ex#3: Hierarchical Categories?

  • Can hierarchical

categories be

  • ntologies?

Conceptualization of medical domain? domain?

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More confusion? More confusion?

Differences and similarities

O l Thesaurus Ontology Taxonomy Meta- d l y model

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Ontology spectrum Ontology spectrum

Strong semantics Local Domain

First order logic

Is disjoint subclass

  • f with transitive

property Theory

UML OWL Description logic

Conceptual Model Is subclass of

RDF/S Extended ER UML

Thesaurus Has narrower meaning than

Schema ER

W k ti Taxonomy Is subclassification of

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Weak semantics

Ref: ”Ontologies Come of Age”, McGuinness, 2003 14

Ontological levels Ontological levels

E.g. units, everyday concepts Most central (a few hundred) concepts in domain. Known to most domain experts Detailed specific concepts not fully understood by all domain experts

Low level concepts are more difficult and expensive to formalize

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Ontology languages Ontology languages

OWL & OWL 2 OWL & OWL 2

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Ontology Languages

  • Wide variety of languages for “explicit specification”

– Graphical notations

  • Semantic networks

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Ontology Languages

  • Wide variety of languages for “explicit specification”

– Graphical notations

  • Topic Maps

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Ontology Languages

  • Wide variety of languages for “explicit specification”

– Graphical notations

  • UML

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Ontology Languages

  • Wide variety of languages for “explicit specification”

– Graphical notations

  • RDF

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Ontology Languages

  • Wide variety of languages for “explicit specification”

– Logic based

  • Description Logics (e.g., OIL, DAML+OIL, OWL)
  • Rule (e.g. SWRL, RuleML, Prolog)
  • First Oder Logic (e.g., KIF)

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Ontology Languages

  • Wide variety of languages for “explicit specification”

– Logics based

  • Conceptual graphs

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Ontology Languages Ontology Languages

  • Degree of formality varies widely

– Increased formality makes languages more amenable to machine i ( t t d i ) processing (e.g., automated reasoning)

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The Semantic Web The Semantic Web

  • Goal: evolve the Web

– From sites designed for human consumption – To sites also understandable and usable by computer programs.

“The Semantic Web is an extension of the current web in which information is “The Semantic Web is an extension of the current web in which information is the current web in which information is given well-defined meaning, better enabling computers and people to the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation.” work in cooperation.”

  • What would that do for us?

– Query answering rather than document retrieval – Services findable, usable, and composable by automated agents – Information exchange among independently designed programs

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The Semantic Web The Semantic Web

  • How do we get there from here?

– For services

  • Service description
  • Ontologies to provide intended meaning of service item.

– For documents

  • Structure, ala XML
  • Ontologies to provide intended meaning of terms

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Semantic Web “Layered Cake” Semantic Web Layered Cake

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Ref: http://www.w3.org/2007/Talks/0130-sb-W3CTechSemWeb/#%2824%29

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Data integration Data integration

TDT4215 - Ontologies Ref: http://www.w3.org/People/Ivan/CorePresentations/SW_QA/Slides.html#%2810%29

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XML XML

  • XML describes document structure
  • “XML is like HTML, where you make up your own tags.”
  • HTML

– Language for describing how to display document content

  • E.g., tag a word to be displayed in bold or italic
  • XML

– Language for describing the structure of document content

  • E.g., declare data to be a retail price, a sales tax, a book title, ...

g , p , , ,

– XML allows authors to create their own markup (e.g. <AUTHOR>), which seems to carry some semantics. However, from a computational perspective tags like <AUTHOR> carries as much semantics as a tag like H1 A t i l d t k h t th i d h th <H1>. A computer simply does not know, what an author is and how the concept author is related to e.g. a concept person.

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Bibliographic entry in XML Bibliographic entry in XML

<Publication URL = "ftp://db.stanford … xml.ps”> <Title> From Semi-structured Data ... Language </Title> <Author> R Goldman </Author> <Author> R. Goldman </Author> <Published> Proceedings of ... Databases </Published> <Location>

Location of what? Location of what?

<City> Philadelphia </City> <State> Pennsylvania </State> </Location>

f

</Location> <Date> <Month> June </Month>

When in June? When in June?

<Year> 1999 </Year> </Date> </Publication>

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XML is not enough XML is not enough

  • Language for describing the structure of document content

– E.g., declare data to be a retail price, a sales tax, a book title, ...

U if th d f d ibi d h i d t i HTTP

  • Uniform method for describing and exchanging data using HTTP

– Ontologies enable independently developed programs to exchange data

  • Provides a “syntactic schema”
  • Provides no means of specifying intended meaning of tags

– Ontologies specify intended meaning in a computer interpretable form

So – So –

  • “XML is like HTML, where you make up your own tags.”
  • “But in XML, you can’t say what your tags mean.”

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W3C Semantic Web Activity W3C Semantic Web Activity

  • Semantic Web Activity (http://www.w3.org/2001/sw/)

– “Established to serve a leadership role, in both the design of enabling specifications and the open collaborative development of technologies that specifications and the open, collaborative development of technologies that support the automation, integration and reuse of data across various applications.” – Successor to the W3C Metadata Activity

  • RDF Core Working Group

(http://www.w3.org/2001/sw/RDFCore/)

– Responsible for the Resource Description Framework (RDF) – Responsible for the Resource Description Framework (RDF)

  • Web Ontology Working Group

(http://www.w3.org/2001/sw/WebOnt/)

– Charter: Build upon the RDF Core work a language for defining structured web based ontologies which will provide richer integration and interoperability of data among descriptive communities Developing Ontology Web Language (OWL)

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– Developing Ontology Web Language (OWL)

  • Based on DAML+OIL, developed in DARPA’s Agent Markup Language program
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RDF RDF

  • Resource Description Framework (RDF)
  • A simple representation language for describing Web

p p g g g resources

  • All sentences are triples of the form

“(Property Subject Object)”

– Property is a binary relation Subject is a URI reference – Subject is a URI reference – Object is either a URI reference or a literal

  • E.g., (creatorOf http://www.w3.org/Lassila “Ora Lassila”)

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RDF Schema RDF Schema

  • Model theoretic semantics
  • Includes a resource “Class” and properties “type”,

p p yp , “subclassOf”, etc.

– Supports classes of resources and literals

E (t El h t Cl d )

  • E.g., (type Elephant Clyde)

– Supports subclass hierarchies

  • E.g., (subclassOf Elephant Mammal)

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RDF Schema RDF Schema

Classes

  • Class

Properties

  • subClassOf

Resource Literal

  • ContainerMembershipProperty
  • subPropertyOf
  • seeAlso
  • isDefinedBy

Class Property Container Statement

  • isDefinedBy
  • comment
  • label

Class Property Container Statement

  • range
  • domain

ContainerMembershipProperty Bag Seq

Alt

  • member

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RDF S Class and Property Definitions RDF-S Class and Property Definitions

<rdf:Class ID="MotorVehicle"> <rdfs:subClassOf rdf:resource="http.../PR-rdf-schema-19990303#Resource"/> </rdf:Class>

Christine is a Christine is a

<rdf:Class ID="PassengerVehicle"> <rdfs:subClassOf rdf:resource="#MotorVehicle"/> </rdf:Class> <rdf:Class ID="Van"

Christine is a Christine is a passenger vehicle. passenger vehicle. Is Christine a motor Is Christine a motor hi l hi l ?

<rdf:Class ID="Van" <rdfs:subClassOf rdf:resource="#MotorVehicle"/> </rdf:Class> <rdf:Class ID="MiniVan">

vehicle vehicle? Yes Yes

<rdfs:subClassOf rdf:resource="#Van"/> <rdfs:subClassOf rdf:resource="#PassengerVehicle"/> </rdf:Class>

Christine is registered Christine is registered

<rdf:Property ID = "registeredTo registeredTo"> <rdfs:domain rdf:resource = “#MotorVehicle” /> <rdfs:range rdf:resource = “#Person” /> </rdf:Property>

Christine is registered Christine is registered to to Arnie Arnie. . What is What is Arnie Arnie? ? A person A person

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p y

A person A person

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Comments on RDF and RDF S Comments on RDF and RDF-S

  • Severely lacking in expressive power

– Domain and range constraints rather than Value-Type

  • E g

can’t define class of people all of whose children are male

  • E.g., can t define class of people all of whose children are male

– No cardinality constraints

  • Particularly important for “exactly 1” and “at most 1”

– No decompositions No decompositions

  • Particularly important for “disjoint” and “exhaustive”

– No axioms – No negation (!) No negation (!)

  • Not useful for checking consistency

– E.g., can’t prove an object is not an instance of a class

B i ll t i t

  • Basically a typing system
  • More powerful ontology representation languages are needed.

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A framework for classification of A framework for classification of

  • ntology applications

gy pp

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A framework for understanding and g classifying ontology application

  • The paper presents a framework for understanding
  • ntology applications (being systems or processes).
  • We will study

– The framework V i t l li ti i ( ) – Various ontology application scenarios (use cases).

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Overview of the framework Overview of the framework

  • Purpose and benefits

– Communication – Interoperability – System engineering benefits, in particular:

  • Reusability, search, reliability, specification, maintenance, knowledge

y, , y, p , , g acquisition

  • Role of the ontology

Th l l f i f ti – Three level of information

  • L0: operational data (e.g. a process description)
  • L1: an ontology (e.g. PIF)
  • L2: ontology representation language (e.g. RDF)

– To share or exchange information at Ln requires reference to a model at level Ln+1

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Overview of the framework Overview of the framework

  • Actors

– Ontology Author (OA) – Data Author (DA) – Application Developer (AD) – Application User (AU) Application User (AU) – Knowledge Worker (KW)

  • Supporting technologies (but not limited to)

– Ontology representation languages. – Knowledge interchange languages. – Translation tools Translation tools – Distributed objects

  • Maturity level

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y

– From untested to commercial applications

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Ontology application scenarios Ontology application scenarios

  • These scenarios are abstractions of specific

application of ontologies taken from industry or research.

  • Four main categories of scenarios

– Neutral authoring

  • Authoring ontologies
  • Authoring operational data

– Ontology as specification – Common access to information

  • Human communication

Human communication

  • Data access via shared ontology
  • Data access via mapped ontology
  • Shared services

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Shared services

– Ontology-based search

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Neutral authoring Neutral authoring

  • The main idea is to author an artifact in a single

language, and to have that artifact translated into a different format for use in multiple target application.

  • The authored artifact to translate can be an ontology

ti l d t

  • r operational data.

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Ontology as specification Ontology as specification

  • Author an ontology which

models the application domain, and provides a vocabulary for specifying requirements for one

  • r more target application.

(building a concept model of a domain in UML)

  • Examples.

– KADS/CML – Protégé – Information Modeling – Object oriented modeling – Software synthesis

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Common access to information Common access to information

  • Use ontologies to enable multiple target applications

(or humans) to have access to heterogeneous sources of information (ontology based information integration). F t i

  • Four categories.

– Human communication – Data access via shared ontology Data access via shared ontology – Data access via mapped ontology – Shared services

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Human communication Human communication

  • Promote common

understanding among u de s a d g a

  • g

knowledge workers.

  • Supporting technologies

include ontology editors and include ontology editors and browsers.

  • Example: the work flow

management coalition reference documents.

  • Maturity: library classification

Maturity: library classification skills have a long history (KWs sharing an ontology in the form of a glossary)

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the form of a glossary)

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Data access via shared ontology Data access via shared ontology

  • An ontology can be used as

an interchange format to a e c a ge o a

  • enable common access to
  • perational data.
  • Example: Process
  • Example: Process

Interchange Format (PIF) and EcoCyc

  • Maturity: commercial

success exists in some context, while in others, the , , technology is a long way from being mature.

– Difficult to agree on common

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Difficult to agree on common

  • ntology

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Data access via mapped ontology Data access via mapped ontology

  • No explicit shared
  • ntology instead
  • ntology, instead

mapping rules are used to define what a term in

  • ne ontology means in

another ontology.

  • Example: mediator

based interoperability

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Shared services Shared services

  • Similar to data access via

shared ontology, but s a ed o

  • ogy, bu

different in the focus of what is being shared. The

  • ntology defines interfaces
  • ntology defines interfaces

in multiple target languages.

  • Example: Using UML to

t t l f create an ontology for product data management, this ontology is then used to generate interface code for the client and server.

  • Maturity: relatively mature

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Maturity: relatively mature

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Ontology based search Ontology based search

  • Use an ontology for

searching an searching an information repository for desired resources.

  • Example: Yahoo
  • Maturity: Many

Maturity: Many commercial internet portals are beginning to explore the use of concepts for ontology- based search

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based search.

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Conclusion Conclusion

  • The paper presents a framework for understanding ontology

applications. W t di d

  • We studied

– The framework – Various ontology application scenarios (use cases).

  • Neutral authoring

– Authoring ontologies – Authoring operational data

  • Ontology as specification
  • Ontology as specification
  • Common access to information

– Human communication – Data access via shared ontology gy – Data access via mapped ontology – Shared services

  • Ontology-based search

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Examples

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Vision of semantic interoperability Vision of semantic interoperability

  • Introduce a semantic standard that

– covers all disciplines in subsea petroleum activities – is used by all companies

  • Use semantic standard to define meaning of data & provide

g p uniform access to all data

Today: Many databases, need 1-to-1 mappings to improve collaboration and integration Future: All data in all databases defined with respect to common standard Standardized semantic

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improve collaboration and integration to common standard. Standardized semantic interface to all data 52

Ontology engineering Ontology engineering

Domain specific terminologies Subsea oil and gas ontology terminologies gas ontology ISO 15926

Production Production

  • Prosty
  • DPR

Structure and add to ISO 15926 Reference Data

Drilling

  • WITSML

DDR Library (RDL)

  • DDR

Drilling Drilling

  • WITSML
  • DDR

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Etc.

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Ontology structure Ontology structure

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OWL petroleum ontology OWL petroleum ontology

<owl:Class rdf:about="#CHRISTMAS_TREE"> … <dc:description rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> An artefact that is an assembly of pipes and piping parts, with valves and associated control equipment that is connected to the top of a wellhead and q p p is intended for control of fluid from a well. </dc:description> <dc:title rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> CHRISTMAS TREE </dc:title> </dc:title> … <rdfs:subClassOf rdf:resource="#ARTEFACT"/> </owl:Class>

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Information exchange in g multidisciplinary domains

As-is To-be

Ontology centric system

Retrieve information Create information Feature vector

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Information exchange in g multidisciplinary domains

search query user

1 5

search results query

  • ntology

repository

  • ntology-driven

2 4

  • ntology-driven

retrieval engine

feature vector repository 3

d query and indexing system

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More of this system in another lecture…

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Summary Summary

  • Ontology ABC

– Motivation

  • Semantic web
  • RDF and RDFS

– Brief introduction to state of art ontology languages.

  • In depth introduction to one of such languages - DAML+OIL
  • Impact of ontology to information system
  • Classification of ontology applications
  • Examples

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