Ontology Dev

Ontology Engineering:
O t l E i i
Design and Practices
g

2009년 03월 21일

한 성 국
의미기술 연구소 / 원광대학교

2009-03-20 skhan@wku.ac.kr 1

Agenda

 Review of Ontology

 Ontology
gy Development Methods
p

 Ontology Building

 Ontology
gy Building Summary
g y

2009-03-20 skhan@wku.ac.kr page 2

Review of Ontology
gy

A Plethora of 'Ontology-Like Things’

Glossaries / Controlled Vocabularies Data and Document Metamodels
ad hoc XML Restricted Logics
Hierarchies (OWL, F-logic)
structured Schema
(Yahoo!) Glossaries formal
Taxonomies
Terms XML DTDs
Thesauri

‘ordinary’ Principled, Data Models
Glossaries informal (UML, STEP)
taxonomies
Data Frames General
Dictionaries Logic
DB (OKBC)
(EDI) Schema
Informal Taxonomies and Thesauri Formal Knowledge Bases & Inference

Many Different Ways of Expressing Meaning
M Diff tW fE i M i

Semantics-Related Technologies

Controlled
Vocabulary
+ Grouping Classification

Controlled Hierarchical
Vocabulary + Structure
Taxonomy

Controlled Term
Vocabulary + Relations
Thesaurus

Controlled Semantic Relation,
Vocabulary + Constraints, Axioms, Rules Ontology

Ontology + Instances Knowledge
Base

2009-03-20 skhan@wonkwang.ac.kr page 5

Summary: Comparing Ontology‐Like Things
Ctld. Taxonomy Thesaurus Ontology Data Models Object Models
Vocab
Defined Controlled Controlled vocab. specification of a Specification of Specification of
Definition terms, vocab. in a in a network. conceptualization DB structure a software
controlled hierarchy. application
domain
Free text,
text Strict: tree Broader/narrower Logics,
Logics e g ER
e.g. Hierarchy of
Notation Definition (maybe taxonomy) Taxonomy as diagrams classes, rel's
structure Or: multi- backbone + atts. Entities & attributes &
varies. parent Gnl. association; & relations. Relations methods
Nrl lang Nrl lang Nrl lang def's + Logics w/ fml
fml. Precise,
Precise not Increasingly
def's def's + meaning of links. semantics. logic-based. formal.
meaning of
link B/N: various mng's Isa hierarchy; Focus on data, Isa hierarchy,
Meaning Gnl Assoc n: no
Assoc'n: Dom/Range not meaning Aggregation /
Dictionary; Strictness & specific meaning constraints; (e.g. toss rel'n Composition,
common Precision cardinality. names). Dom/Range
usage varies. constraints;
Isa, partOf, Nrl. language Data dictionary cardinality.
similarTo … comments in the separate.
ontology.
Human HC + HC + Structure Union of all the HC + Structure HC + Structure
Purpose communi- Structure digital libraries;
g ; others & more. (
(and validate)
) software
cation info. base; indexing, databases. systems.
(HC) browsing browsing & search


Ontology

an formal, explicit specification of a shared conceptualization of a domain.

개념화 방법
개념화 수준 a shared conceptualization of a domain.

형식화 수준 formal
자연언어 > 시소러스 >…형식논리

표현 언어
현
XML > Metadata >…온톨로지 언어
explicit specification


Ontology in a nutshell

 Domain model: a formal, explicit specification of a shared conceptual model
 Shared formal conceptualizations of particular domains.
 Provide a common interpretation of topics that can be communicated between people and
applications.
 A formal vocabulary for information exchange.
exchange
 Typically contain hierarchies of concepts and their relations within the domains and
describe each concept’s crucial properties through an attribute-value mechanism.
 Also allow definition of axioms and constraints on particular concepts and properties.
p p p p
 Ontological Commitment: General agreement between Ontologies
 Ontologies are social contracts. 도메인 핵심 개념어
• Agreed, explicit semantics Concept
p
• Understandable to outsiders
• (Often) derived in a community process 구성 데이터 집합

Instance Relation
핵심 개념어간
의미적 관계
개념 관계값

Function Axiom
도메인 지식 규칙


Example: Ontology


Ontology Development Methods
gy p

An Ontology Building Life‐cycle
Investigation I NVESTI GATI ON
- Identify problem and opportunity
- Identify potential solutions
- Feasibility study

CONSTRUCTI ON
ANALSYS
Analysis
A l i
- Knowledge elicitation process
- Capture requirement specification` develop a seed ontology
` domain and goal of ontology ` modify and extend from initial semi-
` design guidelines formal description of the ontology
Construction
` knowledge source
EVALUATI ON ` users and usage scenarios
` competency question
- Technologh-focussed evaluation framework - Support for collaboration through REFI NEM ENT
` Language conformity / Consistency brainstorming
` Interoperability / Turn around ability - Id tif representation llanguages/tools
Identify t ti /t l - FormalizationOntology
phase gy
` Performance / Memory allocation Refinement ` transfer into the target ontology
Evolution
` Scalability / Integration into frameworks ?express in formal representation
` Connectivity language

- User-focussed evaluation
Evaluation
` requirements specification document
` competency questions
M AI NTENACE
` prototype
` Feedback from beta user
` usage patterns Ontology Development -CCentralized and distributed strategy
- Quality and time


Methodologies to develop Ontology

 OTK (On-To-Knowledge) Methodology
 Univ. of Kharlsrhue
 Methontology
 Univ. Politecnica de Madrid
 Cyc methodology
 Manual codification of common sense knowledge extracted by hand, machine learning tools
for new knowledge acquisition
 Uschold and King
 Identify the purpose, build, evaluate, document
 Gruniger and Fox
 Identify the i
Id tif th main scenarios, identify the competency questions, extract relevant concepts
i id tif th t ti t t l t t
and relations, formalize in FOL
 KACTUS methodology
 Ontology built on the basis of an application KB, by abstraction
KB


OTK Methodology

Baseline Target O-based
GO!
ontology
gy ontology
gy Application
pp

ONTOLOGY

Maintenance
Feasibility Ontology Refinement Evaluation &
study Kickoff Evolution

Identify people Requirement Knowledge Check Manage
Focus domain specification elicitation with requirements organizational
Select tools Analyze domain Test in target maintenance
from OTK tool knowledge experts application
i i process (Who is
suite sources Develop and Analyze
responsible?
Develop refine target usage How is it done?)
GO / No GO
decision baseline ontology p
patterns
ontology Deployment


OKT: Ontology Development Activities

 Feasibility study
• Focus the domain, identify people involved

 Kickoff
• Ontology Requirement Spec Doc: potential users, available sources, baseline description
users sources
from competency questions, brainstorming

 Refinement
• Knowledge elicitation with domain experts, formalization

 Inferencing
• F-logic, implementation issues
F logic,

 Evaluation
• Check requirements, tests, quality (Ontoclean)

 Application&Evolution
• Maintenance program, expected lifetime estimation


Feasibility Study

 Requirement
q Analysis
y
 What is the goals of Ontology?
• usage, user specifications,…
 What is relevant to fulfill the goals?
• entities, relationships, restrictions,…
 What need to be modeled?
• key concepts and components …
components,
 What granularity is useful?
 Many
y factors other than technology determine the success
gy
of ontology development.
 Focus domain for ontology
 Identify people involved
 GO / No GO decision


Ontology Kickoff

 Ontology Requirements Specification Document (ORSD)
 Domain & Goal
 Design guidelines
 Available knowledge sources
g
 Potential users and user scenarios
 Applications supported by the ontology
 Analyze knowledge sources
 Develop baseline ontology description
 Draft version, typically most important concepts and relations are identified


Ontology Kickoff


Refinement

 Knowledge elicitation with domain experts
 Refine concepts and relations
 Concepts should be close to entities (physical or logical) and relationships in the domain.
 Typically axioms are identified
 Consider
C id reuse of other ontology.
f th t l

 Formalize
 E.g. F L i
E F-Logic, DAML+OIL
 Axioms depend on language capabilities

 Develop and refine target ontology
 Different tools may help in the implementation.


Refinement


Evaluation

 Check requirements (ORSD)
 Are all CQs answered?
 Is the ontology within the scope?

 Check completeness, consistence and avoid redundancy

 Test in target application
 Analyze usage patterns

 Deploy
D l applications
li ti
 Produce clear informal and formal documentation.


Maintenance & Evolution

 In real world things are changing – and so do the requirements and the
specifications for ontologies!

 Manage organizational maintenance process
g g p
• Who is responsible?
• How is it done?

 Support evolution of ontology-based application(s)
• Identify new requirements
• Ch
Change specifications and accordingly application(s)
ifi ti d di l li ti ( )


Methontology Framework

 Ontology
gy Development Process (
p (which activities)
)
• Management, Development, Support

 Life Cycle (order of activities)
• Evolving Prototype.

 Methodology (how to carry out)
• Specification
• Knowledge Acquisition
• Conceptualization
• Integration
• Implementation
• Evaluation
l i
• Documentation


Methontology: Ontology Development Activities


METHONTOLOGY: Specification

 Produce an Ontology Specification Document (OSD)
 Content
 Purpose
 Scenarios of use
 Possible end users
 Level of formality of the ontology
 Scope
 Granularity
 Technique
 Competency Questions
C t Q ti
 Output
Ontology
Specification
Document


METHONTOLOGY: Conceptualization

 Organize
g and structure the knowledge acquired during the
g q g
knowledge acquisition
 Terms glossary from Ontology Spec Doc
 Primiti es for Modelling Ta onomies
Primitives Taxonomies
• Subclass-of
• Disjoint decomposition
• Exhaustive-Decomposition
• Partition
 Ad-hoc l i
Ad h relations
 Definition of
• Concept Dictionary, Instance Attributes,Class Attributes
• Formal axioms, Rules, Instances


Ontology Building
gy g

Some slides are from University of Manchester and University of Southampton.

Building Ontologies

 No field of Ontological Engineering equivalent to Knowledge or
Software Engineering;

 No sta da d methodologies for bu d g ontologies;
standard et odo og es o building o to og es;

 Such a methodology would include:
 a set of stages that occur when building ontologies;
t f t th t h b ildi t l i
 guidelines and principles to assist in the different stages;
 an ontology life-cycle which indicates the relationships among stages.

 Gruber's guidelines for constructing ontologies are well known.


The Development Lifecycle

 Two kinds of complementary methodologies emerged:
 Stage-based, e.g. TOVE [Uschold96]
 Iterative evolving prototypes, e.g. MethOntology [Gomez Perez94].
 Most have TWO stages:
 Informal stage
• ontology is sketched out using either natural language descriptions or
some diagram technique
 Formal stage
• ontology is encoded in a formal knowledge representation language,
language
that is machine computable
 An ontology should ideally be communicated to people and
gy y p p
unambiguously interpreted by software
 the informal representation helps the former
 the formal representation helps the latter.
latter


A Provisional Methodology

A skeletal methodology and life-cycle for building
ontologies;
i
 Inspired by the software engineering V-process model;

The left side charts The right side charts the
the processes in guidelines, principles and evaluation
building an ontology used to ‘quality assure’ the
ontology
gy

 The overall process moves through a life-cycle.
p g y


Ontology Development


An Ontology Building Life‐cycle
Identify purpose and scope

Consistency
Kn w g
Knowledge acquisition
qu n Checking

Building
Language and
Conceptualisation
C t li ti
representation

Integrating
existing Available
Encoding ontologies development
tools
t l

Evaluation Ontology L
O t l Learning
i


Questions

 How do we obtain our conceptualisation?
 The role of texts, experts and other sources
 How do we derive conceptualisation from texts etc
 How do we cope with tacit conceptualisations?
 How do we use models with the expert?
HHow do we validate the conceptualisation?
d lid t th t li ti ?


Knowledge Acquisition

 The process of capturing knowledge including
various forms of conceptualisation from
whatever source including experts, documents,
manuals, case studies etc.
 Knowledge Elicitation
g
 techniques that are used to acquire knowledge direct
from human experts
 Machine Learning
 use of AI pattern recognition methods to infer patterns
from sets of examples
f t f l


First Steps ‐
Initial Understanding of the Domain
Initial Understanding of the Domain

 Problem Description
p
 List knowledge resources (verify that knowledge really
exists)
 Experts, Technical Authorities
 Text Books, Training Material
 M
Manuals and P
l d Procedures
d
 Databases and Case Histories
 Produce domain “yellow pages”
 Establish performance metrics
 Initial task environment analysis


Document and Text Analysis

 Look at the structure
 how material is organised into topics and sub-topics
 Co te t a a ys s
Content analysis
 Extract major linguistic categories
• nouns - objects and concepts
• verbs – relations
• modifiers - properties and values
• connectives - rules and links
 Use Intermediate representations
 Pseudo production rules
 Small concept networks and hierarchies


Problems of Document and Text Analysis

 Documents and texts are written for specific p p
p purposes that
may not reveal real knowledge or explicit
conceptualisations
 Duty l
logs and rosters
d
 Teaching texts
 All textual
t t l analysis is a form of content analysis - th
l i i f f t t l i the
interpreter may or may not be imputing the correct
conceptualisation
p
 Difficult to reconstruct the context – need to capture
acquisition and design rationales


Session Plan

 The importance of an acquisition p
p q plan
 A detailed agenda of what is to be covered during a KA
session.
 Should include:
 an introduction describing the objectives
 description of the techniques to be used
 questions to be asked (if required)
 timings
 Should be sent to the expert at least one day in advance of
the session


Knowledge Acquisition (KA)Techniques

 Methods that help acquire and validate knowledge from an expert
during a KA session.

 Three important types:
ee po ta t
 natural techniques
 contrived techniques
 modelling and mediating representation techniques


KA Typology
unstructured interview

interviews semi-structured
interview

structured interview
natural techniques
observation techniques

group meetings

questionnaires

card sorting

three card trick

rep grid technique
KA techniques
limited time
contrived techniques constrained tasks
limited information

20-questions

commentating

teach back

laddering

process mapping
modelling techniques
concept mapping

state diagram mapping


Natural Techniques

 KA techniques that involve the expert performing tasks they
would normally do as part of their job.
 V i ti
Variations:
 Interviews
 Observational techniques
q
 (Group meetings)
 (Questionnaires)


Interviews

 KA technique in which the knowledge engineer asks
q g g
questions of the expert or end user.
 Essential method for acquiring explicit conceptualisations
and k
d knowledge, b t poor f t it k
l d but for tacit knowledge.
l d
 Variations:
 Unstructured interview
 Semi-structured interview
 Structured interview


Unstructured Interview

A
An i
interview in which the knowledge engineer has no pre-
i i i i
defined questions.
 Basically a chat to find out broad aspects of the expert s
expert’s
knowledge.
 An aid to designing a KA session plan.
g g p


Semi‐structured Interview

 An interview in which pre-prepared q
p p p questions are used to
focus and scope what is covered
 Also involves unprepared supplementary questions for
clarification and probing.
l ifi ti d bi
 Questions should be:
 designed carefully
 sent to the expert beforehand
 asked verbatim (read-out as written)
 include timings
 The recommended interview technique at the start of most
KA projects.
projects


Structured Interview

 An interview in which the knowledge engineer follows a
g g
pre-defined set of structured questions but can ask no
supplementary questions.

 Often involves filling-in a matrix or generic headings.


Contrived Techniques

 KA techniques that involve the expert performing tasks
they would not normally do as part of their job.
 Most of these techniques come from psychology.
 U f l f capturing t it knowledge, excellent for
Useful for t i tacit k l d ll t f
conceptualisations.
 Important types:
 card sorting
 three card trick
 repertory grid technique
 constrained tasks
 20 questions
20-questions
 commentating
 teach back


Card Sorting

 KA technique in which a collection of concepts ( other
q p (or
knowledge objects) are written on separate cards and
sorted into piles by an expert in order to elicit classes based
on attributes
attributes.
 Also enables significant elicitation of properties and
dimensions
 Used to capture concept knowledge and tacit knowledge
 Use in conjunction with triadic method
 Can also sort objects or pictures instead of cards


Triadic Elicitation Method

 KA technique used to capture the way in which an expert
q p y p
views the concepts in a domain.
 Involves presenting three random concepts and asking in
what way t of th
h t two f them are similar but diff
i il b t different f
t from the
th
other one.
 Answer will give an attribute.
 A good way of acquiring tacit knowledge.

Book Paper Computer ???


Repertory Grid technique

 KA technique used for a number of p p
q purposes:
 to elicit attributes for a set of concepts
 to rate concepts against attributes using a numerical scale
 uses statistical analysis to arrange and group similar concepts and
t ti ti l l i t d i il t d
attributes
A useful way of capturing concept knowledge and tacit
knowledge
 Requires special software (PC-PACK)


Repertory Grid Example


Constrained Tasks

 KA technique in which the expert p
q p performs a task they
y
would normally do, but with constraints.
 Variations:
 limited time
 limited data
 Useful for focusing the expert on essential knowledge and
priorities


20‐Questions

 KA technique in which the expert asks y
q p yes/no q
questions to
the knowledge engineer in order to deduce an answer.

 The knowledge engineer need not know much about the
domain, or have an answer in mind, just answer “yes” or
“no” randomly.
no

 The q
questions asked pprovide a ggood way of q
y quickly
y
acquiring attributes in a prioritised order.


Commentating and protocol generation

 KA technique in w c the expert p ov des a
ec que which e e pe provides
running commentary of their own or another’s
task performance.
 A valuable method for acquiring process
knowledge and tacit knowledge.
 Variations:
 self-reporting
p g
 imaginary self-reporting
 self-retrospective
 shadowing
 retrospective shadowing


Teach back

 KA technique in which the knowledge engineer explains
knowledge from part of the domain back to the expert.

 The expert then makes comments.

 Helps reveal misunderstandings and clarifies terminology.


Laddering

 KA technique that involves the co s uc o ,
ec que vo ves e construction,
modification and validation of trees.
 A valuable method for acquiring concept
q g p
knowledge and, to a lesser extent, process
knowledge.
 Can make use of various trees:
 concept tree
p
 composition tree
 attribute tree
 process tree
 decision tree
 cause tree

Modelling Techniques

 KA techniques that use knowledge models as the
focus for discussion, validation and modification of
knowledge.
 Can use any form of model, but important types
include:
 process mapping
 concept mapping
 state diagram mapping


Process Mapping

 KA technique that involves the construction, modification
and validation of process maps.

A valuable method for acquiring process knowledge and
tacit k
t it knowledge.
l d


Process Map ‐ Example
aims of research information sources

T1
Conduct literature review
senior investigator

literature review is empirical research required?
resources available

yes no

T2
Conduct empirical research
researcher

empirical results

T3
Write-up research research report
h t
senior investigator


Concept Mapping

and validation of concept maps.
p p

A good method for acquiring concept knowledge.


Concept Map ‐ Example

written by
itt b
Author
Oliver Twist is a
Charles Dickens
wrote
is a
shorter wrote admired
is a than Dostoevsky

Bleak House wrote on

Book is a born in

Russia
has part
Page Paper
made from


State Diagram Mapping

and validation of a state diagram.

A different approach to process mapping.

 Useful for capturing process knowledge, concept
knowledge and tacit knowledge.


State Diagram ‐ Example

Your number is dialed
On hook - no ringing On hook - ringing

Lift receiver
Person at other end rings off

Lift receiver
Off hook - conversation

Hang up Phone i answered at
Ph is d t
Off hook - dialing tone
other end

Hang up
Dial number Off hook - ringing tone
Off hook - dialing

Complete dialing
C l t di li


Ontology Building Summary
gy g y

Designing a KA plan

 We need different techniques because
eed d e e ec ques bec use
 there are different types of knowledge
 acquiring a certain type knowledge is made more efficient
q g yp g
using the right technique
• e.g. can't get tacit knowledge using interviews

 Three types of KA techniques
 Natural (e.g. interviews, observation)
 Contrived (e.g. commentary, rep grid, 20-questions)
 Modelling (e.g. process mapping)


Designing a KA Session Plan

1.
. Be clear what knowledge you want from the
ece w ow edge w o e
session.

2. Write an introduction summarising what
knowledge y want from the session.
g you

3.
3 Select the best KA technique/s to use.
use
 How do we do this? …..



4. Place the techniques selected in a clear and
. ce e ec ques se ec ed ce d
logical order
 e.g. interview questions first
g q
 e.g. commentary and protocols before process mapping

5. Always end the session plan with the following
question:
 "Bearing in mind the goals of this session, what vital
knowledge have we not yet covered“

6. Assign timings to each section.



7. If possible, check the session p
p , plan with y
your p j
project
manager or colleague and make amendments if
necessary.

8. Send (email, fax) the session plan to the expert at least
one day before the session.

9. Make any changes the expert suggests.
y g p gg

10. During the session, stick to the plan and keep to the
timings


Which KA technique to use

 Decide
ec de what type/s o co cep u s o and
w ype/s of conceptualisation d
knowledge you need from the expert
 Is it structural objects oriented knowledge? (i.e. of concepts,
j g ( p
attributes, states & relationships)
 Is it process knowledge? (i.e. how to do things)
 Is it explicit knowledge? (i.e. easily explained)
 Is it tacit knowledge? (i.e. not easily explained)

 Use the diagram shown next to select the best
technique/s to use..
use


Which KA technique to use


PC PACK5
http://www.epistemics.co.uk/Notes/55-0-0.htm

Ladder Matrix Annotation

Diagram Protocol Publisher


Types of Ontology Tools

 Ontology
gy development tools
p
 Editors and browsers
 Graphical editors
 Translators
 Ontology library management
 Ontology documentation
 Ontology population
 Evaluation
 Evolution
 Merge and alignement tools
 Ontology-based annotation tools
QQuerying tools and inference engines
i t l di f i
 Ontology learning tools


감사합니다….
skhan@wku.ac.kr
skhan@wku ac kr

References

Methodologies for building ontologies from the scratch
 Cyc
C methodology URL: http://www.cyc.com
//
 Uschold and King URL: Not available
 Grüninger and Fox URL: Not available
 KACTUS methodology URL: Not available
 METHONTOLOGY URL: Not available
 SENSUS methodology URL: Not available
 On To Knowledge
On-To-Knowledge Methodology URL: http://www ontoknowledge org/
http://www.ontoknowledge.org/

Methodologies for reengineering ontologies
 Method for reengineering ontologies integrated in Methontology URL: Not available
g g g g gy

Methodologies for cooperative construction of ontologies
 CO4 methodology URL: Not available
 (KA)2 methodology URL: Not available


References

Ontology learning methodologies
 Aussenac-Gille's and colleagues methodology URL: http://www.biomath.jussieu.fr/TIA/
 Maedche and colleagues' methodology URL: Not available

Ontology merge methodologies
O t l th d l i
 FCA-merge URL: Not available
 PROMPT URL: Not available
 ONIONS URL: Not a ailable
available

Ontology evaluation methods
 OntoClean: Guarino's group methodology URL: Not available
 Gómez Pérez's evaluation methodology URL: Not available


References

Environments for building ontologies
 APECKS URL: N available
URL Not il bl
 Apollo URL: http://apollo.open.ac.uk
 CODE4 URL: http://www.csi.uottawa.ca/~doug/CODE4.html
 CO4 URL: http://co4.inrialpes.fr/
 DUET (DAML UML Enhanced Tool) URL:
http://grcinet.grci.com/maria/www/CodipSite/Tools/Tools.html
 GKB-Editor URL: http://www.ai.sri.com/~gkb/
 IKARUS URL: http://www.csi.uottawa.ca/~kavanagh/Ikarus/IkarusInfo.html
p g
 JOE (Java Ontology Editor) URL: http://www.engr.sc.edu/research/CIT/demos/java/joe/
 OilEd URL: http://img.cs.man.ac.uk/oil/
 OntoEdit URL: http://ontoserver .aifb.uni- karlsruhe.de/ontoedit /
 Ontolingua URL: http://www-ksl-svc.stanford.edu:5915/
http://www ksl svc stanford edu:5915/
 Ontological Constraints Manager (OCM) URL: http://www.ecs.soton.ac.uk/~yk1/rp956.ps
 Ontology Editor by Steffen Schulze -Kremer URL: http://igd.rz-berlin.mpg.de/~www/prolog/oe.html
 OntoSaurus URL: http://www.isi.edu/isd/ontosaurus.html
 Protégé-2000 URL: http://protege.stanford.edu
 VOID URL: http://www.swi.psy.uva.nl/projects/Kactus/toolkit/about.html
 WebODE URL: http://delicias.dia.fi.upm.es/webODE/index.html
 WebOnto URL: http://kmi.open.ac.uk/projects/webonto/


References

Ontology merging and integration tools
 Chimaera URL: http://www.ksl.stanford.edu/software/chimaera/
 FCA-Merge Tool URL: Not available .
 PROMPT URL: http://protege.stanford.edu/plugins/prompt/prompt.html
Ontology-based annotation t l
O t l b d t ti tools
 OntoMarkupAnnotation Tool URL: http://kmi.open.ac.uk/projects/akt /
 OntoMat URL: http://ontobroker.semanticweb.org/annotation/ontomat/index.html
 OntoAnnotate URL: http://www ontoprise .de/com/co_produ_tool2.htm
http://www.ontoprise de/com/co produ tool2 htm
 SHOE Knowledge Annotator URL:
http://www.cs.umd.edu/projects/plus/SHOE/KnowledgeAnnotator.html
 UBOT DAML Annotation URL: http://ubot.lockheedmartin.com/ubot/
p
Ontology learning tools
 ASIUM URL: http://www.lri.fr/~faure/Demonstration.UK/Presentation_Demo.html
 CORPORUM-OntoBuilder URL: http://ontoserver .cognit .no
 LTG Text Processing Workbench URL:
http://www.ltg.ed.ac.uk/%7Emikheev/workbench.html
 Text-To-Onto URL: http://ontoserver .aifb.uni- karlsruhe.de/texttoonto/


Ontology Dev

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Was ist angesagt?

Was ist angesagt? (16)

Andere mochten auch

Andere mochten auch (16)

Ähnlich wie Ontology Dev

Ähnlich wie Ontology Dev (20)

Mehr von Won Kwang University

Mehr von Won Kwang University (13)

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

Ontology Dev