Ontology Dev

O t l E i iOntology Engineering:Design and Practicesg

2009년 03월 21일

한 성 국의미기술 연구소 / 원광대학교의미기술 연구소 / 원광대학교

2009-03-20 [email protected] 1

Agenda

Review of OntologyReview of Ontology

Ontology Development Methods gy p

Ontology BuildingOntology Building

Ontology Building Summarygy g y

2009-03-20 [email protected] page 2

Review of Ontologygy

A Plethora of 'Ontology-Like Things’

Glossaries / Controlled Vocabularies Data and Document Metamodels

structured Glossaries formal

Restricted Logics(OWL, F-logic)

ad hoc Hierarchies

(Yahoo!)

XML Schema

TermsTaxonomies

Thesauri XML DTDs

‘ordinary’Glossaries

Principled, informal

Data Models(UML, STEP)Glossaries

Data Dictionaries

(EDI)

General Logic

Frames(OKBC)

informal taxonomies

(UML, STEP)

DB Schema(EDI)

Formal Knowledge Bases & InferenceInformal Taxonomies and ThesauriSchema

M Diff t W f E i M iMany Different Ways of Expressing Meaning


Semantics-Related Technologies

ControlledControlledVocabularyVocabulary GroupingGrouping ClassificationClassification+

ControlledControlledVocabularyVocabulary

HierarchicalHierarchicalStructureStructure TaxonomyTaxonomy+


TermTermRelationsRelations ThesaurusThesaurus+


Semantic Relation,Semantic Relation,Constraints, Axioms, RulesConstraints, Axioms, Rules OntologyOntology+

OntologyOntology InstancesInstances KnowledgeKnowledgeKnowledgeKnowledgeBaseBase

+

2009-03-20 page [email protected]

Summary: Comparing Ontology‐Like Things Ctld.

VocabTaxonomy Thesaurus Ontology Data Models Object Models

Defined Controlled Controlled vocab. specification of a Specification of Specification ofDefinition

Defined terms, controlled

Controlled vocab. in a hierarchy.

Controlled vocab. in a network.

specification of a conceptualization

Specification of DB structure

Specification of a software application domain

Free text Strict: tree Broader/narrower Logics e g ER Hierarchy ofNotation

Free text, Definition structure varies.

Strict: tree

Or: multi-parent

Broader/narrower (maybe taxonomy)

Gnl. association;

Logics, Taxonomy as backbone + atts. & relations.

e.g. ER diagrams Entities & Relations

Hierarchy of classes, rel'sattributes & methods

Nrl lang Nrl lang Nrl lang def's + Logics w/ fml Precise not Increasingly

Meaning

Nrl lang def's

Nrl lang def's + meaning of link

Nrl lang def's + meaning of links.

B/N: various mng's Gnl Assoc'n: no

Logics w/ fml. semantics.

Isa hierarchy; Dom/Range

Precise, not logic-based.

Focus on data, not meaning

Increasingly formal.

Isa hierarchy, Aggregation /Meaning

Dictionary; common usage

Strictness & Precision varies.Isa, partOf,

Gnl Assoc n: no specific meaning

Dom/Range constraints;cardinality.

Nrl. language

not meaning (e.g. toss rel'n names).

Data dictionary

Aggregation / Composition, Dom/Range constraints;cardinality.

similarTo … comments in the ontology.

separate.

PurposeHuman communi-

HC + Structure

HC + Structure digital libraries;

Union of all the others & more.

HC + Structure (and validate)

HC + Structure software

2009-03-20 [email protected]

Purposecation (HC)

info. base; browsing

g ;indexing, browsing & search

( )databases. systems.

page 6

Ontology

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

a shared conceptualization of a domain.개념화 방법개념화 수준

formal형식화 수준자연언어 > 시소러스 >…형식논리

explicit specification표현 언어 explicit specification 현 언어XML > Metadata >…온톨로지 언어


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 A formal vocabulary for information 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.

• Agreed, explicit semantics Concept도메인 핵심 개념어

• Understandable to outsiders• (Often) derived in a community process

Relation

p

Instance

구성 데이터 집합

Axiom

핵심 개념어간의미적 관계

개념 관계값

page 82009-03-20 [email protected]

AxiomFunction도메인 지식 규칙

Example: Ontology

2009-03-20 page [email protected]

Ontology Development Methodsgy p

An Ontology Building Life‐cycle

Investigation I NVESTI GATI ON- Identify problem and opportunity

A l i

- Identify potential solutions- Feasibility study

ANALSYSCONSTRUCTI ON

Analysis

Construction

ANALSYS

- Capture requirement specification ` domain and goal of ontology ` design guidelines ` knowledge source

- Knowledge elicitation process` develop a seed ontology

` modify and extend from initial semi- formal description of the ontology

Ontology

knowledge source ` users and usage scenarios ` competency question- Support for collaboration through brainstorming Id tif t ti l /t l

EVALUATI ON

- Technologh-focussed evaluation framework ` Language conformity / Consistency ` Interoperability / Turn around ability

REFI NEM ENT

- Formalization phaseRefinement

gy

Evolution- Identify representation languages/tools Interoperability / Turn around ability

` Performance / Memory allocation ` Scalability / Integration into frameworks ` Connectivity

Formalization phase ` transfer into the target ontology ?express in formal representation language

Evaluation- User-focussed evaluation` requirements specification document

` competency questions ` prototype ` Feedback from beta user

M AI NTENACE

C


Ontology Development ` usage patterns - Centralized and distributed strategy- Quality and time

page 11

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 Uschold and King Identify the purpose, build, evaluate, document

Gruniger and FoxId tif th i i id tif th t ti t t l t t Identify the main scenarios, identify the competency questions, extract relevant concepts and relations, formalize in FOL

KACTUS methodology Ontology built on the basis of an application KB by abstractionOntology built on the basis of an application KB, by abstraction


OTK Methodology

Target ontology

O-based Application

Baseline ontologyGO!

ONTOLOGY

gy ppgy

OntologyKickoff

Refinement EvaluationMaintenance

&Evolution

Feasibilitystudy

Check requirementsTest in target

i i

Requirement specificationAnalyze

Knowledge elicitation with domain experts

Manage organizational maintenance process (Who is

Identify peopleFocus domainSelect tools

applicationAnalyze usage patterns

knowledge sourcesDevelop baseline

experts Develop and refine target ontology

process (Who is responsible? How is it done?)

from OTK tool suiteGO / No GO decision


pDeploymentontology

decision

page 13

OKT: Ontology Development Activities

Feasibility study• Focus the domain, identify people involved

Kickoff• Ontology Requirement Spec Doc: potential users available sources baseline description• Ontology Requirement Spec Doc: potential users, available sources, baseline description

from competency questions, brainstorming

Refinement• Knowledge elicitation with domain experts, formalization

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

Evaluation• Check requirements, tests, quality (Ontoclean)

Application&Evolution• Maintenance program, expected lifetime estimation


Feasibility Study

Requirement Analysisq y What is the goals of Ontology?

• usage, user specifications,…

What is relevant to fulfill the goals? What is relevant to fulfill the goals?• entities, relationships, restrictions,…

What need to be modeled?key concepts and components• key concepts and components, …

What granularity is useful?

Many factors other than technology determine the success y gyof ontology development.

Focus domain for ontology Identify people involvedGO / No GO decision


Ontology Kickoff

Ontology Requirements Specification Document (ORSD) Domain & Goal Design guidelines Available knowledge sourcesg Potential users and user scenarios Applications supported by the ontology

Analyze knowledge sources 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

C id f th t l Consider reuse of other ontology.

FormalizeE F L i DAML+OIL E.g. F-Logic, DAML+OIL

Axioms depend on language capabilities

Develop and refine target ontology 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 Test in target application Analyze usage patterns

D l li ti Deploy applications 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 processg g p• Who is responsible?• How is it done?

Support evolution of ontology-based application(s)• Identify new requirements

Ch ifi ti d di l li ti ( )• Change specifications and accordingly application(s)


Methontology Framework

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

Life Cycle (order of activities)• Evolving Prototype.

Methodology (how to carry out)• Specification• Specification• Knowledge Acquisition• Conceptualization• Integration• Implementation

l i• Evaluation• Documentation


Methontology: Ontology Development Activities


METHONTOLOGY: Specification

Produce an Ontology Specification Document (OSD) Content Purpose Scenarios of useScenarios of use Possible end users Level of formality of the ontology Scope Scope Granularity

TechniqueC t Q ti Competency Questions

OutputOntology

SpecificationSpecificationDocument


METHONTOLOGY: Conceptualization

Organize and structure the knowledge acquired during the g g q gknowledge acquisition Terms glossary from Ontology Spec Doc

Primiti es for Modelling Ta onomies Primitives for Modelling Taxonomies• Subclass-of• Disjoint decomposition• Exhaustive-Decomposition• Partition

Ad h l i Ad-hoc relations Definition of

• Concept Dictionary, Instance Attributes,Class Attributes• Formal axioms, Rules, Instances


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

Such a methodology would include:t f t th t h b ildi t l i a set of stages that occur when building ontologies;

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: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• ontology is encoded in a formal knowledge representation language,

that is machine computable

An ontology should ideally be communicated to people and gy y p punambiguously interpreted by software the informal representation helps the former

the formal representation helps the latter the formal representation helps the latter.


A Provisional Methodology

A skeletal methodology and life-cycle for building iontologies;

Inspired by the software engineering V-process model;

The left side charts the processes in

building an ontology

The right side charts the guidelines, principles and evaluation

used to ‘quality assure’ the ontology

The overall process moves through a life-cycle.

gy

p g y


Ontology Development


An Ontology Building Life‐cycle

Identify purpose and scope

Knowledge acquisitionConsistency

CheckingKn w g qu n

Language and C t li tiBuilding

Language and representationConceptualisation

Integrating Available

development t l

Integrating existing

ontologiesEncoding

Evaluation

tools

O t l L iEvaluation Ontology Learning


Questions

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


Knowledge Acquisition

The process of capturing knowledge includingThe process of capturing knowledge including various forms of conceptualisation from whatever source including experts, documents, manuals, case studies etc.Knowledge Elicitationg

techniques that are used to acquire knowledge direct from human experts

Machine Learning use of AI pattern recognition methods to infer patterns

f t f lfrom sets of examples


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

Problem DescriptionpList knowledge resources (verify that knowledge really

exists) Experts, Technical Authorities Text Books, Training Material

M l d P d Manuals and Procedures Databases and Case Histories

Produce domain “yellow pages”Produce domain “yellow pages”Establish performance metrics Initial task environment analysis Initial task environment analysis


Document and Text Analysis

Look at the structure how material is organised into topics and sub-topics Content analysis Co te t a a ys s Extract major linguistic categories

• nouns - objects and concepts• verbs – relations• modifiers - properties and values• connectives rules and links• connectives - rules and links

Use Intermediate representations Pseudo production rulesPseudo production rules Small concept networks and hierarchies


Problems of Document and Text Analysis

Documents and texts are written for specific purposes that p p pmay not reveal real knowledge or explicit conceptualisations

l d Duty logs and rosters Teaching texts

All t t l l i i f f t t l i thAll textual analysis is a form of content analysis - the interpreter may or may not be imputing the correctconceptualisationp

Difficult to reconstruct the context – need to capture acquisition and design rationales


Session Plan

The importance of an acquisition planp q pA 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 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 types: natural techniques contrived techniques modelling and mediating representation techniques modelling and mediating representation techniques


KA Typology

interviews

unstructured interview

semi-structured interview

natural techniques

observation techniques

group meetings

structured interview

questionnaires

card sorting

three card trick

KA techniques

contrived techniques

rep grid technique

constrained taskslimited time

limited information

20-questions

commentating

limited information

modelling techniques

teach back

laddering

process mappingmodelling 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 tiVariations: Interviews Observational techniquesq (Group meetings) (Questionnaires)


Interviews

KA technique in which the knowledge engineer asks q g gquestions of the expert or end user.

Essential method for acquiring explicit conceptualisations d k l d b t f t it k l dand knowledge, but poor for tacit knowledge.

Variations: Unstructured interview Unstructured interview Semi-structured interview Structured interview


Unstructured Interview

A i i i i iAn interview in which the knowledge engineer has no pre-defined questions.

Basically a chat to find out broad aspects of the expert’sBasically a chat to find out broad aspects of the expert s knowledge.

An aid to designing a KA session plan.g g p


Semi‐structured Interview

An interview in which pre-prepared questions are used to p p p qfocus and scope what is covered

Also involves unprepared supplementary questions for l ifi ti d biclarification and probing.

Questions should be: designed carefully 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 projectsKA projects.


Structured Interview

An interview in which the knowledge engineer follows a g gpre-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 t i t it k l d ll t fUseful for capturing tacit knowledge, excellent for conceptualisations.

Important types: Important types: card sorting three card trick repertory grid technique constrained tasks 20-questions20 questions commentating teach back


Card Sorting

KA technique in which a collection of concepts (or other q p (knowledge objects) are written on separate cards and sorted into piles by an expert in order to elicit classes based on attributeson attributes.

Also enables significant elicitation of properties and dimensions

Used to capture concept knowledge and tacit knowledgeUse in conjunction with triadic methodCan 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 pviews the concepts in a domain.

Involves presenting three random concepts and asking in h t t f th i il b t diff t f thwhat way two of them are similar but different from the

other one.Answer will give an attribute.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 purposes:q p p to elicit attributes for a set of concepts to rate concepts against attributes using a numerical scale

t ti ti l l i t d i il t d uses statistical analysis to arrange and group similar concepts and 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 performs a task they q p p ywould normally do, but with constraints.

Variations: limited time limited data

Useful for focusing the expert on essential knowledge andUseful for focusing the expert on essential knowledge and priorities


20‐Questions

KA technique in which the expert asks yes/no questions to q p y qthe 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 randomly.

The questions asked provide a good way of quickly q p g y q yacquiring attributes in a prioritised order.


Commentating and protocol generation

KA technique in which the expert provides a ec que w c e e pe p ov desrunning commentary of their own or another’s task performance.A valuable method for acquiring process

knowledge and tacit knowledge.Variations: self-reportingp 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 construction, ec que vo ves e co s uc o ,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 treep 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 t it k l dtacit knowledge.


Process Map ‐ Example

aims of research information sources

T1Conduct literature review senior investigator

literature reviewresources available is empirical research required?

yes no

T2Conduct empirical research

researcher

empirical results

T3h tWrite-up research

senior investigatorresearch report


Concept Mapping

KA technique that involves the construction, modification and validation of concept maps.p p

A good method for acquiring concept knowledge.


Concept Map ‐ Example

itt b

Oliver TwistCharles Dickenswrote

written by

Authoris a

wrote

wroteshorter is a

is a

admired

Bleak House

thanis a

wrote on

Dostoevsky

Book is a

Russia

born in

Page Paperhas part

made from


State Diagram Mapping

KA technique that involves the construction, modification and validation of a state diagram.

A different approach to process mapping.

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


State Diagram ‐ Example

On hook - no ringing On hook - ringing

Your number is dialed

Person at other end rings offLift receiver

Lift receiver

Off hook - conversation

Ph i d tHang upOff hook - dialing tone Phone is answered at other end

Hang up

Hang up

Off hook - dialingOff hook - ringing toneDial number

C l t di liComplete dialing


Ontology Building Summarygy g y

Designing a KA plan

We need different techniques becauseWe 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 . e c e w ow edge you w o esession.

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

3 Select the best KA technique/s to use3. Select the best KA technique/s to use. How do we do this? …..



4. Place the techniques selected in a clear and . ce e ec ques se ec ed c e dlogical order e.g. interview questions firstg 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 plan with your project p , p y p jmanager or colleague and make amendments if necessary.

8. Send (email, fax) the session plan to the expert at least one day before the session.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 what type/s of conceptualisation and ec de w ype/s o co cep u s o dknowledge 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 usetechnique/s to use..


Which KA technique to use


PC PACK5

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

Ladder Matrix Annotation

Diagram Protocol PublisherDiagram Protocol Publisher


Types of Ontology Tools

Ontology development toolsgy p Editors and browsers Graphical editors TranslatorsTranslators Ontology library management Ontology documentation Ontology population Ontology population Evaluation Evolution

Merge and alignement toolsOntology-based annotation tools

Q i t l d i f iQuerying tools and inference enginesOntology learning tools


감사합니다….skhan@wku ac [email protected]

References

Methodologies for building ontologies from the scratchC // Cyc methodology URL: http://www.cyc.com

Uschold and King URL: Not available Grüninger and Fox URL: Not available KACTUS methodology URL: Not available KACTUS methodology URL: Not available METHONTOLOGY URL: Not available SENSUS methodology URL: Not available On-To-Knowledge Methodology URL: http://www ontoknowledge org/ On To Knowledge Methodology URL: http://www.ontoknowledge.org/

Methodologies for reengineering ontologies Method for reengineering ontologies integrated in Methontology URL: Not availableg 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

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

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


References

Environments for building ontologiesAPECKS URL N il bl APECKS URL: Not available

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.htmlp 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/ Ontolingua URL: 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/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

O t l b d t ti t lOntology-based annotation 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 OntoAnnotate URL: 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/


Technology

Ontology Dev