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Approaches to Ontology Development Review of Ontology Development methodology
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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
2009-03-20 [email protected] page 4
Semantics-Related Technologies
ControlledControlledVocabularyVocabulary GroupingGrouping ClassificationClassification+
ControlledControlledVocabularyVocabulary
HierarchicalHierarchicalStructureStructure TaxonomyTaxonomy+
ControlledControlledVocabularyVocabulary
TermTermRelationsRelations ThesaurusThesaurus+
ControlledControlledVocabularyVocabulary
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
2009-03-20 [email protected] page 7
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
2009-03-20 [email protected]
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
2009-03-20 [email protected] page 12
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
2009-03-20 [email protected]
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
2009-03-20 [email protected] page 14
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
2009-03-20 [email protected] page 15
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
2009-03-20 [email protected] page 16
Ontology Kickoff
2009-03-20 [email protected] page 17
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.
2009-03-20 [email protected] page 18
Refinement
2009-03-20 [email protected] page 19
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.
2009-03-20 [email protected] page 20
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)
2009-03-20 [email protected] page 21
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
page 222009-03-20 [email protected]
Methontology: Ontology Development Activities
page 232009-03-20 [email protected]
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
2009-03-20 [email protected] page 24
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
page 252009-03-20 [email protected]
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.
2009-03-20 [email protected] page 27
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.
2009-03-20 [email protected] page 28
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
2009-03-20 [email protected] page 29
Ontology Development
2009-03-20 [email protected] page 30
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
2009-03-20 [email protected] page 31
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?
2009-03-20 [email protected] page 32
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
2009-03-20 [email protected] page 33
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
2009-03-20 [email protected] page 34
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
2009-03-20 [email protected] page 35
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
2009-03-20 [email protected] page 36
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
2009-03-20 [email protected] page 37
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
2009-03-20 [email protected] page 38
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
2009-03-20 [email protected] page 39
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)
2009-03-20 [email protected] page 40
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
2009-03-20 [email protected] page 41
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
2009-03-20 [email protected] page 42
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.
2009-03-20 [email protected] page 43
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.
2009-03-20 [email protected] page 44
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
2009-03-20 [email protected] page 45
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
2009-03-20 [email protected] page 46
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 ???
2009-03-20 [email protected] page 47
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)
2009-03-20 [email protected] page 48
Repertory Grid Example
2009-03-20 [email protected] page 49
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
2009-03-20 [email protected] page 50
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.
2009-03-20 [email protected] page 51
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
2009-03-20 [email protected] page 52
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.
2009-03-20 [email protected] page 53
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
2009-03-20 [email protected] page 54
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
2009-03-20 [email protected] page 55
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.
2009-03-20 [email protected] page 56
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
2009-03-20 [email protected] page 57
Concept Mapping
KA technique that involves the construction, modification and validation of concept maps.p p
A good method for acquiring concept knowledge.
2009-03-20 [email protected] page 58
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
2009-03-20 [email protected] page 59
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.
2009-03-20 [email protected] page 60
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
2009-03-20 [email protected] page 61
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)
2009-03-20 [email protected] page 63
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? …..
2009-03-20 [email protected] page 64
Designing a KA Session Plan
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.
2009-03-20 [email protected] page 65
Designing a KA Session Plan
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
2009-03-20 [email protected] page 66
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..
2009-03-20 [email protected] page 67
Which KA technique to use
2009-03-20 [email protected] page 68
PC PACK5
http://www.epistemics.co.uk/Notes/55-0-0.htm
Ladder Matrix Annotation
Diagram Protocol PublisherDiagram Protocol Publisher
2009-03-20 [email protected] page 69
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
page 702009-03-20 [email protected]
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
page 722009-03-20 [email protected]
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
page 732009-03-20 [email protected]
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/
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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/
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