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Ontology Dev
Ontology Dev
Ontology Dev
Ontology Dev
Ontology Dev
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Ontology Dev
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Ontology Dev

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Approaches to Ontology Development …

Approaches to Ontology Development
Review of Ontology Development methodology

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  • 1. 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
  • 2. 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
  • 3. Review of Ontology gy
  • 4. 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 2009-03-20 skhan@wku.ac.kr page 4
  • 5. 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
  • 6. 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 2009-03-20 skhan@wku.ac.kr page 6
  • 7. 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 2009-03-20 skhan@wku.ac.kr page 7
  • 8. 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 2009-03-20 skhan@wonkwang.ac.kr page 8
  • 9. Example: Ontology 2009-03-20 skhan@wonkwang.ac.kr page 9
  • 10. Ontology Development Methods gy p
  • 11. 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 2009-03-20 skhan@wku.ac.kr page 11
  • 12. 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 2009-03-20 skhan@wku.ac.kr page 12
  • 13. 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 2009-03-20 skhan@wku.ac.kr page 13
  • 14. 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 2009-03-20 skhan@wku.ac.kr page 14
  • 15. 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 2009-03-20 skhan@wku.ac.kr page 15
  • 16. 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 2009-03-20 skhan@wku.ac.kr page 16
  • 17. Ontology Kickoff 2009-03-20 skhan@wku.ac.kr page 17
  • 18. 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. 2009-03-20 skhan@wku.ac.kr page 18
  • 19. Refinement 2009-03-20 skhan@wku.ac.kr page 19
  • 20. 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. 2009-03-20 skhan@wku.ac.kr page 20
  • 21. 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 ( ) 2009-03-20 skhan@wku.ac.kr page 21
  • 22. 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 2009-03-20 skhan@wku.ac.kr page 22
  • 23. Methontology: Ontology Development Activities 2009-03-20 skhan@wku.ac.kr page 23
  • 24. 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 2009-03-20 skhan@wku.ac.kr page 24
  • 25. 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 2009-03-20 skhan@wku.ac.kr page 25
  • 26. Ontology Building gy g Some slides are from University of Manchester and University of Southampton.
  • 27. 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. 2009-03-20 skhan@wku.ac.kr page 27
  • 28. 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 2009-03-20 skhan@wku.ac.kr page 28
  • 29. 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 2009-03-20 skhan@wku.ac.kr page 29
  • 30. Ontology Development 2009-03-20 skhan@wku.ac.kr page 30
  • 31. 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 2009-03-20 skhan@wku.ac.kr page 31
  • 32. 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 ? 2009-03-20 skhan@wku.ac.kr page 32
  • 33. 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 2009-03-20 skhan@wku.ac.kr page 33
  • 34. 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 2009-03-20 skhan@wku.ac.kr page 34
  • 35. 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 2009-03-20 skhan@wku.ac.kr page 35
  • 36. 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 2009-03-20 skhan@wku.ac.kr page 36
  • 37. 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 2009-03-20 skhan@wku.ac.kr page 37
  • 38. 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 2009-03-20 skhan@wku.ac.kr page 38
  • 39. 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 2009-03-20 skhan@wku.ac.kr page 39
  • 40. 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) 2009-03-20 skhan@wku.ac.kr page 40
  • 41. 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 2009-03-20 skhan@wku.ac.kr page 41
  • 42. 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 2009-03-20 skhan@wku.ac.kr page 42
  • 43. 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 2009-03-20 skhan@wku.ac.kr page 43
  • 44. 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. 2009-03-20 skhan@wku.ac.kr page 44
  • 45. 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 2009-03-20 skhan@wku.ac.kr page 45
  • 46. 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 2009-03-20 skhan@wku.ac.kr page 46
  • 47. 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 ??? 2009-03-20 skhan@wku.ac.kr page 47
  • 48. 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) 2009-03-20 skhan@wku.ac.kr page 48
  • 49. Repertory Grid Example 2009-03-20 skhan@wku.ac.kr page 49
  • 50. 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 2009-03-20 skhan@wku.ac.kr page 50
  • 51. 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. 2009-03-20 skhan@wku.ac.kr page 51
  • 52. 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 2009-03-20 skhan@wku.ac.kr page 52
  • 53. 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 skhan@wku.ac.kr page 53
  • 54. 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 2009-03-20 skhan@wku.ac.kr page 54
  • 55. 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 skhan@wku.ac.kr page 55
  • 56. 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 2009-03-20 skhan@wku.ac.kr page 56
  • 57. 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 2009-03-20 skhan@wku.ac.kr page 57
  • 58. 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 skhan@wku.ac.kr page 58
  • 59. 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 2009-03-20 skhan@wku.ac.kr page 59
  • 60. 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. 2009-03-20 skhan@wku.ac.kr page 60
  • 61. 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 2009-03-20 skhan@wku.ac.kr page 61
  • 62. Ontology Building Summary gy g y
  • 63. 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) 2009-03-20 skhan@wku.ac.kr page 63
  • 64. 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? ….. 2009-03-20 skhan@wku.ac.kr page 64
  • 65. Designing a KA Session Plan 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. 2009-03-20 skhan@wku.ac.kr page 65
  • 66. Designing a KA Session Plan 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 2009-03-20 skhan@wku.ac.kr page 66
  • 67. 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 2009-03-20 skhan@wku.ac.kr page 67
  • 68. Which KA technique to use 2009-03-20 skhan@wku.ac.kr page 68
  • 69. PC PACK5 http://www.epistemics.co.uk/Notes/55-0-0.htm Ladder Matrix Annotation Diagram Protocol Publisher 2009-03-20 skhan@wku.ac.kr page 69
  • 70. 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 2009-03-20 skhan@wku.ac.kr page 70
  • 71. 감사합니다…. skhan@wku.ac.kr skhan@wku ac kr
  • 72. 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 2009-03-20 skhan@wku.ac.kr page 72
  • 73. 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 2009-03-20 skhan@wku.ac.kr page 73
  • 74. 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/ 2009-03-20 skhan@wku.ac.kr page 74
  • 75. 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/ 2009-03-20 skhan@wku.ac.kr page 75

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