Welcome	  to	  Ontology	  Engineering	        Guus	  Schreiber	  
Lecture	  1	                            Agenda	  •  Course	  introduc:on:	  what	  is	  an	  ontology?	  •  Administra:on	...
Literature	  •  James	  Odell,	  Ontology	  White	  Paper,	  CSC	     Catalyst,	  2011,	  V2011-­‐07-­‐15,	  	            ...
What	  is	  an	  Ontology?	  •  In	  philosophy:	  theory	  of	  what	  exists	  in	  the	  world	  	  •  In	  IT:	  conse...
What	  is	  an	  Ontology?	  II	     “explicit	  specifica-on	  of	  a	  shared	  conceptualiza-on	  that	  holds	  in	  a	...
Knowledge	  sharing	  and	  reuse	  •  Knowledge	  engineering	  is	  costly	  and	  :me-­‐   consuming	  •  Distributed	 ...
Need	  for	  data	  integra:on?	  
Seman:c	  Web	  •  Data	  integra:on	  •  AAA	  slogan	  •  Non-­‐Unique	  Naming	  Assump:on	  •  Open	  vs.	  closed	  W...
The	  Web:	  	            resources	  and	  links	                        Web	  link	  URL	                               ...
The	  Seman:c	  Web:	  	              typed	  resources	  and	  links	  Pain:ng	                   Dublin	  Core	        U...
Seman:c	  Web	  
WordNet	  
Domain	  standards	  and	  vocabularies	  as	                  ontologies	  •  Contain	  ontological	  informa:on	  •  Ont...
Ontology	  spectrum	  Source: http://www.jamesodell.com/Ontology_White_Paper_2011-07-15.pdf.
Document	  fragment	  ontologies	                                       16	  
Instruc:onal	  document	  fragment	            ontologies	                                         17	  
Context	  and	  Domain	  Principle	  1:	  	  	  	  	  	  “ The	  representa:on	  of	  real-­‐world	  objects	  always	  de...
Mul:ple	  views	  on	  a	  domain	  •  typical	  viewpoints	  captured	  in	  ontologies:	  	         • func:on	         •...
Mul:ple	  views	  on	  a	  domain	                                              20	  
Context	  specifica:on	  through	  	               ontology	  types                 	  •  Domain-­‐specific	  ontologies	   ...
Top-­‐level	  categories:	           many	  different	  proposals	  Chandrasekaran et al. (1999)                           ...
The	  famous	  is-­‐a	  rela:onship	  Source: http://www.jamesodell.com/Ontology_White_Paper_2011-07-15.pdf.
Classes	  as	  instances	                                                                           24	  Source: http://ww...
What	  is	  an	  Ontology?	     “explicit	  specifica-on	  of	  a	  shared	  conceptualiza-on	  that	  holds	  in	  a	  par...
Concepts                                	  •  Help	  us	  organize	  the	  world	  around	  us	  •  Act	  as	  recogni:on	...
Concept	  types                                          	  Source: http://www.jamesodell.com/Ontology_White_Paper_2011-07...
The	  concept	  triad	  Source: http://www.jamesodell.com/Ontology_White_Paper_2011-07-15.pdf.
Concept	  specifica:on	  •  Symbol	     – Name	  used	  for	  the	  concept	     – Can	  be	  different	  names,	  different	...
Incomplete	  concept	  specifica:ons                                    	  •  Are	  common	  •  Think	  of	  an	  example:	...
Domain	  =	  area	  of	  interest                                               	  •  Can	  be	  any	  size	  	       – e....
Ontology	  Specifica:on	  •  Class	  (concept)	                  •    Aggrega:on	  •  Subclass	  with	  inheritance	     • ...
Ontology	  Languages	     – UML	     – RDF	  Schema,	  	  OWL	     – …..	  •  Common	  basis	     – Class	  (concept)	    ...
Ontology	  Tools	  Best	  known	  tool	  •  Protégé	  (Stanford)	  •  We	  will	  use	  this	  tool	  Decision	  points:	 ...
Administra:on	  •  Course	  website:	             hNp://seman:cweb.cs.vu.nl/OE2012/	  	  •  Use	  blog	  posts	  for	  con...
Engineering	  needs	  prac:ce!	  	   Lots	  of	  exercises	  throughout	  the	  course:	  •  Two	  mee:ngs	  per	  week	  ...
RDF(S)	  Recap	  •  Which	  RDF/RDF-­‐Schema	  constructs	  do	  you	     remember?	  
URIs,	  URLs	  •  URI:	  global	  iden:fier	  for	  a	  web	  resource	           •  hNp://www.w3.org/2006/03/wn/wn20/insta...
Triples	  ulan:Shakespeare ulan:parentOf ulan:Susanna.kb:Hamlet kb:author kb:Shakspeare.ex:VrijeUniversiteit ex:locatedIn ...
Syntax	  •  N3	  Turtle	      –  hNp://www.w3.org/TeamSubmission/turtle/	  	  •  RDFXML	      –  hNp://www.w3.org/TR/rdf-­...
Blank	  nodes	  How	  would	  you	  model	  “Sonnet78	  was	  inspired	  by	  a	  woman	  who	  lives	    in	  England”?	 ...
subClassOf	  IFA rdfs:subClassOf Br rdf:type ATHENr rdf:type B
subPropertyOf	  IFP rdfs:subPropertyOf Ra P bTHENa R b
Domain	  and	  Range	  IF                  IFP rdfs:domain D     P rdfs:range Rx P y               x P yTHEN              ...
More	  RDF(S)	  •  rdfs:label	  •  rdfs:comment	  •  rdfs:seeAlso	  
RDF-­‐Schema	  •  Provides	  a	  way	  to	  talk	  about	  the	  vocabulary	      – Define	  classes,	  proper:es	         ...
Guidelines	  for	  ontological	                   engineering	  (1)	  •  Do	  not	  develop	  from	  scratch	  •  Use	  ex...
Guidelines	  for	  ontological	                     engineering	  (2)	  •  Do	  not	  confuse	  terms	  and	  concepts	  •...
Ontology Engineering: Introduction
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Ontology Engineering: Introduction

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Introductory lecture to the VU University Amsterdam Master course on Ontology Engineering. See http://semanticweb.cs.vu.nl/OE2012/

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Ontology Engineering: Introduction

  1. 1. Welcome  to  Ontology  Engineering   Guus  Schreiber  
  2. 2. Lecture  1   Agenda  •  Course  introduc:on:  what  is  an  ontology?  •  Administra:on  •  RDF/RDFS  
  3. 3. Literature  •  James  Odell,  Ontology  White  Paper,  CSC   Catalyst,  2011,  V2011-­‐07-­‐15,     hNp://www.jamesodell.com/ Ontology_White_Paper_2011-­‐07-­‐15.pdf.    •  For  this  lecture  Sec.s  1-­‐4  are  relevant  •  Acknowledgement:  some  figures  in  this   lecture  come  from  the  paper  above.    
  4. 4. What  is  an  Ontology?  •  In  philosophy:  theory  of  what  exists  in  the  world    •  In  IT:  consensual  &  formal  descrip:on  of  shared   concepts  in  a  domain   •  Aid  to  human  communica:on  and  shared   understanding,  by  specifying  meaning   •  Machine-­‐processable  (e.g.,  agents  use  ontologies  in     communica:on)   •  Key  technology  in  seman:c  informa:on  processing   •  Applica:ons:  knowledge  management,  e-­‐business,   seman:c  world-­‐wide  web.    
  5. 5. What  is  an  Ontology?  II   “explicit  specifica-on  of  a  shared  conceptualiza-on  that  holds  in  a  par-cular   context”     (several  authors)  
  6. 6. Knowledge  sharing  and  reuse  •  Knowledge  engineering  is  costly  and  :me-­‐ consuming  •  Distributed  systems  •  Increasing  need  for  defini:on  of  a  common   frame  of  reference   – Internet  search,  document  indexing,  ….  
  7. 7. Need  for  data  integra:on?  
  8. 8. Seman:c  Web  •  Data  integra:on  •  AAA  slogan  •  Non-­‐Unique  Naming  Assump:on  •  Open  vs.  closed  World  
  9. 9. The  Web:     resources  and  links   Web  link  URL   URL  
  10. 10. The  Seman:c  Web:     typed  resources  and  links  Pain:ng   Dublin  Core   ULAN  “Woman  with  hat  SFMOMA   creator   Henri  Ma:sse   Web  link   URL   URL  
  11. 11. Seman:c  Web  
  12. 12. WordNet  
  13. 13. Domain  standards  and  vocabularies  as   ontologies  •  Contain  ontological  informa:on  •  Ontology  needs  to  be  “extracted”   –  Not  explicit  •  Lists  of  domain  terms  are  some:mes  also  called   “ontologies”   –  Implies  a  weaker  no:on  of  ontology   –  Scope  typically  much  broader  than  a  specific  applica:on   domain   –  Contain  some  meta  informa:on:  hyponyms,  synonyms,  text  •  Structured  knowledge  is  available  (on  the  web)  –  use   it!   14  
  14. 14. Ontology  spectrum  Source: http://www.jamesodell.com/Ontology_White_Paper_2011-07-15.pdf.
  15. 15. Document  fragment  ontologies   16  
  16. 16. Instruc:onal  document  fragment   ontologies   17  
  17. 17. Context  and  Domain  Principle  1:            “ The  representa:on  of  real-­‐world  objects  always  depends   on  the  context  in  which  the  object  is  used.  This  context   can  be  seen  as  a  “viewpoint”  taken  on  the  object.  It  is   usually  impossible  to  enumerate  in  advance  all  the   possible  useful  viewpoints  on  (a  class  of  )  objects.”  Principle  2:            “Reuse  of  some  piece  of  informa:on  requires  an  explicit   descrip:on  of  the  viewpoints  that  are  inherently  present   in  the  informa:on.  Otherwise,  there  is  no  way  of  knowing   whether,  and  why  this  piece  of  informa:on  is  applicable   in  a  new  applica:on  seing.”  
  18. 18. Mul:ple  views  on  a  domain  •  typical  viewpoints  captured  in  ontologies:     • func:on   • behavior,     • causality   • shape,  geometry   • structure:  part-­‐of  (mereology),  aggrega:on     • connectedness  (topology)  •  viewpoints  can  have  different  abstrac:on   (generaliza:on)  levels    •  viewpoints  can  overlap  •  applica:ons  require  combina:ons  of  viewpoints   19  
  19. 19. Mul:ple  views  on  a  domain   20  
  20. 20. Context  specifica:on  through     ontology  types  •  Domain-­‐specific  ontologies   – Medicine:  UMLS,  SNOMED,  Galen   – Art  history:  AAT,  ULAN   – STEP  applica:on  protocols  •  Task-­‐specific  ontologies   – Classifica:on   – E-­‐commerce  •  Generic  ontologies     – Top-­‐level  categories   – Units  and  dimensions   21  
  21. 21. Top-­‐level  categories:   many  different  proposals  Chandrasekaran et al. (1999) 22  
  22. 22. The  famous  is-­‐a  rela:onship  Source: http://www.jamesodell.com/Ontology_White_Paper_2011-07-15.pdf.
  23. 23. Classes  as  instances   24  Source: http://www.jamesodell.com/Ontology_White_Paper_2011-07-15.pdf.
  24. 24. What  is  an  Ontology?   “explicit  specifica-on  of  a  shared  conceptualiza-on  that  holds  in  a  par-cular   context”     (several  authors)  
  25. 25. Concepts  •  Help  us  organize  the  world  around  us  •  Act  as  recogni:on  device  •  Test  for  reality  •  We  use  many  different  types  of  concepts  
  26. 26. Concept  types  Source: http://www.jamesodell.com/Ontology_White_Paper_2011-07-15.pdf.
  27. 27. The  concept  triad  Source: http://www.jamesodell.com/Ontology_White_Paper_2011-07-15.pdf.
  28. 28. Concept  specifica:on  •  Symbol   – Name  used  for  the  concept   – Can  be  different  names,  different  languages   – E.g.,  “bike”,  fiets”  •  Intension  (defini:on)   – Intended  meaning  of  the  concept  (seman:cs)   – E.g.  a  bike  has  at  least  one  wheel  and  a  human-­‐ powered  movement  mechanism  •  Extension   – Set  of  examples  of  the  concept   – E.g.  “my  bike”,  “your  bike”  
  29. 29. Incomplete  concept  specifica:ons  •  Are  common  •  Think  of  an  example:     – Concept  with  no  instances   – Concept  with  no  symbol  •  Primi:ve  vs.  defined  concepts  
  30. 30. Domain  =  area  of  interest  •  Can  be  any  size     – e.g.,  medicine  •  Concepts  may  have  different  symbols  in   different  domains  •  The  same  symbol  may  be  used  for  different   concepts  in  different  domains  (some:mes   also  in  the  same  domain)    
  31. 31. Ontology  Specifica:on  •  Class  (concept)   •  Aggrega:on  •  Subclass  with  inheritance   •  Rela:on-­‐aNribute  dis:nc:on   •  Trea:ng  rela:ons  as  classes  •  Rela:on  (slot)   •  Sloppy  class/instance   dis:nc:on   –  Class-­‐level  aNributes/ rela:ons   –  Meta  classes   •  Constraints   •  Data  types   •  Modularity   –  Import/export  of  an   ontology   •  Ontology  mapping  
  32. 32. Ontology  Languages   – UML   – RDF  Schema,    OWL   – …..  •  Common  basis   – Class  (concept)   – Subclass  with  inheritance   – Rela:on  (slot)   33  
  33. 33. Ontology  Tools  Best  known  tool  •  Protégé  (Stanford)  •  We  will  use  this  tool  Decision  points:   – Expressivity   – Graphical  representa:on   – DB  backend   – Modulariza:on  support   – Versioning  
  34. 34. Administra:on  •  Course  website:   hNp://seman:cweb.cs.vu.nl/OE2012/    •  Use  blog  posts  for  content  ques:ons  •  Use  oe-­‐list@few.vu.nl  for  admin  ques:ons  
  35. 35. Engineering  needs  prac:ce!     Lots  of  exercises  throughout  the  course:  •  Two  mee:ngs  per  week     •  Lectures  on  Monday   •  Work  sessions  on  Thursday  •  You  are  encouraged  to  do  assignments   together  with  colleagues  •  Individual  porsolio  
  36. 36. RDF(S)  Recap  •  Which  RDF/RDF-­‐Schema  constructs  do  you   remember?  
  37. 37. URIs,  URLs  •  URI:  global  iden:fier  for  a  web  resource   •  hNp://www.w3.org/2006/03/wn/wn20/instances/synset-­‐ anniversary-­‐noun-­‐1  •  URL:  dereferencable  URI,  used  to  locate  a  file  on   the  web.   •  hNp://www.w3.org/2006/03/wn/wn20/instances/synset-­‐ anniversary-­‐noun-­‐1  •  URI  abbrevia:ons:   – Qnames   •  Namespace:iden:fier   •  Wordnet:synset-­‐anniversary-­‐noun-­‐1  
  38. 38. Triples  ulan:Shakespeare ulan:parentOf ulan:Susanna.kb:Hamlet kb:author kb:Shakspeare.ex:VrijeUniversiteit ex:locatedIn tgn:Amsterdam.ex:WillemHage ex:teaches ex:OntologyEngineering.ex:OntologyEngineering rdf:type ex:Course.
  39. 39. Syntax  •  N3  Turtle   –  hNp://www.w3.org/TeamSubmission/turtle/    •  RDFXML   –  hNp://www.w3.org/TR/rdf-­‐syntax-­‐grammar/  
  40. 40. Blank  nodes  How  would  you  model  “Sonnet78  was  inspired  by  a  woman  who  lives   in  England”?   Lit:Sonnet78 lit:hasInspiration [ rdf:type bio:Woman; bio:livedIn geo:England ] .
  41. 41. subClassOf  IFA rdfs:subClassOf Br rdf:type ATHENr rdf:type B
  42. 42. subPropertyOf  IFP rdfs:subPropertyOf Ra P bTHENa R b
  43. 43. Domain  and  Range  IF IFP rdfs:domain D P rdfs:range Rx P y x P yTHEN THENx rdf:type D y rdf:type R
  44. 44. More  RDF(S)  •  rdfs:label  •  rdfs:comment  •  rdfs:seeAlso  
  45. 45. RDF-­‐Schema  •  Provides  a  way  to  talk  about  the  vocabulary   – Define  classes,  proper:es   bb:author rdf:type rdfs:Property•  Enables  inferencing   – Inferring  new  triples  from  asserted  triples.  •  subClassOf,  subPropertyOf,  domain,  range.  
  46. 46. Guidelines  for  ontological   engineering  (1)  •  Do  not  develop  from  scratch  •  Use  exis:ng  data  models  and  domain  standards  as   star:ng  point  •  Start  with  construc:ng  an  ontology  of  common   concepts  •  If  many  data  models,  start  with  two  typical  ones  •  Make  the  purpose  and  context  of  the  ontology   explicit   –  E.g.  data  exchange  between  ship  designers  and   assessors   –  Opera:onally  purpose/context  with  use  cases  •  Use  mul:ple  hierarchies  to  express  different   viewpoints  on  classes  •  Consider  trea:ng  central  rela:onships  as  classes   47  
  47. 47. Guidelines  for  ontological   engineering  (2)  •  Do  not  confuse  terms  and  concepts  •  Small  ontologies  are  fine,  as  long  as  they  meet  their  goal  •  Don’t  be  overly  ambi:ous:  complete  unified  models  are   difficult  •  Ontologies  represent  sta:c  aspects  of  a  domain   –  Do  not  include  work  flow  •  Use  a  standard  representa:on  format,  preferably  with  a   possibility  for  graphical  representa:on  •  Decide  about  the  abstrac:on  level  of  the  ontology  early   on  in  the  process.   –  E.g.,  ontology  only  as  meta  model   48  

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