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Lecture: Ontologies and the Semantic Web


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Semantic Web, Web 3.0, shared understanding, shared semantic annotation, tree of Porphyry, ontology,wordnet, mesh,rdf, iri, description logics, DLs, Owl, WebProtege, domain-specific,Sparql, tags, ontology learning, classes, relations, axioms, instances, semantics in language technology.

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Lecture: Ontologies and the Semantic Web

  1. 1. Seman&c  Analysis  in  Language  Technology 
 Ontologies and the Semantic Web Marina  San(ni   san$     Department  of  Linguis(cs  and  Philology   Uppsala  University,  Uppsala,  Sweden     Spring  2016      
  2. 2. Acknowledgements   •  Most  slides  based  on  Harrocks  (2008).   The  Seman(c  Web  &  Ontologies   2  
  3. 3. Outline   •  The  Seman(c  Web   •  Ontologies   The  Seman(c  Web  &  Ontologies   3  
  4. 4. Chronology   hNp:// History_of_the_World_Wide_Web     •  On  August  6,  1991,Berners-­‐Lee  posted  a  short  summary  of  the  World  Wide   Web  project  on  the  alt.hypertext  newsgroup,  invi(ng  collaborators.  This  date   also  marked  the  debut  of  the  Web  as  a  publicly  available  service  on  the   Internet,  although  new  users  could  only  access  it  aEer  August  23.   •  Beginning  in  2002,  new  ideas  for  sharing  and  exchanging  content  ad  hoc,   such  as  Weblogs  and  RSS,  rapidly  gained  acceptance  on  the  Web.  This  new   model  for  informa(on  exchange,  primarily  featuring  user-­‐generated  and   user-­‐edited  websites,  was  dubbed  Web  2.0.     •  Popularized  by  Berners-­‐Lee's  book  Weaving  the  Web  (2000)  and  a  Scien(fic   American  ar(cle  by  Berners-­‐Lee,  James  Hendler,  and  Ora  Lassila,  the  term     •  Seman&c  Web  describes  an  evolu&on  of  the  exis&ng  Web  in   which  the  network  of  hyperlinked  human-­‐readable  web   pages  is  extended  by  machine-­‐readable  metadata  about   documents  and  how  they  are  related  to  each  other,   enabling  automated  agents  to  access  the  Web  more   intelligently  and  perform  tasks  on  behalf  of  users.     •  In  2006,  Berners-­‐Lee  and  colleagues  stated  that   the  idea  "remains  largely  unrealized"   The  Seman(c  Web  &  Ontologies   4  
  5. 5. Web  1.0   •  Web  1.0  is  a  retronym  referring  to  an  early  stage  of  the   World  Wide  Web's  evolu(on.   •  Some  design  elements  of  a  Web  1.0  site  include:   –  Personal  web  pages  were  common,  consis(ng  mainly  of   sta(c  pages   –  Sta(c  pages  instead  of  dynamic  HTML.   –  The  use  of  HTML  3.2-­‐era  elements  such  as  Framing  (World   Wide  Web)s  and  tables  to  posi(on  and  align  elements  on  a   page    (now  we  use  css  and  frames  are  deprecated)   –  GIF  buNons...   The  Seman(c  Web  &  Ontologies   5  
  6. 6. Web  2.0   •  Web  2.0  describes  World  Wide  Web  sites  that  use  technology   beyond  the  sta(c  pages  of  earlier  Web  sites.     •  The  key  features  of  Web  2.0  include:   –  Tagging  -­‐  allows  users  to  collec(vely  classify  and  find  informa(on  (e.g.   Tagging)   –  Rich  User  Experience-­‐  dynamic  content;  responsive  to  user  input   –  User  Par(cipa(on  -­‐  informa(on  flows  two  ways  between  site  owner  and   site  user  by  means  of  evalua(on,  review,  and  commen(ng.     –  Site  users  add  content  for  others  to  see   –  Mass  Par(cipa(on  -­‐  Universal  web  access  leads  to  differen(a(on  of   concerns  from  the  tradi(onal  internet  userbase.   –  etc.   The  Seman(c  Web  &  Ontologies   6  
  7. 7. Web  3.0   •  “Web  3.0,  a  phrase  coined  by  John  Markoff  of  the  New  York  Times  in  2006,   refers  to  a  supposed  third  genera(on  of  Internet-­‐based  services  that   collec(vely  comprise  what  might  be  called  ‘the  intelligent  Web’  —  such  as   those  using  seman(c  web,  microformats,  natural  language  search,  data-­‐ mining,  machine  learning,  recommenda(on  agents,  and  ar(ficial   intelligence  technologies  —  which  emphasize  machine-­‐facilitated   understanding  of  informa(on  in  order  to  provide  a  more  produc(ve  and   intui(ve  user  experience.”   •  Web  3.0  will  be  more  connected,  open,  and  intelligent,  with  seman(c  Web   technologies,  distributed  databases,  natural  language  processing,  machine   learning,  machine  reasoning,  and  autonomous  agents.   –  hNp://     The  Seman(c  Web  &  Ontologies   7   This  has  yet  to  happen.      
  8. 8. •  "The  Web  was  designed  as  an  informa$on  space,  with  the   goal  that  it  should  be  useful  not  only  for  human-­‐human   communica(on,  but  also  that  machines  would  be  able  to   par(cipate  and  help.     •  One  of  the  major  obstacles  to  this  has  been  the  fact  that   most  informa$on  on  the  Web  is  designed  for  human   consump$on,  and  even  if  it  was  derived  from  a  database   with  well  defined  meanings  (in  at  least  some  terms)  for  its   columns,  that  the  structure  of  the  data  is  not  evident  to  a   robot  browsing  the  Web.     •  Leaving  aside  the  ar(ficial  intelligence  problem  of  training   machines  to  behave  like  people,  the  Seman$c  Web   approach  instead  develops  languages  for  expressing   informa$on  in  a  machine  process-­‐able  form"-­‐   –  Tim  Berners-­‐Lee,  The  Seman&c  Web  Roadmap,  1998   –  hNp://     The  Seman(c  Web  &  Ontologies   8   The  web:  present  and  future  
  9. 9. Today…   •  The  web  is  rela(vely  simple:   – Hypertexts  and  hypermedia   – Access  is  engineered  via  a  combina(on  of   keyword-­‐based  search  and  link  nagiva(on.   This  simplicity  has  been  one  of  the  great   strengths  of  the  web,  and  has  been  an   important  factor  in  its  popularity  and  their  own   content.     The  Seman(c  Web  &  Ontologies   9  
  10. 10. Shortcomings   Examples:   •  Finding  informa(on  about  people  with  very   common  names  can  be  a  frustra(ng  experience.       •  Answering  more  complex  queries  along  with   more  general  informa(on  retrieval,  integra(on,   sharing  and  processing  can  be  difficult  ….  We   have  seen  that…   The  Seman(c  Web  &  Ontologies   10  
  11. 11. Some  solu(ons     •  Sosware  glue:  Mashups   –  loca(on  informa(on  from  one  source  might  be  combined  with   map  informa(on  from  another  source  in  order  to  show  the   loca(on  of  and  provide  direc(ons  to  points  of  interest  such  as   hotels  and  restaurants.   •  Tagging  via  social  networks  (Web  2.0)   –  harness  the  power  of  user  communi(es  in  order  to  share  and   annotate  informa(on.   •  Examples  include  image  and  video  shar-­‐ing  sites  such  as  Flickr  and   YouTube,  and  auc(on  sites  such  as  eBay.     –  In  these  applica(ons,  annota(ons  usually  take  the  form  simple  tags,  such  as   ”each",  ”birthday",  ”family"  and  ”friends".  The  meaning  of  tags  is,  however,   typically  not  well  defined,  and  may  be  impenetrable  even  to  human  users:   typ-­‐ical  examples  (from  Flickr)  include  "asquatchmusicfes(val",   "elebritylookalikes",  and  "wab08".   The  Seman(c  Web  &  Ontologies   11  
  12. 12. The  ”travel  agent”   •  The  classic  example  of  a  seman(c  web  applica(on  is  an   automated  travel  agent  that,  given  various  constraints   and  preferences,  would  offer  the  user  suitable  travel  or   vaca(on  sugges(ons.     •  A  key  feature  of  such  a  "sosware  agent"  is  that  it   would  not  simply  exploit  a  predetermined  set  of   informa(on  sources,  but  would  search  the  web  for   relevant  informa(on  in  much  the  same  way  that  a   human  user  might  do  when  planning  a  vaca(on.   The  Seman(c  Web  &  Ontologies   12  
  13. 13. The  goal   •  The  goal  of  the  Seman(c  Web  is  to  allow  web   informa(on  and  services  to  be  more   effec(vely  exploited  by  humans  and   automated  tools.         The  Seman(c  Web  &  Ontologies   13  
  14. 14. Seman(c  Web   •  The  focus  of  the  seman(c  web  is  to  share  data   instead  of  documents.     •  In  other  words,  it  is  a  project  that  should  provide   a  common  framework  that  allows  data  to  be   shared  and  reused  across  applica(on,  enterprise,   and  community  boundaries.     •  It  is  a  collabora(ve  effort  led  by  World  Wide  Web   Consor(um  (W3C).   The  Seman(c  Web  &  Ontologies   14  
  15. 15. Semantic Web & Ontologies •  How  are  we  going  to  represent  meaning  and  knowledge  on  the  web?   •  A  key  idea  behind  the  seman&c  web  is  to  address  this  problem  by  giving   machine-­‐accessible  seman&cs  via  annota&on.     •  Knowledge  is  represented  in  the  form  of  rich  conceptual  schemas  called   ontologies.     •  Ontologies  are  the  backbone  of  the  Seman(c  Web.   •  Ontologies  are  rich  conceptual  schemas  that  give  formally  defined   meanings  to  the  terms  used  in  annota&ons,  transforming  them  into   seman&c  annota&ons.   •  They  provide  the  knowledge  that  is  required  for  seman(c  applica(ons  of  all   kinds.     15The  Seman(c  Web  &  Ontologies  
  16. 16. Main  Difficulty   •  Current  web  content  is  intended  for  humans   (HTML  markup  with  layout,  images  and  other   presenta(onal  features).     •  Humans  understand  this  content,  but   machines  can’t.   The  Seman(c  Web  &  Ontologies   16  
  17. 17. Basically... •  Ontologies provide a shared understanding of a domain. •  They provide background knowledge to automatize certain tasks. •  By the process of annotation, knowledge can be linked to ontologies. –  Example: “Angelina Jolie” (Text) linked to concept Actress –  In our ontology we also know that an actress always is female and a person. •  Ontologies allow the creation of annotations à machine-readable and machine-understandable content. •  If machines can understand content, they can also perform more meaningful and intelligent queries. –  Distinction of Jaguar the animal and the car. –  Combination of information that is distributed on the Web. 17The  Seman(c  Web  &  Ontologies  
  18. 18. Old  and  New  Issues   Old  ones:   •  knowledge  representa(on     •  Reasoning   •  Harnessing  the  idiosyncracies  of  natural  languages   •  …   New  ones:   •  integra(ng  different  ontologies  may  prove  to  be  at  least  as   hard  as  integra(ng  the  resources  that  they  describe     •  Crea(on  of  suitable  annota(ons   •  …   The  Seman(c  Web  &  Ontologies   18  
  19. 19. Regardless  these  issues…   •  …  considerable  progress  has  been  made  in  the   development  of  the  infrastructure  needed  to   support  the  seman(c  web.     •  In  par(cular,  there  has  been  impressive   progress  in  the  development  of  languages  and   tools  for  content  annota(on  and  for  the   design  and  deployment  of  ontologies.   The  Seman(c  Web  &  Ontologies   19  
  20. 20. Seman(c  Annota(on   •  To  facilitate  the  process  of  seman(c   annota(on,  RDF  and  OWL  have  been   developed  as  standard  formats  fo  the  sharing   and  integra(on  of  data  and  knowledge.   •  RDF  and  OWL  are  standards:   – RDF  (Resource  Descrip(on  Framework)   – OWL  (Web  Ontology  Language)   The  Seman(c  Web  &  Ontologies   20  
  21. 21. Ontologies   (Metaphysics)   •  Ontology,  in  its  original  philosophical   sense,  is  a  fundamental  branch  of   metaphysics  focusing  on  the  study  of   existence.   •  Its  objec(ve  is  to  determine  what   en((es  and  types  of  en((es  actually   exist,  and  thus  to  study  the  structure   of  the  world.     •  The  study  of  ontology  can  be  traced   back  to  the  work  of  Plato  and   Aristotle,  and  includes  the   development  of  hierarchical   categorisa(ons  of  different  kinds  of   en((es  and  the  features  that   dis(nguish  them   The  Seman(c  Web  &  Ontologies   21   Tree  of  Porphyry  
  22. 22. Tree  of  Porphyry,    III  AD   •  The  Porphyrian  tree,  Tree  of  Porphyry  or   Arbor  Porphyriana  is  a  classic  device  for   illustra(ng  what  is  also  called  a  "scale  of   being".  It  was  suggested  by  the  3rd  century   AD  Greek  neoplatonist  philosopher  and   logician  Porphyry     The  Seman(c  Web  &  Ontologies   22  
  23. 23. Ontology  (Computer  Science,  AI,  LT,  IR…)   •  Engineering  artefact,  usually  a  model  of  some   aspect  of  the  world.   •  It  introduces  vocabulary  describing  various   aspects  of  the  domain  being  modelled,  and   provides  an  explicit  specifica(on  of  the  intended   meaning  of  the  vocabulary.     •  This  specifica(on  osen  includes  classifica(on-­‐ based  informa(on,  not  unlike  that  in  Porphyry's   tree.     The  Seman(c  Web  &  Ontologies   23  
  24. 24. What is an ontology (i)? 24 “An  ontology  is  a  formal,  explicit  specifica&on  of  a    shared  conceptualiza&on”   Studer,  Benjamins,  Fensel.  Knowledge  Engineering:  Principles  and  Methods.  Data  and  Knowledge  Engineering.  25  (1998)  161-­‐197     An ontology is an explicit specification of a conceptualization Gruber, T. A translation Approach to portable ontology specifications. Knowledge Acquisition. Vol. 5. 1993. 199-220 Abstract model and simplified view of some phenomenon in the world that we want to represent Machine-readable Concepts, properties relations, functions, constraints, axioms, are explicitly defined Consensual Knowledge The  Seman(c  Web  &  Ontologies  
  25. 25. What is an ontology (ii)? •  An ontology is a hierarchically structured set of terms for describing a domain that can be used as a skeletal foundation for a knowledge base B. Swartout; R. Patil; k. Knight; T. Russ. Toward Distributed Use of Large-Scale Ontologies Ontological Engineering. AAAI-97 Spring Symposium Series. 1997. 138-148 •  An ontology defines the basic terms and relations comprising the vocabulary of a topic area, as well as the rules for combining terms and relations to define extensions to the vocabulary Neches, R.; Fikes, R.; Finin, T.; Gruber, T.; Patil, R.; Senator, T.; Swartout, W.R. Enabling Technology for Knowledge Sharing. AI Magazine. Winter 1991. 36-56 •  An ontology provides the means for describing explicitly the conceptualization behind the knowledge represented in a knowledge base A. Bernaras;I. Laresgoiti; J. Correra. Building and Reusing Ontologies for Electrical Network Applications ECAI96. 12th European conference on Artificial Intelligence. Ed. John Wiley & Sons, Ltd. 298-302 25The  Seman(c  Web  &  Ontologies  
  26. 26. Examples   •  Top  level  ontology:  Standard  Upper  Ontology   –  In  informa(on  science,  an  upper  ontology  (also  known  as  a  top-­‐ level  ontology  or  founda(on  ontology)  is  an  ontology  (in  the   sense  used  in  informa(on  science)  which  describes  very  general   concepts  that  are  the  same  across  all  knowledge  domains.   •  Linguis(c  ontology:  WordNet   •  General  Ontology:  Cyc,  UNSPSC,  ecl@ss   •  Domain  ontology:  MeSH  (Medical  Subject  Headings),   CHEMICALS,  UMLS   •  Research  ontology:  KA2  (Knowledge  Acquisi(on   Community  Ontology)   The  Seman(c  Web  &  Ontologies   26  
  27. 27. Resource  Descrip(on  Framework  (i)   •  A  language  that  has  been  developed  in  order  to   provide  a  extensible  mechanism  for  describing  web   resources  and  rela(onships  between  them.     •  A  key  feature  of  RDF  is  the  use  of  Interna(onalized   Resource  Iden(fiers  (IRIs)  (which  is  a  generalisa(on  of   Uniform  Resource  Locators  (URLs)  to  refer  to   resources.     •  RDF  is  a  very  simple  language:  its  underlying  data   structure  is  a  labelled  directed  graph,  and  its  only   syntac(c  construct  is  the  triple.     •  A  triple  consists  of  three  components,  referred  to  as   the  subject,  predicate  and  object.   The  Seman(c  Web  &  Ontologies   27   a  directed  graph   is  a  set  of  nodes   connected  by   edges,  where   the  edges  have  a   direc(on   associated  with   them.   /ˈaɪˌɑːˌraɪ/  
  28. 28. RDF  (ii)   •  More  formally,    a  triple  represents  a  single  edge  (labelled   with  the  predicate)  connec(ng  two  nodes  (labelled  with   the  subject  and  object);  it  describes  a  binary  rela(onship   between  the  subject  and  object  via  the  predicate.     •  The  predicate  of  a  triple  is  always  an  IRI,  and  an  IRI  that  is   used  in  the  predicate  posi(on  of  a  triple  is  called  a   property.     •  A  set  of  triples  is  called  an  RDF  graph.     •  In  order  to  facilitate  the  sharing  and  exchanging  of  graphs   on  the  web,  an  XML  serialisa(on  has  also  been  defined.   The  Seman(c  Web  &  Ontologies   28  
  29. 29. ”Harry  PoNer  has  a  pet  called  Hedwig…”   The  Seman(c  Web  &  Ontologies   29   RDF/XML   RDF  graph  
  30. 30. Lect  09:  Rela(on  Extrac(on:   DBPediaRela(on  database  that  draw  from  Wikipedia   •  Resource  Descrip&on  Framework  (RDF)  triples   subject  predicate  object   Golden Gate Park location San Francisco! dbpedia:Golden_Gate_Park      dbpedia-­‐owl:loca(on       dbpedia:San_Francisco   ! •  DBPedia:  The  DBpedia  project  uses  the  Resource   Descrip(on  Framework  (RDF)  to  represent  the  extracted   informa(on  and  consists  of  3  billion  RDF  triples,  580  million   extracted  from  the  English  edi(on  of  Wikipedia  and  2.46   billion  from  other  language  edi(ons  (wikipedia,  March   2016).   30  The  Seman(c  Web  &  Ontologies  
  31. 31. …  but  …  not  enough…   •  Capabili(es  of  RDF  as  ontology  language  are   limited   – No  cardinality     – No  possible  to  describe  conjunc(on  of  classes   – …   RDF  is  a  very  simple  language     The  Seman(c  Web  &  Ontologies   31   cardinality  of  a  set  is  a  measure   of  the  "number  of  elements  of   the  set”.  For  example,  the  set  A   =  {2,  4,  6}  contains  3  elements,   and  therefore  A  has  a   cardinality  of  3  
  32. 32. Need  for  a  more  expressive  ontology  language:   OWL  (Web  Ontology  Language)   •  Since  the  architecture  of  the  web  depends  on  agreed   standards,  the  World  Wide  Web  Consor(um  (W3C)  set   up  a  standardisa(on  working  group  to  develop  a   standard  for  a  web  ontology  language.   •   The  result  of  this  ac(vity  was  the  OWL  ontology   language  standard.   •  The  integra(on  of  OWL  with  RDF  has  the  advantage  of   making  OWL  ontologies  directly  accessible  to  web   based  applica(ons.   The  Seman(c  Web  &  Ontologies   32  
  33. 33. Back  Story:     hNp://­‐web-­‐ ontology-­‐language-­‐is-­‐abbreviated-­‐as-­‐owl-­‐and-­‐not-­‐wol/     The  Seman(c  Web  &  Ontologies   33  
  34. 34. Descrip(on  Logics  (DLs)   •  A  key  feature  of  OWL  is  its  basis  in  Descrip(on   Logics,  a  family  of  logic-­‐based  knowledge   representa(on  formalisms  that  have  a  formal   seman(cs  based  on  first-­‐order  logic  (FOL).   The  Seman(c  Web  &  Ontologies   34  
  35. 35. Descrip(on  Logics   •  We  can  use  DLs  to  model  an  applica(on  domain.  The  focus  is   then  on:   –  Representa(on  of  knowledge  about  categories   –  The  set  of  categories  in  an  applica(on  domain  is  called   terminology   –  The  terminology  is  arranged  in  a  hierachical  organiza&on  called   ontology,  which  capture  superset  &  subset  rela(ons  among   categoires/concepts.     –  In  order  to  specify  a  hierachical  structure,  we  can  use   subsump$on  rela(ons  betw  the  appropriate  concepts  in  a   terminiology     –  Subsump$on  is  a  form  of  inference.  Determines  whether  a   superset/subset  rela(on  (based  on  the  fact  asserted  in  a   terminology)  exists  betw  two  concepts.   The  Seman(c  Web  &  Ontologies   35  
  36. 36. In  short,  DLs  are…   •  …  formalisms  based  on  an  object-­‐oriented   modelling,  in  which  the  domain  is  described  in   terms  of  individuals  (instances),  concepts   (classes),  and  roles  (proper(es/predicates):   –  individuals,  e.g.,  "Hedwig",  are  the  basic  elements  of   the  domain;     –  concepts,  e.g.,  "Owl",  describe  sets  of  individuals   having  similar  characteris(cs;     –  roles,  e.g.,  "hasPet",  describe  rela(onships  between   pairs  of  individuals,  such  as  "HarryPoNer  hasPet   Hedwig".   The  Seman(c  Web  &  Ontologies   36  
  37. 37. Axioms   •  An  OWL  ontology  consists  of  a  set  of  axioms   •  Exemple:     –  given  the  axiom  C  equivalentClass  D,  then  an  individual  is  an  instance  of  C  if  and   only  if  it  is  an  instance  of  D.     –  i.e.  Combining  axioms  with  class  descrip(ons  allows  for  easy  extension  of  the   vocabulary  by  introducing  new  names  as  abbrevia(ons  for  descrip(ons.     See  the  following  axiom:     Class: HogwartsStudent! !EquivalentTo: Student and attendsSchoolvalue Hogwarts!   introduces  the  class  name  HogwartsStudent,  and  asserts  that  its  instances  are   just  those  Students  who  aNend  Hogwarts.   The  Seman(c  Web  &  Ontologies   37  
  38. 38. TBox  &  ABox   •  Axioms  describe  constraints  on  the  structure  of  the   domain:   –  in  DLs  such  a  set  of  axioms  is  called  a  TBox  (Terminology   Box).     •  OWL  also  allows  for  axioms  asser&ng  facts  about  some   concrete  situa(on,  similar  to  data  in  a  database  se€ng:   –  in  DLs  such  a  set  of  axioms  is  called  an  ABox  (Asser(on  Box).   The  Seman(c  Web  &  Ontologies   38  
  39. 39. Decid-­‐ability  (i)   •  Descrip(on  Logics  are  fully-­‐fledged  logics  and   so  have  a  formal  seman(cs.   •   DLs  can  be  seen  as  decidable  subsets  of  FOL   with:   –   individuals  being  equivalent  to  constants,     – concepts  to  unary  predicates,   – roles  to  binary  predicates.     The  Seman(c  Web  &  Ontologies   39  
  40. 40. FOL  …  undecidable  (some(mes)   •  The  Incompleteness  Theorem  ,  proven  in   1930,  demonstrates  that  first-­‐order  logic  is  in   general  undecidable.     •  That  means  there  exist  statements  in  this  logic   form  that,  under  certain  condi(ons,  cannot  be   proven  either  true  or  false.   •  Ex:  can’t  solve  the  Hal$ng  Problem   The  Seman(c  Web  &  Ontologies   40  
  41. 41. Hal(ng  Problem   •  In  1936  Alan  Turing  proved  that  it's  not  possible  to  decide  whether   an  arbitrary  program  will  eventually  halt,  or  run  forever.     •  The  official  defini&on  of  the  problem  is  to  write  a  program  (actually,   a  Turing  Machine*)  that  accepts  as  parameters  a  program  and  its   parameters.  That  program  needs  to  decide,  in  finite  &me,  whether   that  program  will  ever  halt  running  these  parameters.   •  The  hal(ng  problem  is  a  cornerstone  problem  in  computer  science.   It  is  used  mainly  as  a  way  to  prove  a  given  task  is  impossible,  by   showing  that  solving  that  task  will  allow  one  to  solve  the  hal(ng   problem.   *A  Turing  machine  is  a  hypothe(cal  device  that  manipulates  symbols   according  to  a  table  of  rules.  Despite  its  simplicity,  a  Turing  machine   can  be  adapted  to  simulate  the  logic  of  any  computer  algorithm,     The  Seman(c  Web  &  Ontologies   41  
  42. 42. Decid-­‐ability  (ii)   •  DLs  give  a  precise  and  unambiguous  meaning   to  descrip(ons  of  the  domain   •  This  also  allows  for  the  development  of   reasoning  algorithms  that  can  provide  correct   answers  to  arbitrarily  complex  queries  about   the  domain.   The  Seman(c  Web  &  Ontologies   42  
  43. 43. Reasoning:   OWL  vs  Databases   OWL  axioms  behave  like  inference  rules  rather  than  database  constraints.     ! Class: Phoenix! !SubClassOf: isPetOf only Wizard! ! Individual: Fawkes! Types: Phoenix! Facts: isPetOf Dumbledore! •  Fawkes  is  said  to  be  a  Phoenix  and  to  be  the  pet  of  Dumbledore,  and  it  is  also  stated  that  only  a   Wizard  can  have  a  pet  Phoenix.     •  In  OWL,  this  leads  to  the  implica(on  that  Dumbledore  is  a  Wizard.  That  is,  if  we  were  to  query  the   ontology  for  instances  of  Wizard,  then  Dumbledore  would  be  part  of  the  answer.     •  In  a  database  se€ng  the  schema  could  include  a  similar  statement  about  the  Phoenix  class,  but  in   this  case  it  would  be  interpreted  as  a  constraint  on  the  data:  adding  the  fact  that  Fawkes  isPetOf   Dumbledore  without  Dumbledore  being  already  known  to  be  a  Wizard  would  lead  to  an  invalid   database  state,  and  such  an  update  would  therefore  be  rejected  by  a  database  management   system  as  a  constraint  viola(on.   The  Seman(c  Web  &  Ontologies   43  
  44. 44. Ontology  Development  Tools   •  State  of  the  art  ontology  development  tools,  such   as  SWOOP,  Protégé,  and  TopBraid  Composer,  use   DL  reasoners  to  provide  feedback  to  the  user  about   the  logical  implica(ons  of  their  design:     – i.e.  warnings  about  inconsistencies  and  synonyms.   The  Seman(c  Web  &  Ontologies   44  
  45. 45. WebProtégé   hNp://­‐protege       The  Seman(c  Web  &  Ontologies   45  
  46. 46. VOWL:     Visual  Nota(on  for  OWL   Ontologies   hNp://       The  Seman(c  Web  &  Ontologies   46  
  47. 47. Domain-­‐specific  ontologies   •  The  availability  of  tools  has  contributed  to  the   increasingly  widespread  use  of  OWL,  and  it  has   become  the  de  facto  standard  for  ontology   development  in  fields  as  diverse  as       –  Biology   –  Medicine   –  Geography   –  Geology   –  Agriculture     –  Defence   –  etc   The  Seman(c  Web  &  Ontologies   47  
  48. 48. Complex  Queries   •  The  use  of  DL  reasoners  allows  OWL  ontology   applica(ons  to  answer  complex  queries  and  to   provide  guarantees  about  the  correctness  of  the   result.   •  Reliability  and  correctness  are  clearly  important   features  of  any  informa(on  system;     •  They  are  par(cularly  important  if  ontology  based   systems  are  to  be  used  in  safety-­‐cri(cal   applica(ons  such  as  medicine,  where  incorrect   reasoning  could  adversely  impact  pa(ent  care.   The  Seman(c  Web  &  Ontologies   48  
  49. 49. Standard  Query  Language   •  It  has  long  been  recognised  that  the  seman(c   web,  and  seman(c  web  knowledge   representa(on  languages  such  as  RDF  and   OWL,  would  also  benefit    from  the  availability   of  a  standardised  query  language  such  as  SQL   •  A  W3C  standardisa(on  working  group  was  set   up,  and  has  completed  its  work  on  the   SPARQL  query  language  standard.   The  Seman(c  Web  &  Ontologies   49  
  50. 50. SPARQL  Protocol  and  RDF  Query  Language   …   •  …  is  an  RDF  query  language,  ie  a  query   language  that  can  retrieve  and  manipulate   data  stored  in  RDF  format  (ie  triples).     •  SPARQL  allows  for  a  query  to  consist  of  triple   paSerns,  conjunc(ons,  disjunc(ons,  and   op(onal  paNerns   The  Seman(c  Web  &  Ontologies   50  
  51. 51. Tags  &  Ontologies   •  Tagging  facili(es  within  Web  2.0  applica(ons   have  shown  how  it  might  be  possible  for  user   communi(es  to  collabora(vely  annotate  web   content,  and  create  simple  forms  of  ontology   via  the  development  of  hierarchically   organised  sets  of  tags,  osen  called   folksonomies….     The  Seman(c  Web  &  Ontologies   51  
  52. 52. Challenges   •  Currently  hard  to  combine:     – Increased  expressive  power  (by  using  more   sophis(cated  logics)  with  scalability  (large  ontologies)   The  Seman(c  Web  &  Ontologies   52  
  53. 53. Ontology  Learning   •  Ontology  learning  (ontology  extrac(on,  ontology  genera(on,  or  ontology   acquisi(on)  is  the  automa(c  or  semi-­‐automa(c  crea(on  of  ontologies,   including  extrac(ng  the  corresponding  domain's  terms  and  the   rela&onships  between  those  concepts  from  a  corpus  of  natural  language   text,  and  encoding  them  with  an  ontology  language  for  easy  retrieval.     •  As  building  ontologies  manually  is  extremely  labor-­‐intensive  and  (me   consuming,  there  is  great  mo(va(on  to  automate  the  process.   •  Typically,  the  process  starts  by  extrac(ng  terms  and  concepts  or  noun   phrases  from  plain  text  using  linguis(c  processors  such  as  part-­‐of-­‐speech   tagging  and  phrase  chunking.  Then  sta(s(cal  techniques  are  used  to   extract  rela(on,  osen  based  on  Machine  Learning.   –  hNp://     The  Seman(c  Web  &  Ontologies   53  
  54. 54. In  summary…   Why  to  build  an  ontology?      •  To  share  common  understanding  of  the   structure  of  informa(on  among  people  or   sosware  agents   •  To  enable  reuse  of  domain  knowledge   •  To  make  domain  assump(ons  explicit   •  To  analyze  domain  knowledge   The  Seman(c  Web  &  Ontologies   54  
  55. 55. How  to  build  an  ontology   Generally  speaking  (and  roughtly  said),  when   designing  an  ontology,  four  main  components   are  used:   1.  Classes   2.  Rela(ons   3.  Axioms   4.  Instances       The  Seman(c  Web  &  Ontologies   55  
  56. 56. Classes   •  concepts  of  the  domain  or  tasks,  which  are   usually  organized  in  taxonomies   Ex:  in  a  university  ontology,  student  and   professor  are  two  classes   The  Seman(c  Web  &  Ontologies   56  
  57. 57. Rela(ons   A  type  of  interac(on  between  concepts  of  the   domain:   Ex:  subclass-­‐of  or  is-­‐a    are  rela(ons   The  Seman(c  Web  &  Ontologies   57  
  58. 58. Axioms   Asser(ons  that  are  always  true  for  the  domain   of  interest     Ex:  if  a  student  aNends  both  ”Math”  and  ”Basic   text  processing”  courses,    then  he  or  she  must   be  a  1st  year  student.   The  Seman(c  Web  &  Ontologies   58  
  59. 59. Instances   Represent  specific  elements   Ex:  a  Student  called  Peter  is  the  instance  of   Student  class   The  Seman(c  Web  &  Ontologies   59  
  60. 60. Important!     •   There  is  no  single  correct  class  hierarchy  for   any  given  domain.   •  The  hierarchy  depends  on  the  possible  uses  of   the  ontology.   •  The  level  of  detail  is  depend  on  the  applica(ons   and  purposes.     The  Seman(c  Web  &  Ontologies   60  
  61. 61. The  end   61  The  Seman(c  Web  &  Ontologies