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Fusing semantic data

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Andriy Nikolov
Andriy Nikolov
EducaçãoTecnologia
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Fusing automatically extracted annotations for the Semantic Web Andriy Nikolov Knowledge Media Institute The Open University
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Database scenario ,[object Object],[object Object],[email_address] [email_address] E-mail … 1983 J. Smith … 1972 H. Schmidt Address Year of birth Name … [email_address] 1980 Wen, Zhao [email_address] E-Mail Job position Year of birth Name … 1973 Schmidt, Hans
Database scenario ,[object Object],[object Object],[email_address] [email_address] E-mail … 1983 J. Smith … 1972 H. Schmidt Address Year of birth Name … [email_address] 1980 Wen, Zhao [email_address] E-Mail Job position Year of birth Name … 1973 Schmidt, Hans
Database scenario ,[object Object],[object Object],[email_address] [email_address] E-mail … 1983 J. Smith … 1972 H. Schmidt Address Year of birth Name … [email_address] 1980 Wen, Zhao [email_address] E-Mail Job position Year of birth Name … 1973 Schmidt, Hans
Semantic data scenario ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],foaf:Person sweto:Person foaf:name xsd:string xsd:string foaf:mbox sweto:Place sweto:lives_in sweto:Organization sweto:affiliated_with sweto:Researcher xsd:string some:has_degree
Motivating scenario – X-Media RDF Images Other data Annotation Fusion Text Internal corporate reports (Intranet) Pre-defined public sources (WWW) Domain ontology KnoFuss Knowledge base
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Handling fusion subtasks ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Handling fusion subtasks ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Problem-solving method approach ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
KnoFuss architecture ,[object Object],[object Object],[object Object],[object Object],[object Object],Fusion KB Intermediate data Main KB KnoFuss CoreferenceResolutionMethod ConflictDetectionMethod ConflictResolutionMethod Method library New data Fusion ontology
Task decomposition Source KB Target KB (fused) Target KB Knowledge fusion Coreference resolution Knowledge base updating Model configuration Dependency identification Dependency resolution Link discovery
Method selection Adaptive learning matcher Application context: Publication Application context: Journal Article rdf:type owl:Thing datatypeProperty ?x reliability =0.4 rdf:type sweto:Publication rdfs:label ?x sweto:year ?y reliability =0.8 rdf:type sweto:Article rdfs:label ?x sweto:year ?y sweto:journal ?z sweto:volume ?a reliability =0.9 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Using class hierarchy ,[object Object],[object Object],owl:Thing foaf:Person foaf:Document sweto:Publication sweto:Article sweto:Article_in_Proceedings year name volume book_title label journal_name Ind1: {label, year, book_title} Ind2: {label, year, book_title} Ind3: {label, year, book_title} Ind1: {label, year} Ind2: {label, year} Ind3: {label, year} Ind1: {label} Ind2: {label} Ind3: {label}
Using class hierarchy ,[object Object],[object Object],sweto:Publication sweto:Article sweto:Article_in_Proceedings year volume book_title label journal_name Ind1: {label, year, book_title} Ind2: {label, year, book_title} Ind3: {label, year, book_title} Ind1: {label, year} Ind2: {label, year} Ind3: {label, year} Ind4: {label, year} Ind5: {label, year} Ind6: {label, year} Ind4: {label, year, journal_name, volume} Ind5: {label, year, journal_name, volume} Ind6: {label, year, journal_name, volume}
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data quality problems ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Refining fused data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Dempster-Shafer theory of evidence ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],probability support plausibility
Dependency detection ,[object Object],[object Object],Article Proceedings Paper_10 owl:disjointWith owl:FunctionalProperty rdf:type rdf:type hasYear 2007 hasYear 2006 E. Motta V.S. Uren hasAuthor hasAuthor
Belief networks (cont) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Belief networks (cont) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Example #Paper_10 Article Proceedings owl:disjointWith rdf:type rdf:type
Example #Paper_10 2 Article #Paper_10 2 Proceedings #Paper_10 Article Proceedings owl:disjointWith rdf:type rdf:type
Example #Paper_10 2 Article #Paper_10 2 Proceedings Article v : Proceedings #Paper_10 Article Proceedings owl:disjointWith rdf:type rdf:type
Example #Paper_10 2 Article #Paper_10 2 Proceedings Article v : Proceedings m(true)=0.8 m(false) = 0 m({true;false})=0.2 m(true)=0.6 m(false) = 0 m({true;false})=0.4 m( )=1.0 m( )=0.0 true true false true true false false false #Paper_10 2 Proceedings #Paper_10 2 Article
Example #Paper_10 2 Article #Paper_10 2 Proceedings Article v : Proceedings m(true)=0.8 m(false) = 0 m({true;false})=0.2 m(true)=0.6 m(false) = 0 m({true;false})=0.4 m( )=0.15 ­ -Dempster’s rule m( m( )=0.23 )=0.62 true false false true false true true false false false #Paper_10 2 Proceedings #Paper_10 2 Article
Example #Paper_10 2 Article #Paper_10 2 Proceedings Article v : Proceedings m(true)= 0.62 m(false) = 0.23 m({true;false})= 0.15 m(true)= 0.23 m(false) = 0.62 m({true;false})= 0.15 m( )=0.15 m( m( )=0.23 )=0.62 true false false true false true true false false false #Paper_10 2 Proceedings #Paper_10 2 Article
Belief propagation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Article v: in_Proc Ind1=Ind2 Functional(year) Article(Ind1) (0.99;1.0)/(0.97;0.98) in_Proc(Ind2) Ind1=Ind2 inProc(Ind1) Ind1=Ind2 year(Ind1, 2007) year(Ind2, 2007) year(Ind1, 2006) (0.9;1.0)/(0.74;0.82) (0.92;1.0)/(0.2;0.21) (0.85;1.0)/(0.72;0.85) (0.95;1.0)/(0.91;0.96)
Neighbourhood graph ,[object Object],Paper_10 H. Schmidt hasAuthor Paper_11 Schmidt, Hans owl:sameAs (0.9) owl:sameAs (0.3) hasAuthor Proceedings rdf:type rdf:type Person Germany H. Schmidt citizen_of Germany Schmidt, Hans owl:sameAs (1.0) owl:sameAs (0.3) citizen_of Country rdf:type rdf:type Person
Neighborhood graph ,[object Object],Person11 = Person12 Person21 = Person22 Coauthor(Person12, Person22) Person11 = Person12 Coauthor(Person11, Person22) Person21 = Person22 Coauthor(Person21, Person22) “ Bard, J.B.L.”=“Jonathan Bard” “ Webber, B.L.”=“Bonnie L. Webber” 0.84/(0.86;1.0) 0.16/(0.83;1.0) 1.0/(1.0;1.0) 1.0/(1.0;1.0)
Provenance ,[object Object],[object Object],[object Object],[object Object],Arl_Va  Arl_Tx Arlington = Arl_Tx Arlington  Arl_Tx Arlington = Arl_Va Arlington = Arl_Va Arl_Va  Arl_Tx 1.0/(1.0;1.0) 0.9/(0.31;0.35) Arlington, Virginia 0.95/(0.65;0.69) Arlington, Texas
Experiments ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Experiments ,[object Object],[object Object],0.884 0.836 0.939 0.821 0.931 0.735 Monge-Elkan 7 Cora (I) 0.895 0.981 0.823 0.702 0.982 0.546 L2 Jaro-Winkler 6 0.905 0.983 0.838 0.558 0.984 0.389 L2 Jaro-Winkler 4 0.957 0.956 0.958 0.817 0.986 0.698 Monge-Elkan 8 Cora (II) 0.938 0.932 0.944 0.916 0.933 0.899 Jaro-Winkler 5 Rexa/DBLP 0.971 0.952 0.992 0.937 0.952 0.922 Jaro-Winkler 3 AKT/DBLP 0.939 0.956 0.923 0.916 0.956 0.879 L2 Jaro-Winkler 2 0.895 0.832 0.969 0.887 0.833 0.950 Jaro-Winkler 1 AKT/Rexa F1 Recall Prec. F1 Recall Prec. Publication Matcher No Dataset
Experiments ,[object Object],[object Object],[object Object],0.936 0.895 0.981 0.928 0.879 0.983 L2 Jaro-Winkler 10 Cora (I) 0.997 0.994 0.999 0.997 0.994 0.999 L2 Jaro-Winkler 11 Cora (II) 0.920 0.876 0.968 0.846 0.755 0.965 Jaro-Winkler 9 Rexa/DBLP 0.714 0.921 0.583 0.621 0.746 0.532 L2 Jaro-Winkler 8 AKT/DBLP 0.855 0.935 0.788 0.806 0.888 0.738 L2 Jaro-Winkler 7 AKT/Rexa F1 Recall Prec. F1 Recall Prec. Person Matcher No Dataset
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Advanced scenario ,[object Object],[object Object]
Data linking: current state ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Reference datasets
Problems ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],A B Reference
Schema matching ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],+ - DBPedia dbpedia:Actor - + LinkedMDB movie:Actor David Garrick Richard Nixon Repository
KnoFuss - enhanced Source KB Target KB SPARQL query translation Knowledge fusion Ontology integration Knowledge base integration Ontology matching Instance transformation Coreference resolution Dependency resolution
Schema matching ,[object Object],[object Object],Ontology 1 Ontology 2 Dataset 1 Dataset 2 Ontology 1 Ontology 2 Dataset 1 Dataset 2
[object Object],[object Object],[object Object],Overview
Algorithm ,[object Object],[object Object],LinkedMDB DBPedia movie:music_contributor/2490 MusicBrainz music:artist/a16…9fdf = = dbpedia:Ennio_Morricone
Algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],LinkedMDB DBPedia MusicBrainz music:artist/a16…9fdf = = dbpedia:Ennio_Morricone movie:music_contributor/2490 movie:music_contributor dbpedia:Artist is_a is_a
[object Object],[object Object],[object Object],[object Object],[object Object],Algorithm
Algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Results ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],DBPedia/ DBLP DBPedia/ LinkedMDB DBPedia/ BookMashup
Future work ,[object Object],[object Object],[object Object],[object Object],[object Object]
Questions? ,[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object]

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Fusing semantic data

  • 1. Fusing automatically extracted annotations for the Semantic Web Andriy Nikolov Knowledge Media Institute The Open University
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7. Motivating scenario – X-Media RDF Images Other data Annotation Fusion Text Internal corporate reports (Intranet) Pre-defined public sources (WWW) Domain ontology KnoFuss Knowledge base
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13. Task decomposition Source KB Target KB (fused) Target KB Knowledge fusion Coreference resolution Knowledge base updating Model configuration Dependency identification Dependency resolution Link discovery
  • 14.
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  • 24. Example #Paper_10 Article Proceedings owl:disjointWith rdf:type rdf:type
  • 25. Example #Paper_10 2 Article #Paper_10 2 Proceedings #Paper_10 Article Proceedings owl:disjointWith rdf:type rdf:type
  • 26. Example #Paper_10 2 Article #Paper_10 2 Proceedings Article v : Proceedings #Paper_10 Article Proceedings owl:disjointWith rdf:type rdf:type
  • 27. Example #Paper_10 2 Article #Paper_10 2 Proceedings Article v : Proceedings m(true)=0.8 m(false) = 0 m({true;false})=0.2 m(true)=0.6 m(false) = 0 m({true;false})=0.4 m( )=1.0 m( )=0.0 true true false true true false false false #Paper_10 2 Proceedings #Paper_10 2 Article
  • 28. Example #Paper_10 2 Article #Paper_10 2 Proceedings Article v : Proceedings m(true)=0.8 m(false) = 0 m({true;false})=0.2 m(true)=0.6 m(false) = 0 m({true;false})=0.4 m( )=0.15 ­ -Dempster’s rule m( m( )=0.23 )=0.62 true false false true false true true false false false #Paper_10 2 Proceedings #Paper_10 2 Article
  • 29. Example #Paper_10 2 Article #Paper_10 2 Proceedings Article v : Proceedings m(true)= 0.62 m(false) = 0.23 m({true;false})= 0.15 m(true)= 0.23 m(false) = 0.62 m({true;false})= 0.15 m( )=0.15 m( m( )=0.23 )=0.62 true false false true false true true false false false #Paper_10 2 Proceedings #Paper_10 2 Article
  • 30.
  • 31.
  • 32.
  • 33.
  • 34.
  • 35.
  • 36.
  • 37.
  • 38.
  • 39.
  • 41.
  • 42.
  • 43. KnoFuss - enhanced Source KB Target KB SPARQL query translation Knowledge fusion Ontology integration Knowledge base integration Ontology matching Instance transformation Coreference resolution Dependency resolution
  • 44.
  • 45.
  • 46.
  • 47.
  • 48.
  • 49.
  • 50.
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  • 53.