KARMA. Pedro Szekely and Craig A. Knoblock. University of Southern California, Information Sciences Institute

Transcription

1 KARMA Pedro Szekely and Craig A. Knoblock Information Sciences Institute

2 Outline What doors does Karma open? What is Linked Data? Why is Karma the best tool? How does Karma benefit you?

3 What Is Karma? tears down data silos dsearles/flickr

4 What Is Karma? tears down data silos connects information in separate databases dsearles/flickr

5 What Is Karma? tears down data silos connects information in separate databases exposes untapped value of database content dsearles/flickr

6 What Is Karma? tears down data silos connects information in separate databases exposes untapped value of database content opens doors to new forms of monetization dsearles/flickr

7 Karma s Audience Entertainment Cultural heritage Intelligence Science... anyone who has data silo problems

8 Current Dilemma if I could see

9 Karma Connects Data

10 Linked Data Subject Predicate Object

11 IMDB Linked Data Movie Actor Invincible Mark Wahlberg Subject Predicate Object

12 IMDB Linked Data Movie Actor Invincible Mark Wahlberg Subject Predicate Object Invincible type actor Movie Mark Wahlberg

13 Linked Data Invincible type actor Movie Mark Wahlberg IMDB

14 Linked Data Invincible subject type actor Movie Mark Wahlberg Vince Papale IMDB

15 Linked Data Invincible subject type actor Movie Mark Wahlberg Vince Papale filming location Central High IMDB DisneyWiki

16 Linked Data Invincible subject type actor Movie Mark Wahlberg Vince Papale filming location Central High alumni Bill Cosby IMDB DisneyWiki

17 Linked Data Invincible subject type actor Movie Mark Wahlberg Vince Papale filming location played Football played Central High Bill Cosby alumni IMDB DisneyWiki Wikipedia

18 Linked Data Invincible subject type actor Movie Mark Wahlberg Vince Papale played Football profession filming location Actor Central High alumni played Bill Cosby profession IMDB DisneyWiki Wikipedia NY Times

19 Linked Data connects data silos

20 Linked Data connects data silos Karma helps you create it

21 Where Is Linked Data Stored?

22 Where Is Linked Data Stored? Triple Store

23 You Control Access to Your Linked Data Triple Store inside your firewall outside, shared with the public selective sharing with your partners

24 non technical staff, distributors, marketing can query your data Karma Faceted Browsers... Triple Store

25 find all football sequences in content produced by Disney filmed in Philadelphia related to Bill Cosby and show me the NY Times reviews

26 Karma Triple Store

27 SQL FileMaker Pro XML, JSON Web APIs, REST Excel, CSV Karma Triple Store

28 Karma Interactive tool for rapidly extracting, cleaning, transforming, and publishing data Tabular Sources Hierarchical Sources Services Karma Model CSV Database KML Craig Knoblock University of 28

29 Karma Models Karma Model = Meaning of the data in each column + Relationships among data columns Craig Knoblock 29

30 Semantic Types: Meaning of Data in Columns Semantic vs Syntactic Types String String String Date String String Not useful for information integration

31 Semantic Types: Meaning of Data in Columns Semantic vs Syntactic Types Name of Person Nationality of Entity Location of Event Date of Event Type of Event Description of Event

32 Semantic Types Defined Using an Ontology Classes Properties Ontology Person Entity Event Event Event Event Name Nationality Location Date Type Description Craig Knoblock University of 32

33 Relationships Among Columns victim? perpetrator? reporter? same event? different event? Ontology Person Entity Event Event Event Event Name Nationality Location Date Type Description Craig Knoblock University of 33

34 Relationships Specified in Terms of Classes and Properties perpetrator Organization Event member Person Location Date Type Description Name Nationality

35 Karma CONOPS Alignment 1. load sample 2. demonstrate transformations 3. model Execution 1. load data 2. apply model 3. export

36 Alignment 1. load sample 2. demonstrate transformations 3. model

37 Alignment 1. load sample 2. demonstrate transformations 3. model

38 Alignment 1. load sample 2. demonstrate transformations 3. model

39 Alignment 1. load sample 2. demonstrate transformations 3. model

40 Alignment 1. load sample 2. demonstrate transformations 3. model

41 Batch Execution 1. load data 2. apply model 3. export

42 Batch Execution dataset A + model A RDF A dataset B + model B RDF B dataset C + model C RDF C select name, location of people associated with FARC Triple Store (RDF)

43 Karma Benefits does not alter original database librarians can use it to map the data points the way to new businesses and products brings to light fresh storylines and fresh ways to mashup and repackage content

44 Connecting the Smithsonian American Art Museum to the Linked Data Cloud Pedro Szekely, Craig A. Knoblock, Fengyu Yang, Xuming Zhu, Eleanor E. Fink, Rachel Allen, and Georgina Goodlander, Los Angeles, California, USA Nanchang Hangkong University, Nanchang, China Smithsonian American Art Museum, Washington, DC, USA

45 The Smithsonian American Art Museum is a museum in Washington, D.C. which has one of the world's largest and most inclusive collections of art, from the colonial period to the present, made in the United States. Wikipedia

46 Big Picture

47 Problem SAAM Data What ontology to use? Data consistency Structure mismatches What to link to? 100% precision How to enable museums to do this themselves?

48 Steps to Create Linked Data Map data to RDF select ontologies define mappings Link to external resources identify the links Curate the Linked Data museums demand 100% correctness

49 select ontologies

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51 Complicated Many irrelevant classes and properties Incomplete

52 ore:aggregation edm:europeanaaggregation crm:e89_propositional_object edm:webresource edm:hasview edm:aggregatedcho edm:providedcho aac:culturalheritageobject dcterms:creator edm:agent/crm:e39_actor, foaf:person aac:person aac:associatedplace rdagr2:placeofbirth rdagr2:placeofdeath edm:place/crm:e53_place aac:place schema:address schema:postaladdress

53 skos:preflabel skos:concept skos:narrower dcterms:title dcterms:description dcterms:medium saam:objectid skos:preflabel saam:objectnumber saam:constituentid skos:preflabel skos:altlabel skos:concept edm:hastype rdagr2:biographicalinformation ore:aggregation edm:europeanaaggregation edm:aggregatedcho edm:providedcho aac:culturalheritageobject dcterms:creator edm:hasview edm:agent/crm:e39_actor, foaf:person aac:person rdagr2:placeofbirth aac:associatedplace edm:place/crm:e53_place aac:place dcterms:subject crm:e89_propositional_object edm:webresource dcterms:created dcterms:rights dcterms:format dcterms:date dcterms:provenance rdagr2:dateofbirth rdagr2:dateofdeath rdagr2:dateassociated WithThePerson rdagr2:placeofdeath schema:name schema:country skos:preflabel schema:addresscountry schema:addressregion schema:address schema:postaladdress schema:addresslocality

54 mapping the data to the ontologies how to enable museums to do this themselves?

55 USC Linked Data Conversion Tool

56 Evaluation of Data Mapping Using Karma SAAM database 8 tables 29 columns Ontologies 407 classes 105 data properties 229 object properties Run 1: no training data Run 2: using Run 1 as training # of times Karma s top 4 suggestions contain the correct semantic type # of times Karma correctly assigns object properties 7 out of 29 (24%) 30 out of 35 (85%) out of 29 (93%) 32 out of 35 (91%) 8 Time (minutes)

57 identifying and curating links

58 Multiple John Singer Sargent cb:person_john_singer_sargent a aac-ont:person ; ont0:dateofbirth "1879", "1885" ; ont0:dateofdeath "1925" ; foaf:name "John Singer Sargent". ima:person_john_singer_sargent a aac-ont:person ; dct:date " " ; foaf:name "John Singer Sargent". dallas:person_john_singer_sargent a aac-ont:person ; ont0:dateofbirth "1856" ; ont0:dateofdeath "1925" ; foaf:name "John Singer Sargent". saam:person_4253 a aac-ont:person ; met:person_john_singer_sargent aac-ont:associatedplace a aac-ont:person ; saam:saamplace_ t1r13950_0, ont0:placeofresidence saam:saamplace_ t1r13951_0 ; "North and Central America", saam:constituentid "4253" ; "United States" ; rdagr2:biographicalinformation foaf:name "John Singer Sargent". Painter. Sargent traveled " ; rdagr2:dateassociatedwiththeperson " , " " ; rdagr2:dateofbirth " " ; rdagr2:dateofdeath " " ; rdagr2:placeofbirth saam:saamplace_ t1r13952_0 ; rdagr2:placeofdeath saam:saamplace_ t1r13953_0 ; foaf:name "John S. Sargent" ; skos:altlabel "John S. Sargent" ; skos:preflabel "John Singer Sargent".

59 John Singer Sargent cb:saamperson_john_singer_sargent a saam:saamperson ; ont0:dateofbirth "1879", "1885" ; ont0:dateofdeath "1925" ; skos:preflabel "John Singer Sargent". ima:saamperson_john_singer_sargent a saam:saamperson ; dct:date " " ; foaf:name "John Singer Sargent". dallas:saamperson_john_singer_sargent a saam:saamperson ; ont0:dateofbirth "1856" ; ont0:dateofdeath "1925" ; foaf:name "John Singer Sargent". saam:saamperson_4253 a saam:saamperson ; met:saamperson_john_singer_sargent saam:associatedplace a saam:saamperson ; saam:saamplace_ t1r13950_0, ont0:placeofresidence saam:saamplace_ t1r13951_0 ; "North and Central America", saam:constituentid "4253" ; "United States" ; rdagr2:biographicalinformation foaf:name "John Singer Sargent". Painter. Sargent traveled " ; rdagr2:dateassociatedwiththeperson " , " " ; rdagr2:dateofbirth " " ; rdagr2:dateofdeath " " ; rdagr2:placeofbirth saam:saamplace_ t1r13952_0 ; rdagr2:placeofdeath saam:saamplace_ t1r13953_0 ; skos:altlabel "John S. Sargent" ; skos:preflabel "John Singer Sargent".

60 Linking John Singer Sargent saam:person_4253 owl:sameas cb:person_john_singer_sargent ; owl:sameas dallas:person_john_singer_sargent ; owl:sameas ima:person_john_singer_sargent ; owl:sameas met:person_john_singer_sargent ; owl:sameas dbpedia:john_singer_sargent ; owl:sameas nytimes:n ; owl:sameas w-flick:john_singer_sargent ;....

61 Intuition Estimate discrimination power of properties, e.g., of name, birth and death dates every combination of dates birth date death date # of people similar idea to Song, D., Heflin, J.: Domain-independent entity coreference for linking ontology instances. ACM Journal of Data and Information Quality (ACM JDIQ) (2012)

62 Evaluation of Automatic Linking SAAM names starting with A matched by hand 535 people 176 matches

63 Results of Automatic Linking DBPedia 2,194 New York Times 70 estimate 30 missing links to DBpedia Getty ULAN 2,110 Rijksmuseum 551 Geonames 3,068

64 Curating Links with Karma

65 Linking with Karma

66 results of automated linking and interactive curation recorded using PROV owl:sameas statements constructed using SPARQL CONSTRUCT queries over PROV records

67 deployment

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72 Related Work Europeana 17 million items, 1,500 institutions Require exports in Europeana format Amsterdam Museum, Museum Finland Rich ontology, RDF to RDF mapping rules LODAC museums in Japan 114 museums, simple ontology Research Space, British Museum CIDOC CRM ontologies, complex mappings We focused significantly on Linking identification and curation

73 Next Steps Applications leveraging linked data Virtual museum Tools to create multimedia stories about art Tools to find inconsistencies Feed data to wikidata American Art Collective: a linked data consortium of museums

74 Virtual Museum John Singer Sargent Smithsonian LACMA MET Brooklyn DMA IMA British Museum TATE Louvre Imperial War Museum

75 Multimedia Stories

76 github.com/informationintegrationgroup/web-karma

77 Merci