Prsenta

1. Linked Data at
Tatiana Al-Chueyr Martins
tatiana.martins@corp.globo.com
@tati_alchueyr
18 de setembro de 2013, Simpósio Rio Info
globo.com

2. BROADCAST MOVIES PAY TV INTERNET
EVENTS MUSIC
PUBLISHING
NEW VENTURES NEWSPAPERRADIO NETWORK

3. Andréia Bustamante
Ícaro Medeiros
Tatiana Al-Chueyr
Rodrigo Senra
Semantic Team

4. Franklin Amorim
Diogo Kiss
Contributors

5. MotivationNot only words
São Paulo

6. MotivationNot only words
São Paulo?

7. MotivationNot only words
São Paulo state

8. MotivationNot only words
São Paulo city

9. MotivationNot only words
São Paulo saint

10. MotivationNot only words
São Paulo soccer team

11. MotivationMultiple words for the same thing
Female
f
F
female
woman
...

12. MotivationMultiple words for the same thing
http://data.globo.com/female

13. Motivation
Soccer player
Cross-link content from different web products

14. Politician
MotivationCross-link content from different web products

15. Celebrity
Motivation
● Cross-link content from different web products
MotivationCross-link content from different web products

16. Isabella Nardoni foi morta em 29 de março de 2008
na Zona Norte de São Paulo (Foto:Reprodução)
Isabella de Oliveira Nardoni, de 5
anos, foi morta na noite de 29 de
março de 2008. A perícia concluiu
que a menina foi atirada do sexto
andar do prédio onde moravam seu
pai, Alexandre Nardoni, sua
madrasta, Anna Carolina Jatobá, e
dois filhos pequenos do casal, na
Vila Isolina Mazzei, na zona norte de
São Paulo.
Túmulo de Isabella vira local de visitação em SP; casal Nardoni está preso.
Caso Isabella Nardoni
Juliana Cardilli G1 SP
RDF
FOAF
GEO
Dublin
Core
SKOS
Semantic markup in web pages
Motivation

17. Recommend annotations to information Producer
Motivation

18. Suggest related content to information Consumer
Motivation

19. Suggest related content to information Consumer
Motivation

20. Suggest related content to information Consumer
Motivation

21. Changes
● Replacement of words by entities
http://data.globo.com/person/Person/santos_dumont

22. Changes
● Replacement of labels by qualified relationships

23. Changes
● Organize data from tables to graphs

24. Outcomes
● To replace words by entities improved:
○ Finding
○ Linking
○ Reconciling
○ Organizing
multiple layers of information

25. Outcomes
● Flexible ways to organize content
● Ease to find related issues
● Explicit relations derived from annotated content
● Up-to-date topic pages with little editorial effort
● Linking content across different web products
● Seamless navigation leading to flow state

26. Status Quo
Used by the main web products of Globo.com:
○ 18,485 organizations
○ 83,000 people
○ 9,129 places
○ 1,000,000+ annotated news
Which sum up 2,500,000+ entities!
from August 2010 to May 2013

27. Linked data
problems

28. Legacy Architecture
CDA
CMA
triple
store
search
engine
ontology

29. CDA
CMA
CDA
CMA
CDA
CMA
CDA
CMA
Legacy Architecture
triple
store
search
engine
ontology

30. Poor data management
○ direct access to triple store (unmanaged)
○ difficulty to share data (distributed DBs)
○ re-sync triple-store and search engine index
○ scalability of triple store
○ high entropy in distributed ontology engineering
Problems

31. Problems

32. Ontology Engineering
Domain-driven
(current)
Base
G1 GE EGO TVG
news sports gossip tv
Upper
Person Organization
Music
Politics
Programme Education
Sports
Product-driven
(past)
Place

33. Possible Solution
Upper
Ontology

34. Semantic as a library
○ many different versions in production
○ programming language dependent
○ steep learning curve for RDF/OWL/SPARQL
Problems

35. Create an open semantic data management platform
● Scalable
● Mobile and Web friendly
● Interconnect Globo's data with external data sources
● Automate content extraction (including NER)
Solution

36. Brainiak
linked data restful
API

37. CDA
CMA
CDA
CMA
CDA
CMA
CDA
CMA
Legacy Architecture
triple
store
search
engine
ontology

38. API
Brainiak
CMA
CDA
CDA
CDA
CDA
triple
store
search
engine
Under Development

39. Requirements
● Indirect usage of SPARQL
● Programming language independent
● Data management with quality
● Finer-grained authorization and authentication
● Isolate applications from triplestore
● Improve triplestore performance

40. SPARQL query
DEFINE input:inference <http://data.globo.com/ruleset>
SELECT ?uri ?label
FROM <http://data.globo.com/sports/>
WHERE
{
?uri a <http://data.globo.com/sports/Team>;
rdfs:label ?label .
}
LIMIT 10
OFFSET 0
task: list all sports teams

41. /sports/Team
Brainiak query
GET

42. SPARQL response

43. Brainiak response

44. SPARQL query
SELECT DISTINCT ?class
WHERE {
<http://data.globo.com/place/City> rdfs:subClassOf ?class OPTION
(TRANSITIVE, t_distinct, t_step('step_no') as ?n, t_min (0)) .
?class a owl:Class .
}
task: retrieve all superclasses of a class

45. SPARQL query
SELECT DISTINCT ?predicate ?predicate_graph ?predicate_comment ?type ?range ?title ?range_graph ?range_label ?super_property
WHERE {
{
GRAPH ?predicate_graph { ?predicate rdfs:domain ?domain_class } .
} UNION {
graph ?predicate_graph {?predicate rdfs:domain ?blank} .
?blank a owl:Class .
?blank owl:unionOf ?enumeration .
OPTIONAL { ?enumeration rdf:rest ?list_node OPTION(TRANSITIVE, t_min (0)) } .
OPTIONAL { ?list_node rdf:first ?domain_class } .
}
FILTER (?domain_class IN (<http://data.globo.com/place/City>, <http://data.globo.com/place/GeopoliticalDivision>, <http://data.globo.com/place/Place>, <http://data.globo.
com/upper/Object>, <http://data.globo.com/upper/Substance>, <http://data.globo.com/upper/ConcreteEntity>, <http://data.globo.com/upper/Entity>))
{?predicate rdfs:range ?range .}
UNION {
?predicate rdfs:range ?blank .
?blank a owl:Class .
?blank owl:unionOf ?enumeration .
OPTIONAL { ?enumeration rdf:rest ?list_node OPTION(TRANSITIVE, t_min (0)) } .
OPTIONAL { ?list_node rdf:first ?range } .
}
FILTER (!isBlank(?range))
?predicate rdfs:label ?title .
?predicate rdf:type ?type .
OPTIONAL { ?predicate rdfs:subPropertyOf ?super_property } .
FILTER (?type in (owl:ObjectProperty, owl:DatatypeProperty)) .
FILTER(langMatches(lang(?title), "en") OR langMatches(lang(?title), "")) .
OPTIONAL { ?predicate rdfs:comment ?predicate_comment }
FILTER(langMatches(lang(?predicate_comment), "en") OR langMatches(lang(?predicate_comment), "")) .
OPTIONAL {
GRAPH ?range_graph {
?range rdfs:label ?range_label .
FILTER(langMatches(lang(?range_label), "en") OR langMatches(lang(?range_label), "")) .
}
}
}
task: retrieve all properties of a group of classes

46. SPARQL query
SELECT DISTINCT ?predicate ?min ?max ?range ?enumerated_value ?enumerated_value_label
WHERE {
<http://data.globo.com/place/City> rdfs:subClassOf ?s OPTION (TRANSITIVE, t_distinct, t_step('step_no') as ?n,
t_min (0)) .
?s owl:onProperty ?predicate .
OPTIONAL { ?s owl:minQualifiedCardinality ?min } .
OPTIONAL { ?s owl:maxQualifiedCardinality ?max } .
OPTIONAL {
{ ?s owl:onClass ?range }
UNION { ?s owl:onDataRange ?range }
UNION { ?s owl:allValuesFrom ?range }
OPTIONAL { ?range owl:oneOf ?enumeration } .
OPTIONAL { ?enumeration rdf:rest ?list_node OPTION(TRANSITIVE, t_min (0)) } .
OPTIONAL { ?list_node rdf:first ?enumerated_value } .
OPTIONAL {
?enumerated_value rdfs:label ?enumerated_value_label .
} .
}
}
}
task: retrieve the cardinalities of all properties of a certain class

47. /place/City/_schema
Brainiak query
GET

48. ● Enrich Globo.com search
● SEO (automatic schema.org)
● Improve annotator (DBpedia Spotlight)
● Richer content relationships (inference)
● Link to open data (e.g. DBPedia, dados.gov.br)
Next steps

49. Stay tuned
@brainiak_api
... will be soon released
as an open source project !

50. http://www.slideshare.net/
@semantic_team
@alchueyr
Slides

51. tatiana.martins@corp.globo.com
semantica@corp.globo.com
globo.com
Thank you
for the attention!