1. Linked Data at
Semantic Team
semantica@corp.globo.com
Tatiana Al-Chueyr and Rodrigo D. A. Senra
{tatiana.martins, rodrigo.senra}@corp.globo.com
globo.com

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

3. Franklin Amorim
João Caros Mendes
Alberto Beloni
André Nicodemus
Contributors

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

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

6. Politician
MotivationCross-link content from different web products

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

8. 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

9. Recommend annotations to information Producer
Motivation

10. Suggest related content to information Consumer
Motivation

11. Suggest related content to information Consumer
Motivation

12. Suggest related content to information Consumer
Motivation

13. 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

14. Status Quo
Used by the main web products of Globo.com
linking, among others:
○ 18,485 organizations
○ 82,386 people
○ 9,129 places
○ 1,000,000+ annotated news
from August 2010 to May 2013

15. Legacy Architecture
CDA
CMA
triple
store
search
engine
ontology

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

17. 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

18. Problems

19. 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

20. Possible Solution
Upper
Ontology

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

22. 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)
Next Step

23. Brainiak
linked data restful
API

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

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

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

27. 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

28. /sports/Team
Brainiak query
GET

29. SPARQL response

30. Brainiak response

31. Brainiak concepts
● Instance
● Collection (set of instances from a given Class)
● Schema (the Class definition)
● Context

32. Instance

33. Collection

34. Schema

35. Context

36. place
State
Brazil
Country
Japan
City
Real example

37. /placeGET
/place/CountryGET
/place/Country/_schemaGET
/place/Country/BrazilGET
Real example

38. resource URL→ /place/Country/Brazil
context (graph)→ http://semantica.globo.com/place/
class → http://semantica.globo.com/place/Country
instance → http://semantica.globo.com/place/Country/Brazil
URI Conventions

39. /place/River
?graph_uri=http://dbpedia.org/resource/classes#
&class_uri=dbpedia:River
Overriden
context (graph) → http://dbpedia.org/resource/classes#
class → http://dbpedia.org/ontology/River
Convention
context (graph)→ http://semantica.globo.com/place/
class → http://semantica.globo.com/place/River
Legacy URIs

40. Hypermedia
● Flexibility and programmatic adaptation
● Semantic affordances
● Client has to understand what is consumed
● "Hypermedia APIs are not fully baked yet"

41. Brainiak hypermedia graph
context instance
/ schema
inCollection
item
instances
instances
describedBy
self
replace
delete
self
instances
self
self
self
create
collection

42. Services
● List Contexts
● List Collections
● Get a Schema
● List Prefixes
● Status of Services
● Create
● Retrieve
● Delete
● Edit
● List
Instances

43. Features
● JSON-Schema
● JSON-LD
● REST
● Python + Tornado
OPTIONS GET PUT POST DELETE

44. /sports/Team
Brainiak query
GET

45. Brainiak response

46. Brainiak response

47. Brainiak response

48. Brainiak response

49. 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

50. 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

51. 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

52. /place/City/_schema
Brainiak query
GET

53. ● SEO (automatic schema.org)
● Improved annotator (DBpedia Spotlight)
● Richer content relationships (inference)
● Link to open data (e.g. DBPedia, dados.gov.br)
Next steps

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

55. Semantic Team
semantica@corp.globo.com
globo.com
Thank you
for the attention!