The document discusses using semantic web technologies to make big data smarter. It provides an overview of key concepts in semantic web, including linked data and ontologies. It describes how semantic web can add structure and meaning to unstructured data through modeling data as graphs and defining relationships and properties. The goal is to publish and query interconnected data at scale to enable new types of queries and inferences over big data.

1. Using the Semantic Web
Stack to Make
Big Data Smarter
Matheus Mota
PhD Candidate @ LIS.IC.UNICAMP
@matheusmota
/msmota
matheusmota.com

2. “
Semantic web is an extension of the WWW that enables
both sharing and integration of content beyond the
boundaries of applications and websites"
http://www.dataversity.net/big-data-semantic-web-technology-data-visualization
Semantic Web
Adapted from
DataVersity
2

3. “
Structured extra stuff that you should put under
web's hood to feed our robots".
http://www.dataversity.net/big-data-semantic-web-technology-data-visualization
Semantic Web
3

4. http://www.dataversity.net/big-data-semantic-web-technology-data-visualization
Semantic Web
4

5. http://www.dataversity.net/big-data-semantic-web-technology-data-visualization
Semantic Web
5

6. http://www.dataversity.net/big-data-semantic-web-technology-data-visualization
Semantic Web
6

7. http://www.dataversity.net/big-data-semantic-web-technology-data-visualization
Semantic Web
7

8. “
big data is going to give to semantic web
the massive amounts of metadata it needs
to really get traction."
http://radar.oreilly.com/2011/06/big-data-and-the-semantic-web.html
Big Semantic
Web of Data
Edd Dumbill
8

9. WWW yesterday
HyperText
9

10. WWW as a platform
HyperText
10

11. “
big data is going to give to semantic web
the massive amounts of metadata it needs
to really get traction."
http://radar.oreilly.com/2011/06/big-data-and-the-semantic-web.html
Big Semantic
Web of Data
Edd Dumbill
11

12. http://radar.oreilly.com/2011/06/big-data-and-the-semantic-web.html
Semantic Web: (new?) players
Source: [3]
12

13. The so-called
Semantic Web Cake
You won't need it all to bootstrap
13

14. The so-called Semantic Web Stack
14

15. The so-called Semantic Web Stack
Future
Present
Past
15

16. Semantic Web - The Goal
16

17. Getting Started
Sounds cool. How can I play?
17

18. “
-Flexible Modeling for interconnected data
-Agile Evolution of the Data Model
-Scalable Evaluation of Join-Intensive Queries/Paths
It all starts with graphs".
Is it good for your problem/scenario/data?
18

19. 19
Types of Graph-related tasks - Link Prediction

20. 20
Types of Graph-related tasks - Link Prediction
?
?
?
?

21. 21
Topology aware Link Prediction
1
4
4
3

22. 22
1
4
4
3
<new_user>
<famousEnt>
Topology aware Link Prediction

23. 23
Topology Agnostic - Properties/Semantics

24. 24
sameAs
rdf:type
Topology Agnostic - Properties/Semantics

25. Semantic Web vs. Linked Data
25

26. 26
LinkedData
Semantic Web vs. Linked Data

27. DBPedia
27

28. 28
DBPedia

29. 29
DBPedia

30. Semantic Web - The Goal
30

31. Semantic Web - The Goal
31

32. Publishing/
Querying
How to?
P/Q
32

33. Publishing: HTML Embedded
-Microformats
-Microdata
-RDFa
-N3-in-HTML
33

34. It is already out there
34

35. It is already out there
35

36. It is already out there
http://radar.oreilly.com/2011/06/big-data-and-the-semantic-web.html 36

37. P/Q: Data (SPARQL) Endpoints
-OpenLink Virtuoso
-Allegro Graph
-Apache Jena + SQL BD
-GraphDB
-Adhoc Interface + another graph DB (neo4j, etc)
37

38. P/Q: Data (SPARQL) Endpoints
38

39. P/Q: Main Challenges
39
- ETL
- Performance
- Motivation =(

40. Going Further
What is next?
40

41. Ontology/
Inference
Modeling and Inferences
O/I
41

42. Modeling
RDF
Triple Structure.
RDFs
Adds taxonomic
relations, object
relations and
datatype properties.
OWL
Richer vocabulary;
cardinality, logical
operators; semantic
relationships
between objects.
42

43. RDF/ RDFs
43
ns:motorV
ns:SUV
rdfs:subClassOf
rdf:type
rdf:type
a rdfs:Class

44. RDF/ RDFs
44
ns2:X_33
ns2:seller 1
ns:sold
ns:motorV
ns:SUV
rdfs:subClassOf
rdf:type
rdf:type
a rdfs:Class
rdfs:range

45. RDF/ RDFs / OWL
45
ns2:x_33
ns2:seller 1
ns:measured
ns:4wheeler
ns:SUV
rdfs:subClassOf
rdf:type
rdf:type
a rdfs:Class
rdfs:range
ns:motorV
rdfs:subClassOf
rdf:type
a

46. RDF/ RDFs / OWL
46
ns2:x_33
ns2:seller 1
ns:measured
ns:4wheeler
ns:SUV
rdfs:subClassOf
rdf:type
rdf:type
a rdfs:Class
rdfs:range
ns:motorV
rdfs:subClassOf
ns:2wheeler
rdf:type
a owl:disjoint

47. RDF/ RDFs / OWL
47

49. ns:4wheeler
ns:SUV
rdfs:subClassOf
rdf:type
rdf:type
a rdfs:Class
ns:motorV
rdfs:subClassOf
ns:2wheeler
rdf:type
a owl:disjoint
Inferences

50. ns:4wheeler
ns:SUV
rdfs:subClassOf
rdf:type
rdf:type
a rdfs:Class
ns:motorV
rdfs:subClassOf
ns:2wheeler
rdf:type
a owl:disjoint
rdfs:subClassOf
Inferences

51. ns:van
ns:4wheeler
rdfs:subClassOf
ns:kombi
rdf:type
rdf:type
rdf:type
a rdfs:Class
Inferences

52. ns:van
ns:4wheeler
rdfs:subClassOf
ns:kombi
rdf:type
rdf:type
rdf:type
a rdfs:Class
rdfs:type
Inferences

53. Research @ LIS
Powered by LinkedData
53

54. Structure Aware Document
Annotation and Indexation
54

55. Hypothesis
55

56. Scenario
• High volume of heterogeneous textual documents
• Tasks could be better executed if such structure is
available
56

57. Scenario
• High volume of heterogeneous textual documents
• Tasks could be better executed if such structure is
available
57
Clustering
Similarity Det.
Copy detection
Ranking

58. 58

59. 59
Endpoint: http://sparql.lis.ic.unicamp.br
Graph: http://lis.ic.unicamp.br/fnjv

60. 60
Query: “which documents mention species that appear in a polygon?”

61. Extracting Data From Biology-
related Spreadsheets
61

62. Scenario
62

63. 63
Mental-Map - Pattern on Creation
SS Classification event, Proc., Object

64. 64

65. http://purl.org/biospread
65

66. Data Endpoint: http://sparql.lis.ic.unicamp.br
Project: http://purl.org/biospread/
66

67. Thanks!
Any questions?
67
@matheusmota
/msmota
matheusmota.com
mota@ic.unicamp.br@

68. Acknowledgments Credits
Special Thanks to
◎ Professor André Santanchè (including slides)
◎ Laboratory of Information Systems (http://lis.ic.unicamp.br)
◎ Institute Of Computing - UNICAMP
Thanks to all the people who made and released these awesome
resources for free:
◎ Presentation template by SlidesCarnival
◎ Photographs by Unsplash & Death to the Stock Photo (license)
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