Enterprises are drowning in data that they can't find, access, or use. For many years, enterprises have wrestled with the best way to combine all that data into actionable information without building systems that break as schemas evolve. Approaches like warehousing and ETL can be brittle in the face of changing data sources or expensive to create. Data integration at the application level is common but this results in significant complexity in the code. Data-oriented web services attempt to provide reusable sources of integrated data, however these have just added another layer of data access that constrain query and access patterns. This talk will look at how semantic web technologies can be used to make existing data visible and actionable using standards like RDF (data), R2RML (data translation), OWL (schema definition and integration), SPARQL (federated query), and RIF (rules). The semantic web approach takes the data you already have and makes that data available for query and use across your existing data sources. This base capability is an excellent platform for building federated analytics.

1. Releasing
Relational Data to
the Semantic Web
Alex Miller
amiller@revelytix.com
1

2. Semantic web
Relational data
Federation
HREIW
Analytics
2

3. There are things we wish
to describe.
3

4. We need some way to
identify each thing.
4

5. A URI is abo ut
"identifying" things,
not
"locating" things (a
URL).
On the web, we identify
things with a URI.
5

6. dbp:Chicago_(band)
dbp:Wrigley_Field
dbp:The_Blues_Brothers_(film)
dbp:Chicago
dbp:Chicago_Cubs dbp:Barack_Obama
dbp:Pizza
dbp: http://dbpedia.org/resource/
6

7. Things are more
interesting if we relate
them.
Relationships are also
described by a URI.
7

8. dbp:Chicago_(band)
dbp:The_Blues_Brothers_(film)
dbp:Wrigley_Field
n
db tio
po oca
:lo
c _l
m
at
ion :fil
ie
ov
m
dbpo:owner
dbp:Chicago
dbp
o:r
e si
den
c e
dbp:Chicago_Cubs
dbp:Barack_Obama
dbp:Pizza
dbp: http://dbpedia.org/resource/
dbpo: http://dbpedia.org/ontology/
8

9. Triple
<subject> <predicate> <object>
9

10. Subject dbp:Chicago_(band)
dbp:The_Blues_Brothers_(film)
dbp:Wrigley_Field
Predicate n
db tio
po ca
:lo o
ca _l
m
tio fil
Object
:
n ie
ov
m
dbpo:owner
dbp:Chicago
dbp
o:r
e si
den
c e
dbp:Chicago_Cubs
dbp:Barack_Obama
dbp:Pizza
dbp: http://dbpedia.org/resource/
dbpo: http://dbpedia.org/ontology/
10

11. <subject> <predicate> <object>
dbp:Wrigley_Field dbpo:location dbp:Chicago
resource resource resource
or
value
11

12. dbp:Chicago_(band)
dbp:The_Blues_Brothers_(film)
dbp:Wrigley_Field
n
db tio
po oca
:lo
c _l
m
at
ion :fil
ie
ov
m
dbpo:owner
dbp:Chicago
dbp
o:r
e si
den
c e
dbp:Chicago_Cubs
dbp:Barack_Obama
dbp:Pizza
dbp: http://dbpedia.org/resource/
dbpo: http://dbpedia.org/ontology/
12

13. Congratulations!
You now know
RDF.
13

14. If things and relationships
can be defined by any
URI, how do we know
what we're talking about?
14

15. We need metadata.
15

16. Specifically, we need a
vocabulary of common
terms that describe
our data.
16

17. A class describes a
group of things that
share common
properties.
17

18. ex:City
is a is a is a
dbp:San_Francisco dbp:Chicago dbp:Saint_Louis
dbp: http://dbpedia.org/resource/
ex: http://example.org/ontology/
rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: http://www.w3.org/2000/01/rdf-schema#
18

19. ex:City
rdf:type
rdf:type rdf:type
dbp:San_Francisco dbp:Chicago dbp:Saint_Louis
dbp: http://dbpedia.org/resource/
ex: http://example.org/ontology/
rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: http://www.w3.org/2000/01/rdf-schema#
19

20. rdfs:Class
rdf:type
ex:City
rdf:type
rdf:type rdf:type
dbp:San_Francisco dbp:Chicago dbp:Saint_Louis
dbp: http://dbpedia.org/resource/
ex: http://example.org/ontology/
rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: http://www.w3.org/2000/01/rdf-schema#
20

21. rdf:type
ex:Location rdfs:Class
rdfs:subClassOf
rdf:type
ex:City rdfs:Class
dbp: http://dbpedia.org/resource/
ex: http://example.org/ontology/
rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: http://www.w3.org/2000/01/rdf-schema#
21

22. Classes let us talk about
kinds of things. Now we
need some way to
describe attributes.
22

23. ex:City
rdf:type
ex:country ex:founded
dbp:United_States 1837
dbp:Chicago
dbp: http://dbpedia.org/resource/
ex: http://example.org/ontology/
rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: http://www.w3.org/2000/01/rdf-schema#
23

24. rdfs:do
ex:City main
rdfs:range
rdf:Property xsd:gYear
rdf:type
rdf:type
ex:founded
1837
dbp:Chicago
dbp: http://dbpedia.org/resource/
ex: http://example.org/ontology/
rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: http://www.w3.org/2000/01/rdf-schema#
24

25. Congratulations!
You now know
RDF Schema.
25

26. How do we find stuff in
this data?
SPARQL
26

27. ex:Baseball_Team ex:Stadium ex:City
rdf:type rdf:type rdf:type
dbpo:owner dbpo:location
dbp:Chicago
dbp:Chicago_Cubs
dbp:Wrigley_Field
dbp: http://dbpedia.org/resource/
dbpo: http://dbpedia.org/ontology/
27

28. ex:Stadium ex:City
rdf:type rdf:type
dbpo:owner dbpo:location
?owner ?stadium ?city
28

29. ex:Stadium ex:City
?stadium rdf:type ex:Stadium . ?city rdf:type ex:City .
rdf:type rdf:type
dbpo:owner dbpo:location
?owner ?stadium ?city
?owner dbpo:owner ?stadium . ?stadium dbpo:location ?city .
29

30. ex:Stadium ex:City
?stadium rdf:type ex:Stadium . ?city rdf:type ex:City .
rdf:type rdf:type
dbpo:owner dbpo:location
?owner ?stadium ?city
?owner dbpo:owner ?stadium . ?stadium dbpo:location ?city .
SELECT ?owner ?stadium ?city
WHERE {
?owner dbpo:owner ?stadium .
?stadium dbpo:location ?city .
?stadium rdf:type ex:Stadium .
?city rdf:type ex:City .
}
30

31. Unions
Joins SPARQL
Outer joins
Filter with criteria
Project expressions
Sort
Duplicate removal
Slice (limit / offset)
Aggregates (grouping, etc)
Subqueries
22
31

32. Semantic web
Relational data
Federation
HREIW
Analytics
32

33. Sounds interesting.
But my data is in a
relational database!
33

34. Music Database
Musicians:
MID First Last Inst_ID
1 Eddie Van Halen 10
2 Yo Yo Ma 20
3 Kenny G 30
Instruments: IID Instrument Type
10 Guitar String
20 Cello String
30 Saxophone Woodwind
34

35. Musician Schema
rdfs:Class rdf:Property
rdf:type rdf:type
rdfs:domain music:firstName
music:Musician rdfs:doma
in
rdfs music:lastName
:dom
ain
rdfs:range music:plays
music:Instrument rdfs:dom
ain
rdfs
:do
music:instName
mai
n
music:instType
35

36. Triples From Tables
Musicians: Instruments:
MID First Last Inst_ID IID Instrument Type
1 Eddie Van Halen 10 10 Guitar String
2 Yo Yo Ma 20 20 Cello String
3 Kenny G 30 30 Saxophone Woodwind
Turn each key into a resource and specify the proper
type of each resource:
artist:1 rdf:type music:Musician instrument:10 rdf:type music:Instrument
artist:2 rdf:type music:Musician instrument:20 rdf:type music:Instrument
artist:3 rdf:type music:Musician instrument:30 rdf:type music:Instrument
36

37. Triples From Tables
Musicians: Instruments:
MID First Last Inst_ID IID Instrument Type
1 Eddie Van Halen 10 10 Guitar String
2 Yo Yo Ma 20 20 Cello String
3 Kenny G 30 30 Saxophone Woodwind
Turn each cell into a triple based on the key, property
(mapped per column), and value:
artist:1 music:firstName "Eddie" instrument:10 music:instName "Guitar"
artist:1 music:lastName "Van Halen" instrument:10 music:instType "String"
artist:2 music:firstName "Yo Yo" instrument:20 music:instName "Cello"
artist:2 music:lastName "Ma" instrument:20 music:instType "String"
artist:3 music:firstName "Kenny" instrument:30 music:instName "Saxophone"
artist:3 music:lastName "G" instrument:30 music:instType "Woodwind"
37

38. Triples From Tables
Musicians: Instruments:
MID First Last Inst_ID IID Instrument Type
1 Eddie Van Halen 10 10 Guitar String
2 Yo Yo Ma 20 20 Cello String
3 Kenny G 30 30 Saxophone Woodwind
Turn each foreign key reference into a relationship
between the foreign and primary resources.
artist:1 music:plays instrument:10
artist:1 music:plays instrument:20
artist:2 music:plays instrument:30
38

39. R2RML
• "Relational to RDF Mapping Language"
• RDB2RDF Working Group at W3C
• ETL "data transformation" use case
• Dynamic "query translation" use case
• SPARQL to SQL
39

40. R2RML Triple Mapping
ain music:instName
rdfs:dom
music:Instrument
rdfs:d
omain
music:instType
Instruments:
IID Instrument Type
10 Guitar String
40

41. R2RML Triple Mapping
ain music:instName
rdfs:dom
music:Instrument
rdfs:d
omain
music:instType
Triples Map rr:tableName
Instruments:
IID Instrument Type
10 Guitar String
40

42. R2RML Triple Mapping
ain music:instName
rdfs:dom
music:Instrument
rdfs:d
omain
rr:class music:instType
Subject Map
"http://example.com/music/
Inst-{iid}"
Triples Map rr:tableName
Instruments:
IID Instrument Type
10 Guitar String
40

43. R2RML Triple Mapping
ain music:instName
rdfs:dom
music:Instrument
rdfs:d
omain
rr:class music:instType
rr:predicate
Subject Map
"http://example.com/music/
Inst-{iid}"
Predicate Map
Predicate Object
Map
Object Map
Triples Map rr:tableName
Instruments: rr:column
IID Instrument Type
10 Guitar String
40

44. @prefix rr: <http://www.w3.org/ns/r2rml#> .
@prefix music: <http://example.com/music/> .
@prefix mapping: <http://example.com/ont/> .
mapping:InstrumentMapping
a rr:TriplesMapClass;
rr:tableName "Instruments";
rr:subjectMap [
rr:template "http://example.com/music/Inst-{iid}";
rr:class music:Instrument
];
rr:predicateObjectMap [
rr:predicateMap [ rr:predicate music:instName ];
rr:objectMap [ rr:column "instrument" ];
];
rr:predicateObjectMap [
rr:predicateMap [ rr:predicate music:instType ];
rr:objectMap [ rr:column "type" ];
];
.
41

45. SPARQL translation
SPARQL Solutions
R2RML
SQL Results
Database
42

46. Semantic web
Relational data
Federation
HREIW
Analytics
43

47. SPARQL Protocol
• Standard HTTP API for calling a SPARQL
processor
• Supported by all major triple stores and
query processors
44

48. SPARQL Federation
SELECT ?artist ?song ?buyLink Return Federated data
WHERE {
SERVICE <http://listening> {
?listened rdf:type listen:event . Call SPARQL endpoint that tracks
?listened listen:artist ?artist . your listening (like last.fm)
?listened listen:song ?song }
OPTIONAL {
SERVICE <http://amazon> { Call Amazon endpoint to get info
?isbn rdf:type amaz:mp3 .
on where to download the song.
?isbn amaz:artist ?artist .
?isbn amaz:song ?song .
?isbn amaz:link ?buyLink } } }
45

49. Service Descriptions
46

50. Federator
SPARQL Endpoint
Ontology and
Federator service registry
R2RML Web SPARQL
Endpoint Endpoint Endpoint
Data Db Triple
base pedia Store
47

51. Named graph mapping
• Services can provide named graphs,
described in their service description
• Federator lets you create federated
named graphs that map to service
named graphs
48

52. Data integration
• Performance - data volume from sources
is key
• Source capabilities
• Source statistics
49

53. Performance concerns:
data volume
SELECT ...
FILTER (?age >= 24) ...
Reduction factors:
•criteria Domain
•minimal projection
Results
Query
•aggregation
•joins (sometimes)
•dup removal
WHERE Person.age >= 24
50

54. Performance concerns:
federated joins
51

55. Data source capabilities
• SQL support
• Function support
• Function translation
• Inverse functions
• Data type mappings and translations
52

56. Data source statistics
• Table cardinality
• Column selectivity
• Column null density
• Join selectivity
53

57. Semantic web
Relational data
Federation
HREIW
Analytics
54

58. HREIW - HR Analysis
55

59. “Which Marines that speak French and/
or French Creole have had at least six
months since their last deployment?”
56

60. “How many discharges were the
result of the Don’t Ask Don’t Tell
policy per year?”
57

61. “What is the average length of service for
soldiers deployed in Afghanistan vs Iraq?”
58

62. Where’s the data?
59

63. Ontologies
HR Standards
HR Domain
Mapping
Sources
60

64. Technologies
cs
Analyti
y
HR Standards
Ontolog
ment
develo p
HR Domain
SPARQL
Federation
Mapping
Rule
s
Sources
SPARQL to
database
61

65. Collaborative Ontologies
model
Domain
ontology
wiki discuss
Ontologist Subject Matter
Experts
diagram
discuss
62

66. Ontology Visualization
63

67. Semantic web
Relational data
Federation
HREIW
Analytics
64

68. RIF
• Rule Interchange Format, W3C recommendation
• Rule = IF - THEN statement
• Used to derive new triples from existing triples
• Dialects
• Core
• Framework for Logic Dialects (FLD)
• Basic Logic Dialect (BLD)
• Production Rules Dialect (PRD)
• Rex - Revelytix RIF Core implementation
65

69. Dashboards
66

70. Dashboards
67

71. Dashboards
68

72. Enterprise Semantic Web
• Knoodl - collaborative ontology creation
• OntVis - ontology visualization (OWL)
• Spyder - SPARQL to SQL (RDF, R2RML)
• Federator - SPARQL federation (SPARQL 1.1,
SPARQL Federation extensions)
• Rex - entailment with rules (RIF)
• Dashboards - analytics, visualization
69

73. More information
• Revelytix - http://revelytix.com
• Knoodl - http://knoodl.com
• OntVis - http://bit.ly/hLm3sd
• Spyder - http://revelytix.com/content/spyder
• Federator - beta coming soon...
• Rex - beta coming soon...
70