ESWC2015 presentation

1. Gagg: A Graph Aggregation Operator
June 2nd 2015
Fadi Maali*, Stephane Campinas, Stefan Decker
ESWC2015
* Funded by the Irish Research Council

2. The Famous LOD Cloud
1/20
http://lod-cloud.net/

3. The Famous LOD Cloud - COLOURED
2/20
http://lod-cloud.net/

4. The Famous LOD Cloud
(from a different Angel)
3/20

5. The Famous LOD Cloud
(from a different Angel)
4/20

6. The Famous LOD Cloud
(from a different Angel)
5/20

7. The Famous LOD Cloud
(from a different Angel)
6/20

8. Graph Aggregation
Condenses a large graph into a structurally
similar but smaller graph by collapsing vertices
and edges

9. Graph Aggregation - Schema Discovery
8/20
Introducing RDF Graph Summary with application to Assisted SPARQL Formulation

10. Graph Aggregation - Requirements
9/20
:linkset
:dbpedia :bbc-music
:crossdomain
23k
1.2b
20m
triples
triples
triples
:open-license
:media
:cc-by-sa
:closed-license
:bbc-terms
subject
subject
licenselicense
subjectsTarget objectsTarget

11. Graph Aggregation Methods
1. Custom Code
error prone, time, efficiency…
2. SPARQL
error prone, time, efficiency…
3. Graph Databases
expressivity, optimisation…
4. Gagg, a first-class operator

12. Graph Aggregation Methods
1. Custom Code
error prone, time, efficiency…
2. SPARQL
error prone, time, efficiency…
3. Graph Databases
expressivity, optimisation…
4. Gagg, a first-class operator
Operational Semantics
In-memory evaluation algorithm
Experimental evaluation

13. Gagg: Two-steps Aggregation
11/20

14. Gagg: Two-steps Aggregation
11/20
● Relation & measure
● Subject dimension(s)
& measure
● Object dimension(s) &
measure
Uses aggregation
functions and a template
similar to CONSTRUCT
queries

15. Graph Aggregation - Requirements
12/20
:linkset
:dbpedia :bbc-music
:crossdomain
23k
1.2b
20m
triples
triples
triples
:open-license
:media
:cc-by-sa
:closed-license
:bbc-terms
subject
subject
licenselicense
subjectsTarget objectsTarget
measure
relation

16. Graph Aggregation - Requirements
12/20
:linkset
:dbpedia :bbc-music
:crossdomain
23k
1.2b
20m
triples
triples
triples
:open-license
:media
:cc-by-sa
:closed-license
:bbc-terms
subject
subject
licenselicense
subjectsTarget objectsTarget
measure
relation
?l a void:LinkSet ;
void:subjectsTarget ?s ;
void:objectsTarget ?o ;
void:triples ?m .

17. Graph Aggregation - Requirements
13/20
:linkset
:dbpedia :bbc-music
:crossdomain
23k
1.2b
20m
triples
triples
triples
:open-license
:media
:cc-by-sa
:closed-license
:bbc-terms
subject
subject
licenselicense
subjectsTarget objectsTarget

18. Graph Aggregation - Requirements
13/20
:linkset
:dbpedia :bbc-music
:crossdomain
23k
1.2b
20m
triples
triples
triples
:open-license
:media
:cc-by-sa
:closed-license
:bbc-terms
subject
subject
licenselicense
subjectsTarget objectsTarget
?s dct:subject ?sd ;
void:triple ?sm .

19. Graph Aggregation - Requirements
14/20
:linkset
:dbpedia :bbc-music
:crossdomain
23k
1.2b
20m
triples
triples
triples
:open-license
:media
:cc-by-sa
:closed-license
:bbc-terms
subject
subject
licenselicense
subjectsTarget objectsTarget

20. Graph Aggregation - Requirements
14/20
:linkset
:dbpedia :bbc-music
:crossdomain
23k
1.2b
20m
triples
triples
triples
:open-license
:media
:cc-by-sa
:closed-license
:bbc-terms
subject
subject
licenselicense
subjectsTarget objectsTarget
?o dct:subject ?od ;
void:triple ?om .

21. Graph Aggregation - Definition
15/20
Q=(D,M,E,N,R,f)
D: subject dimensions
M: subject measure
E: object dimensions
N: object measure
R: relation query
f: reduce function
?x ?sd
?x ?sm
?y ?od
?y ?om
?x ?m?p ?y

22. Graph Aggregation - Grouped Graph
16/20
crossdomain media
10k 33k 3k 1.2M ......
linksTo

23. Graph Aggregation - Evaluation
17/20
● Build a binding table
● Build the Grouped Graph
O(|B|) algorithm where B is the size of the binding
table
● Apply the reduction function
?x ?m?p ?y ?sd ?sm ?od ?om

24. Graph Aggregation - Experiment Setup
18/20
● Extended In-memory Apache Jena
using SSE
● BSBM and SP2B
● Type Summary and Bibliometrics

25. Graph Aggregation - Experiment Results
19/20
Data size
(#triples)
fullSPARQL 3SPARQLs reduced Gagg
5k 0.08 0.06 0.01 0.03
190k 9.84 1.25 0.42 0.55
370k 31.88 2.82 1.00 1.13
1.8M 454.07 13.48 4.37 5.61
Type Summary on BSBM data

26. Graph aggregation as a first-class operator
- Easier for users to express
- Easier for engines to support and optimise
- Easier for further research and study
Further Questions:
- Syntax and effect on SPARQL
- Distributed implementation
Conclusion
20/20

27. PREFIX : <http://example.org/>
PREFIX rdf:<http://www.w3.org/1999/02/22-rdf-syntax-
ns#>
CONSTRUCT {
_:b0 a ?t1; :count COUNT(?sub.s) .
_:b1 a ?t2; :count COUNT(?obj.o).
_:b2 a rdf:Statement; rdf:predicate ?p; rdf:subject
_:b0; rdf:object _:b1; :count ?prop_count
} WHERE {
GRAPH_AGGREGATION {
?s ?p ?o
{?s a ?t1} GROUP BY ?t1 AS ?sub
{?o a ?t2} GROUP BY ?t2 AS ?obj
}
}

28. SELECT ?t1 ?count_s ?subId ?t2 ?count_o ?objId ?p (COUNT(*) AS
?rel_count){
{
SELECT ?t1 ?count_s ?subId ?t2 ?count_o ?objId ?p {
?s a ?t1 . ?s ?p ?o . ?o a ?t2 .
{
SELECT ?t1 ?subId (COUNT(DISTINCT ?s) AS ?count_s){
?s a ?t1 . ?s ?p ?o .?o a ?t2 .
BIND (iri(CONCAT(str(?t1), "_s")) AS ?subId)
} GROUP BY ?t1 ?subId
}
{
SELECT ?t2 ?objId (COUNT(DISTINCT ?t2) AS ?count_o){
?s a ?t1 .
?s ?p ?o .
?o a ?t2 .
BIND (iri(CONCAT(str(?t2), "_o")) AS ?objId)
} GROUP BY ?t2 ?objId
}
}
}
} GROUP BY ?t1 ?count_s ?t2 ?count_o ?subId ?objId
}