1. RDF graph summaries 金成
2014/11/3

2. Graph Summary
A graph summary captures the information that represents the original data graph.
Most of summaries are the substitution of the data graph with a homomorphic graph, which contains ideally less nodes and edges with regards to the data graph.
Each approach produces different graph summary.
[Picture from Campinas S, Perry T E, Ceccarelli D, et al. Introducing rdf graph summary with application to assisted sparql formulation[C]]

3. Pattern extraction

4. RDF graph sample db:player/1 eg:country “Argentina”;
eg:birthday “1987/6/24”;
rdf:type “player”
eg:friends “Beckham”
eg:teammate “neymar”
eg:instrestedIn “the roling stones”
db:player/2 eg:country “England”
eg:type “player”
db:player/3 eg:country ”Brazil”
eg:birthday”1992/2/5”
db:band/1 eg:manager:”bob”
eg:create “Far East Tour”
eg:create “Voodoo Lounge”
db:record/1 rdf type “Far East Tour”
Db:record/2 rdf:type “Voodoo Lounge”
Figure 3: RDF statements
Figure 2: A RDF graph

5. DFS frequent pattern extraction
DFS code: A 5-tuple i,j denote the discovery time of DFS search. The rest denote the mapping of elements to integer.
Figure 4 original graph
Figure 5: summary pattern
properties
country 1
birthday 2
friends 3
teammate 4
instrestedIn 5
manager 6
create 7
types of subjects or objects
player 8
band 9
record 10
literal
Table 1: mapping of classes and properties into integer

6. Clustering linked data sources
db:player/1 eg:country “Argentina”;
eg:birthday “1987/6/24”;
rdf:type “player”
eg:friends “Beckham”
eg:teammate “neymar”
eg:instrestedIn “the roling stones”
db:player/2 eg:country “England”
eg:type “player”
db:player/3 eg:country ”Brazil”
eg:birthday”1992/2/5”
db:band/1 eg:manager:”bob”
eg:create “Far East Tour”
eg:create “Voodoo Lounge”
db:record/1 rdf type “Far East Tour”
Db:record/2 rdf:type “Voodoo Lounge”
CD1{country,birthday,type,friends,teanmate,interestedIn}
CD2{coutry,type}
CD3{country,birthday,,type}
CD4{manager,create}
CD5{type}
CD6{type} [Pool of individuals]
CD1{country,birthday,type,friends,teanmate,interestedIn}
CD2{coutry,type}
CD3{country,birthday,,type}
CD4{manager,create}
CD5{type}
CD6{type}
C luster of label:player
CD1{country,birthday,type,friends,teanmate,interestedIn}
CD2{coutry,type}
CD3{country,birthday,,type}
Cluster of label;band
CD4{manager,create}
Cluster of label:record
CD5{type}
CD6{type} [Possible clusters]
Figure 6: summary processing

7. Latent topic extraction
Conceptual patterns:
Conceptual Motif Patterns(CM patterns):
Generate random graphs that contain all nodes of the original graph and accept only those that have a similar node degree distribution as the original graph.
Then, we use a t-Test to check the occurrence frequencies of patterns in the original against pattern frequencies in the accepted random graphs.
Mutual Information Patterns(MI patterns):
Count the strength of relationships between classes with an estimate of the mutual information
Percolating Patterns:
Combine the matches (conceptual patterns)
Figure 7 graph summary processing

8. ExpLOD: a SPARQL assistance tool
db:player/1 eg:country “Argentina”;
eg:birthday “1987/6/24”;
rdf:type “player”
eg:friends “Beckham”
eg:teammate “neymar”
eg:instrestedIn “the roling stones”
db:player/2 eg:country “England”
eg:type “player”
db:player/3 eg:country ”Brazil”
eg:birthday”1992/2/5”
db:band/1 eg:manager:”bob”
eg:create “Far East Tour”
eg:create “Voodoo Lounge”
db:record/1 rdf type “Far East Tour”
Db:record/2 rdf:type “Voodoo Lounge”
The RDF usage prefix :
’P’ for predicates;
’C’ for classes;
’I’ for instances;
’L’ for literals.
Figure 8: applying bisimulation labels to RDF
Figure 9: class usage summary
Figure 10:predicate usage summary

9. Add user-selected abilities
SNAP: grouping nodes based on user-selected attributes and relationships.
K-SNAP: on the basis of SNAP, user may control the size of clusters.
Figure 11: SNAP summary
user defined:{rdf:type}{interestedIn,create}
Figure 12: k-SNAP
different resolution(k)

10. A scalable approach Metadata extraction Resource sampling
Entity/topic extraction
Profile graphs
Profiles representation
[picture from Fetahu B, Dietze S, Nunes B P, et al. A scalable approach for efficiently generating structured dataset topic profiles[M]]

11. Extracting core knowledge
Figure 14 processing pipeline
Figure 15 corresponding RDF processing

12. Schema extraction

13. Schema construction What to extract? measures
The center that may cover or represent most of the information in the dataset
Individuals
Entities
Properties ……
measures
Individuals ranking
Tf-idf
LDA ……

14. Web schema construction
Table 3: web schema content and statics
[Tables from Ashraf J, Hadzic M. Web schema construction based on web ontology usage analysis[M]]
Table 2:list of ontologies found in a e-Commerce dataset

15. Visual summary: LODex Figure 16: LODex architecture
Pictures from Benedetti F, Bergamaschi S, Po L. A Visual Summary for Linked Open Data sources[J].
Figure 17: a visual sample

16. summarization Approach Year User-customized Application input output
DFS-based
2010 No
Represent dataset
A RDF graph
A graph
Clustering
2013
Data integration/ query formulation
RDF statements
Latent topic
2012
Topics mining
Multi-graph
topics
ExpLOD
Query assistance
A dataset
Two kinds of graph
User-control
2008
A little
Multi-level inquiry
Multi-resolution graphs
scalable
2014
Topic extraction
datasets
Central types or properties
Extracting core knowledge
2007
Path clusters
Schema construction
2011
Ontologies recognition
Ontologies usages
Visual summary
Exploring dataset
The URL of a SPARQL endpoint
Visual graph
Table 4 summarization for all approaches