Graphs aphs Workshop on Linked Data on the Web (LDOW 2013) - - PowerPoint PPT Presentation
14 Mai 2013 Similar milar Str truc uctures tures ins nside ide RDF- Graphs aphs Workshop on Linked Data on the Web (LDOW 2013) Collocated with the 22nd International World Wide Web Conference (WWW 2013) Anas s Alzogbi bi Georg rg
Workshop on Linked Data on the Web (LDOW 2013) Collocated with the 22nd International World Wide Web Conference (WWW 2013)
Anas s Alzogbi bi Georg rg Lausen University of Freiburg Databases & Information Systems
14 Mai 2013
RDF datasets are growing constantly (e.g. LOD) Minimum Constraints for RDF data make it
Idea
Similar Structures inside RDF-Graphs 2
Two phases approach
3
RDF Graph
Non-Literal Entities
Perfect Typing
Bisimilarity Equivalence
Similarity based reduction
Complete link agglomerative clustering
PTG
reduced RDF Graph
Similar Structures inside RDF-Graphs
4
𝑤 = 𝑄 𝑣
𝑤4 𝑤5 𝑤3 𝑤6 a b b a c c d d e e g h i
𝑄
𝑤2 = 𝑄 𝑤6 = 𝑗 ⇒ 𝑄 𝑤2 ≈𝐶 𝑄 𝑤6
𝑄
𝑤5 = 𝑄 𝑤3 = { 𝑏 , 𝑐, 𝑗 , 𝑑 , 𝑒, ℎ , 𝑒, , 𝑓 }
⇒ 𝑄
𝑤3 ≈𝐶 𝑄 𝑤5
Similar Structures inside RDF-Graphs
𝑤2 i
Hierarchical clustering
Instance tree & intersection tree [Lösch et al. 2012]
𝑈
𝜏(𝑤) is the instance tree of node 𝑤
5
𝑤1 𝑤3 𝑤4 𝑤2 a a b b c c d d e e f g h i PTG 𝑤1 𝑤3 𝑤4 a b c d e f g h i 𝑈
𝜏(𝑤1)
Similar Structures inside RDF-Graphs
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𝑤1 𝑤3 𝑤4 a b c d e f g h i 𝑈
𝜏(𝑤1)
𝑤3 𝑤4 𝑤2 a b c d e g h i 𝑈
𝜏(𝑤2)
a b c d e g h i 𝑗𝑜𝑢𝑓𝑠𝑡𝑓𝑑𝑢 𝑈
𝜏 𝑤1 , 𝑈 𝜏 𝑤2
𝑡𝑗𝑨𝑓 𝑈
𝜏 𝑤1
= 9 𝑡𝑗𝑨𝑓 𝑈
𝜏 𝑤2
= 8 𝑡𝑗𝑨𝑓 𝑗𝑜𝑢𝑓𝑠𝑡𝑓𝑑𝑢 = 8 𝑡𝑗𝑛 𝑤1, 𝑤2 = 𝑡𝑗𝑨𝑓(𝑗𝑜𝑢𝑓𝑠𝑡𝑓𝑑𝑢 𝑈
𝜏 𝑤1 , 𝑈 𝜏 𝑤2
) (𝑡𝑗𝑨𝑓 𝑈
𝜏 𝑤1
+ 𝑡𝑗𝑨𝑓 𝑈
𝜏 𝑤2
) 2 = 8 8,5 = 0,94
Similar Structures inside RDF-Graphs
Instance tree & intersection tree Pairwise similarity
Similar Structures inside RDF-Graphs 7
agglomerative algorithm for complete-link
x1 x4 x5 x2 x3 x1 x4 x5 x2 x3
G(0.9)={{x1}, {x2, x3}, {x4},{x5}} G(0.8) = {{x1, x4},{x2, x3}, {x5}} G(0.3) = {{x1, x4, x5},{x2, x3}} G(0) = {{x1, x4, x5,x2, x3}} G(∞)={{x1},{ x2},{x3}, {x4}, {x5}} Dendrogram Threshold graph
Similar Structures inside RDF-Graphs 8
List of partitions Which partition is appropriate?
𝐽𝑜𝑢𝑠𝑏𝑇𝑗𝑛𝒬𝜐 =
1 |𝒬𝜐|
𝐽𝑜𝑢𝑠𝑏𝑇𝑗𝑛𝑑
𝑑∈𝒬𝜐
𝐽𝑜𝑢𝑠𝑏𝑇𝑗𝑛𝑑 =
1 𝜇
𝑇[𝑑𝑗, 𝑑
𝑘] 𝑜 𝑗<𝑘
, where: 𝜇 =
𝑜(𝑜−1) 2
, 𝑜: the number of elements in 𝑑 G(0.9)={{x1}, {x2, x3}, {x4},{x5}} G(0.8) = {{x1, x4},{x2, x3}, {x5}} G(0.3) = {{x1, x4, x5},{x2, x3}} G(0) = {{x1, x4, x5, x2, x3}} G(∞)={{x1},{x2},{x3}, {x4}, {x5}}
9 Similar Structures inside RDF-Graphs
Data set Subjects Objects Predicates Edges SP2Bench250K 50K 100K 61 250K LUBM2 40K 20K 32 240K BSBM500K 48K 100K 40 500K SwDogFood 25K 55K 170 290K
Experimental Results
1. IntraSim & Similarity value
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Experimental Results
1. IntraSim & Partition size
Similar Structures inside RDF-Graphs 11
Experimental Results
2 universities appeared with 3728 courses
21 ResearchTopics appeared with 36 SpatialThings
Similar Structures inside RDF-Graphs 12
Data set Subjects RDF types Clusters errors SP2Bench250K 50K 9 85 LUBM2 40K 14 6 2 BSBM500K 48K 9 7 SwDogFood 25K 43 1918 22
Similar Structures inside RDF-Graphs 13
Experimental Results
22K typed subjects 43 different types
𝑂𝑣𝑛𝑐𝑓𝑠 𝑝𝑔 𝐷𝑚𝑣𝑡𝑢𝑓𝑠𝑡 . 104
Partition
𝒬64%
#Clusters
1918
#Clusters with Types
1795
Multi Types Clusters
83
#Errors
22
Error Ratio
0, 09%
𝒬50%
424 413 58 133 0, 6%
𝒬
45% 287 280 51 209 0, 94%
𝒬
40% 196 191 46 209 0, 94%
𝒬35%
119 116 33 209 0, 94%
𝒬23%
25 25 17 251 1,26%
𝒬30%
70 68 23 210 0,95%
Concl
Future
Consider only important edges
14
[http://www.superscholar.org]
Similar Structures inside RDF-Graphs
15
Similar Structures inside RDF-Graphs
[Lösch et al. 2012]
Similar Structures inside RDF-Graphs 16
Similar Structures inside RDF-Graphs 17
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Similar Structures inside RDF-Graphs 19