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Presentation on Using Neural Networks in pruning Linked Data rules. 28/11/2014 @ EKAW 2014, Linköping, Sweden
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Using Neural Networks to aggregate Linked Data rules
Ilaria Tiddi, Mathieu d’Aquin, Enrico Motta
Knowledge Discovery patterns
• Useful, explicit information about some collected data.
• Problems
Quantity - too many
Quality - not interesting
raw data
clean data
Knowledge
PATTERNS
Preprocessing
Mining
Interpreting
Dedalo: interpreting patterns with Linked Data
• Given the patterns of a clustering process
• Find explanations for one cluster with information from Linked Data
KMi researchers grouped together according to their co-authorship
• Linked Data can help the non experts in understanding the pattern
Dedalo: interpreting patterns with Linked Data
“Researchers working on projects led by someone interested in Semantic Web”
Dedalo: the bottleneck
• Explanations are atomic.“People working with Enrico Motta” F1=66%
“People working interested in Semantic Web” F1=66%
“People working interested in Ontologies” F1=66%
• We want to aggregate them to improve the explanation of the cluster“People working with Enrico Motta OR interested in Semantic Web”
F1=93%
“People interested in Semantic Web AND Ontologies” F1=86%
Rule aggregation state of the art
• Pre-production of patterns
• Machine Learning techniques
• Artificial Neural Networks (ANN) for features reduction
• Patterns post-processing
• Interestingness measures (IR, Statistics…)
• Semantic knowledge (ontologies, taxonomies)
Proposition:
use ANNs for post-processing patterns(i.e. for aggregating Linked Data rules)
Using ANNs to combine rules
• We want to know if two rules r1 and r2 are worth combining
• There must be a relationship between features of two rules that can help in deciding their combination
• We know their F-score, hence Precision and Recall
…(r1)
…(r2)
…(r1)
UNION(r1,r2)
INTERSECTION(r1,r2)
Model training and testing
• Model configuration
• Model : Feedforward multilayer perceptron
• Neurons structure: 9 – 12 – 2
• Inputs : P(r1), P(r2), R(r1), R(r2), F1(r1), F1(r2), and their absolute
differences
• Training and test set
• 30,000 automatically labeled combinations (unions and intersections)
for training
• Boolean label if the F1 of the combination has increased
• 30,000 combinations for testing
• Error rate: 0.24 (MSE rate)
Predicting combinations
• A process to combine rules
• a large set of ranked rules
• the nnet learned model
• a prediction indicator p(r1,r2)= nnet(r1,r2)*max(f(r1),f(r2))
• Start from the top(H) rule
• predict p(top(H),ri) for each rule in H
• combine rules if p(top(H),ri) above a given threshold
• add the new rule
Experiments - datasets
• KMiA – authors clustered according to the papers written together
• KMiP – papers clustered according to the abstracts words
• Huds – students clustered according to the books they borrowed
Data Rules RAM Time (sec) Initial top(H) Ending top(H)
KMiA1 369 4G 60’’ 71.1% 86.3%
KMiA2 511 4G 60’’ 60.6% 63.9%
KMiP1 747 4G 75’’ 54.9% 63.9%
KMiP2 1746 4G 160’’ 30.6% 84.1%
Hud1 11,937 10G 2,500’’ 20.2% 66.9%
Hud2 11,151 10G 3,000’’ 13.3% 67.3%
Experiments - strategies
Compare the NNET process with other strategies
Random baseline combine a random rule with the top(H)
AllComb baseline combine everything in H with everything in H
Top100 naïve combine the first 100 rules in H only
First naïve always combine the top(H) with H
Delta combine all rules above a threshold
NNET combine any pair predicted
NNET50combine if prediction is higher than 50% ofthe highest score at the current iteration
Experiments - results
Huds1 example.
Random
AllComb
Top100
First
Delta
NNET
NNET500.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0 500 1000 1500 2000 2500
Experiments - results
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
0 10 20 30 40 50 600.7
0.72
0.74
0.76
0.78
0.8
0.82
0.84
0.86
0.88
0 10 20 30 40 50 60
KMiP1 example.
KMiP2 example. Huds2 example.
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 500 1000 1500 2000 2500 3000
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0 30 60 90 120 150
KMiA2 example.
0.55
0.57
0.59
0.61
0.63
0 10 20 30 40 50 60 70
KMiA1 example.
Experiments - performance
• Comparing performances
Speed Accuracy Scalability
Random ++ - ++
AllComb + ++ --
Top100 + -- +
First + -- --
Delta + -- --
NNET /NNET50
++ ++ ++
Conclusions and future work
• An approach to predict rule combination based on
Artificial Neural Networks
• Trained model on the information (Precision, Recall,
F-score) about the rules
• Save time and computational costs (vs. other)
• Evaluating Dedalo on Google Trends: why is a trend
popular according to Linked Data?
http://linkedu.eu/dedalo/eval/
ilaria.tiddi @open.ac.uk
@IlaTiddi
THANKS FOR YOUR ATTENTION
Questions?
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