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Motivation Approach Evaluation Conclusion and Future Work
DEERAutomating RDF Dataset Transformation and Enrichment
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo andJens Lehmann
June 3, 2015
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 1/26
Motivation Approach Evaluation Conclusion and Future Work
Outline
1 Motivation
2 Approach
3 Evaluation
4 Conclusion and Future Work
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 2/26
Motivation Approach Evaluation Conclusion and Future Work
Outline
1 Motivation
2 Approach
3 Evaluation
4 Conclusion and Future Work
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 3/26
Motivation Approach Evaluation Conclusion and Future Work
Why RDF Transformation & Enrichment?
Dataset DrugBank
Goal Gather information about companies related to drugs fora market study
:Aspirin
:Paracetamol
:Ibuprofen
:Quinine
:Druga
a
aa
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 4/26
Motivation Approach Evaluation Conclusion and Future Work
Why RDF Transformation & Enrichment?
Dataset DrugBank
Goal Gather information about companies related to drugs fora market study
:Aspirin
:Paracetamol
:Ibuprofen
:Quinine
db:Ibuprofen
db:Aspirin
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Druga
a
aa
owl:sameAs
owl:sameAs
rdfs:comment
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 4/26
Motivation Approach Evaluation Conclusion and Future Work
RDF Transformation & Enrichment
Need for enriched datasets
TourismQuestion AnsweringEnhanced Reality...
RDF transformation and enrichment
Triples to be added to the originalKB and/orTriples to be deleted from theoriginal KB
:Aspirin
:Paracetamol
:Ibuprofen
:Quinine
:Druga
a
aa
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 5/26
Motivation Approach Evaluation Conclusion and Future Work
Manual Knowledge Base Enrichment
Demands for the specification of data enrichmentpipelines
Describe how data is to be integrated (usually manually)
Manual customized enrichment pipelines
⊕ Leads to the expected results
Time consuming
Cannot be ported easily to other datasets
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 6/26
Motivation Approach Evaluation Conclusion and Future Work
Manual Knowledge Base Enrichment
Demands for the specification of data enrichmentpipelines
Describe how data is to be integrated (usually manually)
Manual customized enrichment pipelines
⊕ Leads to the expected results
Time consuming
Cannot be ported easily to other datasets
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 6/26
Motivation Approach Evaluation Conclusion and Future Work
Automatic Knowledge Base Enrichment
Enrichment pipeline M : K → K that maps KB K to anenriched KB K ′ with K ′ = M(K ).
M is an ordered list of atomic enrichment functionsm ∈M
M =
{φ if K = K ′,
(m1, . . . ,mn),where mi ∈M, 1 ≤ i ≤ n otherwise.
Research questions
1 How to create self-configuring atomic enrichmentfunctions m ∈M?
2 How to automatically generate an enrichment pipeline M?
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 7/26
Motivation Approach Evaluation Conclusion and Future Work
Automatic Knowledge Base Enrichment
Enrichment pipeline M : K → K that maps KB K to anenriched KB K ′ with K ′ = M(K ).
M is an ordered list of atomic enrichment functionsm ∈M
M =
{φ if K = K ′,
(m1, . . . ,mn),where mi ∈M, 1 ≤ i ≤ n otherwise.
Research questions
1 How to create self-configuring atomic enrichmentfunctions m ∈M?
2 How to automatically generate an enrichment pipeline M?
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 7/26
Motivation Approach Evaluation Conclusion and Future Work
Outline
1 Motivation
2 Approach
3 Evaluation
4 Conclusion and Future Work
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 8/26
Motivation Approach Evaluation Conclusion and Future Work
Atomic Enrichment FunctionsI. Dereferencing atomic enrichment function
Datasets are linked (e.g., using owl:sameAs)
Deferences pre-specified set of predicates
Adds found predicates to source the dataset
:Aspirin
:Paracetamol
:Ibuprofen
:Quinine
:Druga
a
aa
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 9/26
Motivation Approach Evaluation Conclusion and Future Work
Atomic Enrichment FunctionsI. Dereferencing atomic enrichment function
Datasets are linked (e.g., using owl:sameAs)
Deferences pre-specified set of predicates
Adds found predicates to source the dataset
:Aspirin
:Paracetamol
:Ibuprofen
:Quinine
db:Ibuprofen
db:Aspirin
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Druga
a
aa
owl:sameAs
owl:sameAs
rdfs:comment
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 9/26
Motivation Approach Evaluation Conclusion and Future Work
Atomic Enrichment FunctionsI. Dereferencing atomic enrichment function
Datasets are linked (e.g., using owl:sameAs)
Deferences pre-specified set of predicates
Adds found predicates to source the dataset
:Aspirin
:Paracetamol
:Ibuprofen
:Quinine
db:Ibuprofen
db:Aspirin
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Druga
a
aa
owl:sameAs
owl:sameAs
rdfs:commentrdfs:comment
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 9/26
Motivation Approach Evaluation Conclusion and Future Work
Atomic Enrichment FunctionsI. Dereferencing atomic enrichment function
Datasets are linked (e.g., using owl:sameAs)
Deferences pre-specified set of predicates
Adds found predicates to source the dataset
:Aspirin
:Paracetamol
:Ibuprofen
:Quinine
db:Ibuprofen
db:Aspirin
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Druga
a
aa
owl:sameAs
owl:sameAs
rdfs:commentrdfs:comment
rdfs:comment
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 9/26
Motivation Approach Evaluation Conclusion and Future Work
Self-ConfigurationI. Dereferencing Enrichment Functions
Finds the set of predicates Dp from the enriched CBDsthat are missing from source CBDs
Non-enriched CBD of Ibuprofen
:Ibuprofendb:Ibuprofen :Drugaowl:sameAs
Enriched CBD of Ibuprofen
:Ibuprofendb:Ibuprofen
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Drug
:BootsCompany
a
:relatedCompany
owl:sameAs
rdfs:comment
Dp = {:relatedCompany, rdfs:comment}
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 10/26
Motivation Approach Evaluation Conclusion and Future Work
Self-ConfigurationI. Dereferencing Enrichment Functions
Finds the set of predicates Dp from the enriched CBDsthat are missing from source CBDs
Non-enriched CBD of Ibuprofen
:Ibuprofendb:Ibuprofen :Drugaowl:sameAs
Enriched CBD of Ibuprofen
:Ibuprofendb:Ibuprofen
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Drug
:BootsCompany
a
:relatedCompany
owl:sameAs
rdfs:comment
Dp = {:relatedCompany, rdfs:comment}
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 10/26
Motivation Approach Evaluation Conclusion and Future Work
Self-ConfigurationI. Dereferencing Enrichment Functions
Finds the set of predicates Dp from the enriched CBDsthat are missing from source CBDs
Non-enriched CBD of Ibuprofen
:Ibuprofendb:Ibuprofen :Drugaowl:sameAs
Enriched CBD of Ibuprofen
:Ibuprofendb:Ibuprofen
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Drug
:BootsCompany
a
:relatedCompany
owl:sameAs
rdfs:comment
Dp = {:relatedCompany, rdfs:comment}
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 10/26
Motivation Approach Evaluation Conclusion and Future Work
Self-ConfigurationI. Dereferencing Enrichment Functions
Finds the set of predicates Dp from the enriched CBDsthat are missing from source CBDs
Non-enriched CBD of Ibuprofen
:Ibuprofendb:Ibuprofen :Drugaowl:sameAs
Enriched CBD of Ibuprofen
:Ibuprofendb:Ibuprofen
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Drug
:BootsCompany
a
:relatedCompany
owl:sameAs
rdfs:comment
Dp = {:relatedCompany, rdfs:comment}
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 10/26
Motivation Approach Evaluation Conclusion and Future Work
Self-ConfigurationI. Dereferencing Enrichment Functions
Dereferences Dp = {:relatedCompany, rdfs:comment}
CBD of Ibuprofen
:Aspirin
:Paracetamol
:Ibuprofen
:Quinine
db:Ibuprofen
db:Aspirin
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Druga
a
aa
owl:sameAs
owl:sameAs
rdfs:comment
Finds only rdfs:comment, adds it to the source dataset
Dereferencing enriched CBD of Ibuprofen
:Ibuprofendb:Ibuprofen
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Drugaowl:sameAs
rdfs:comment
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 11/26
Motivation Approach Evaluation Conclusion and Future Work
Self-ConfigurationI. Dereferencing Enrichment Functions
Dereferences Dp = {:relatedCompany, rdfs:comment}
CBD of Ibuprofen
:Aspirin
:Paracetamol
:Ibuprofen
:Quinine
db:Ibuprofen
db:Aspirin
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Druga
a
aa
owl:sameAs
owl:sameAs
rdfs:comment
Finds only rdfs:comment, adds it to the source dataset
Dereferencing enriched CBD of Ibuprofen
:Ibuprofendb:Ibuprofen
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Drugaowl:sameAs
rdfs:comment
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 11/26
Motivation Approach Evaluation Conclusion and Future Work
Self-ConfigurationI. Dereferencing Enrichment Functions
Dereferences Dp = {:relatedCompany, rdfs:comment}
CBD of Ibuprofen
:Aspirin
:Paracetamol
:Ibuprofen
:Quinine
db:Ibuprofen
db:Aspirin
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Druga
a
aa
owl:sameAs
owl:sameAs
rdfs:comment
Finds only rdfs:comment, adds it to the source dataset
Dereferencing enriched CBD of Ibuprofen
:Ibuprofendb:Ibuprofen
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Drugaowl:sameAs
rdfs:comment
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 11/26
Motivation Approach Evaluation Conclusion and Future Work
Self-ConfigurationI. Dereferencing Enrichment Functions
Dereferences Dp = {:relatedCompany, rdfs:comment}
CBD of Ibuprofen
:Aspirin
:Paracetamol
:Ibuprofen
:Quinine
db:Ibuprofen
db:Aspirin
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Druga
a
aa
owl:sameAs
owl:sameAs
rdfs:comment
Finds only rdfs:comment, adds it to the source dataset
Dereferencing enriched CBD of Ibuprofen
:Ibuprofendb:Ibuprofen
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Drugaowl:sameAs
rdfs:comment
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 11/26
Motivation Approach Evaluation Conclusion and Future Work
Atomic Enrichment FunctionsII. NLP atomic enrichment function
Datatype objects contain unstructured information
Uses Named Entity Recognition to extract implicit data
Adds extracted entities to the source datasets
:Ibuprofendb:Ibuprofen
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Drugaowl:sameAs
rdfs:comment
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 12/26
Motivation Approach Evaluation Conclusion and Future Work
Atomic Enrichment FunctionsII. NLP atomic enrichment function
Datatype objects contain unstructured information
Uses Named Entity Recognition to extract implicit data
Adds extracted entities to the source datasets
:Ibuprofendb:Ibuprofen
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Drugaowl:sameAs
rdfs:comment
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 12/26
Motivation Approach Evaluation Conclusion and Future Work
Atomic Enrichment FunctionsII. NLP atomic enrichment function
Datatype objects contain unstructured information
Uses Named Entity Recognition to extract implicit data
Adds extracted entities to the source datasets
:Ibuprofendb:Ibuprofen
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Drugaowl:sameAs
rdfs:comment
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 12/26
Motivation Approach Evaluation Conclusion and Future Work
Atomic Enrichment FunctionsII. NLP atomic enrichment function
Datatype objects contain unstructured information
Uses Named Entity Recognition to extract implicit data
Adds extracted entities to the source datasets
:Ibuprofendb:Ibuprofen
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Drug
:BootsCompany
a
fox:relatedTo
owl:sameAs
rdfs:comment
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 12/26
Motivation Approach Evaluation Conclusion and Future Work
Self-ConfigurationII. NLP Enrichment Function
Extracts all possible named entity types
Adds extracted entities to the source dataset
NLP enriched CBD of Ibuprofen
:Ibuprofendb:Ibuprofen
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Drugaowl:sameAs
rdfs:comment
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 13/26
Motivation Approach Evaluation Conclusion and Future Work
Self-ConfigurationII. NLP Enrichment Function
Extracts all possible named entity types
Adds extracted entities to the source dataset
NLP enriched CBD of Ibuprofen
:Ibuprofendb:Ibuprofen
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Drugaowl:sameAs
rdfs:comment
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 13/26
Motivation Approach Evaluation Conclusion and Future Work
Self-ConfigurationII. NLP Enrichment Function
Extracts all possible named entity types
Adds extracted entities to the source dataset
NLP enriched CBD of Ibuprofen
:Ibuprofendb:Ibuprofen
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Drug
:BootsCompany
a
fox:relatedTo
owl:sameAs
rdfs:comment
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 13/26
Motivation Approach Evaluation Conclusion and Future Work
Atomic Enrichment FunctionsIII. Predicate conformation atomic enrichment function
Enriched datasets may contain diverse ontologies
Predicate conformation maps a set of a pre-specifiedpredicates to a target ontology
:Ibuprofendb:Ibuprofen
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Drug
:BootsCompany
a
fox:relatedTo
owl:sameAs
rdfs:comment
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 14/26
Motivation Approach Evaluation Conclusion and Future Work
Atomic Enrichment FunctionsIII. Predicate conformation atomic enrichment function
Enriched datasets may contain diverse ontologies
Predicate conformation maps a set of a pre-specifiedpredicates to a target ontology
:Ibuprofendb:Ibuprofen
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Drug
:BootsCompany
a
fox:relatedTo
owl:sameAs
rdfs:comment
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 14/26
Motivation Approach Evaluation Conclusion and Future Work
Atomic Enrichment FunctionsIII. Predicate conformation atomic enrichment function
Enriched datasets may contain diverse ontologies
Predicate conformation maps a set of a pre-specifiedpredicates to a target ontology
:Ibuprofendb:Ibuprofen
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Drug
:BootsCompany
a
fox:relatedTo:relatedCompany
owl:sameAs
rdfs:comment
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 14/26
Motivation Approach Evaluation Conclusion and Future Work
Self-ConfigurationIII. Predicate conformation Enrichment Function
Finds list of predicates Ps and Pt from the source resp.target datasets with the same subject and objectsChanges each Ps with its respective Pt
NLP enriched CBD of Ibuprofen
:Ibuprofendb:Ibuprofen
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Drug
:BootsCompany
a
fox:relatedTo
owl:sameAs
rdfs:comment
Enriched CBD of Ibuprofen (positive example target)
:Ibuprofendb:Ibuprofen
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Drug
:BootsCompany
a
:relatedCompany
owl:sameAs
rdfs:comment
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 15/26
Motivation Approach Evaluation Conclusion and Future Work
Self-ConfigurationIII. Predicate conformation Enrichment Function
Finds list of predicates Ps and Pt from the source resp.target datasets with the same subject and objectsChanges each Ps with its respective Pt
NLP enriched CBD of Ibuprofen
:Ibuprofendb:Ibuprofen
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Drug
:BootsCompany
a
fox:relatedTo
owl:sameAs
rdfs:comment
Enriched CBD of Ibuprofen (positive example target)
:Ibuprofendb:Ibuprofen
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Drug
:BootsCompany
a
:relatedCompany
owl:sameAs
rdfs:comment
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 15/26
Motivation Approach Evaluation Conclusion and Future Work
Self-ConfigurationIII. Predicate conformation Enrichment Function
Finds list of predicates Ps and Pt from the source resp.target datasets with the same subject and objectsChanges each Ps with its respective Pt
NLP enriched CBD of Ibuprofen
:Ibuprofendb:Ibuprofen
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Drug
:BootsCompany
a
fox:relatedTo:relatedCompany
owl:sameAs
rdfs:comment
Enriched CBD of Ibuprofen (positive example target)
:Ibuprofendb:Ibuprofen
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Drug
:BootsCompany
a
:relatedCompany
owl:sameAs
rdfs:comment
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 15/26
Motivation Approach Evaluation Conclusion and Future Work
KB Enrichment Refinement Operator
Input
Set of atomic enrichment functions MSet of positive examples E
Refinement Operator
ρ(M) =⋃
∀m∈MM ++ m ( ++ is the list append operator)
Output
Enrichment pipeline M
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 16/26
Motivation Approach Evaluation Conclusion and Future Work
KB Enrichment Refinement Operator
Input
Set of atomic enrichment functions MSet of positive examples E
Refinement Operator
ρ(M) =⋃
∀m∈MM ++ m ( ++ is the list append operator)
Output
Enrichment pipeline M
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 16/26
Motivation Approach Evaluation Conclusion and Future Work
KB Enrichment Refinement Operator
Input
Set of atomic enrichment functions MSet of positive examples E
Refinement Operator
ρ(M) =⋃
∀m∈MM ++ m ( ++ is the list append operator)
Output
Enrichment pipeline M
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 16/26
Motivation Approach Evaluation Conclusion and Future Work
Positive Example
:Ibuprofendb:Ibuprofen :Drugaowl:sameAs
Non-enriched CBD of Ibuprofen
:Ibuprofendb:Ibuprofen
Ibuprofen was extracted by the research armof Boots company during the 1960s ...
:Drug
:BootsCompany
a
:relatedCompany
owl:sameAs
rdfs:comment
Enriched CBD of Ibuprofen
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 17/26
Motivation Approach Evaluation Conclusion and Future Work
Learning Algorithm
1 Start by empty enrichment pipeline M = ⊥2 Self-configure all mi ∈M, add as child to ⊥3 Select most promising node
4 Expand most promising node
⊥
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 18/26
Motivation Approach Evaluation Conclusion and Future Work
Learning Algorithm
1 Start by empty enrichment pipeline M = ⊥2 Self-configure all mi ∈M, add as child to ⊥3 Select most promising node
4 Expand most promising node
⊥
(m1) (m2) (m3)
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 18/26
Motivation Approach Evaluation Conclusion and Future Work
Learning Algorithm
1 Start by empty enrichment pipeline M = ⊥2 Self-configure all mi ∈M, add as child to ⊥3 Select most promising node
4 Expand most promising node
⊥
(m1) (m2) (m3)
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 18/26
Motivation Approach Evaluation Conclusion and Future Work
Learning Algorithm
1 Start by empty enrichment pipeline M = ⊥2 Self-configure all mi ∈M, add as child to ⊥3 Select most promising node
4 Expand most promising node
⊥
(m1) (m2) (m3)
(m1,m2) (m1,m3)
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 18/26
Motivation Approach Evaluation Conclusion and Future Work
Learning Algorithm
1 Start by empty enrichment pipeline M = ⊥2 Self-configure all mi ∈M, add as child to ⊥3 Select most promising node
4 Expand most promising node
⊥
(m1) (m2) (m3)
(m1,m2) (m1,m3)
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 18/26
Motivation Approach Evaluation Conclusion and Future Work
Learning Algorithm
1 Start by empty enrichment pipeline M = ⊥2 Self-configure all mi ∈M, add as child to ⊥3 Select most promising node
4 Expand most promising node
⊥
(m1) (m2) (m3)
(m1,m2) (m1,m3) (m3,m1) (m3,m2)
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 18/26
Motivation Approach Evaluation Conclusion and Future Work
Learning Algorithm
1 Start by empty enrichment pipeline M = ⊥2 Self-configure all mi ∈M, add as child to ⊥3 Select most promising node
4 Expand most promising node
⊥
(m1) (m2) (m3)
(m1,m2) (m1,m3) (m3,m1) (m3,m2)
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 18/26
Motivation Approach Evaluation Conclusion and Future Work
Learning Algorithm
1 Start by empty enrichment pipeline M = ⊥2 Self-configure all mi ∈M, add as child to ⊥3 Select most promising node
4 Expand most promising node
⊥
(m1) (m2) (m3)
(m1,m2) (m1,m3) (m3,m1) (m3,m2)
(m3,m2,m1)
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 18/26
Motivation Approach Evaluation Conclusion and Future Work
Most Promising Node Selection
Node complexity c(n)
Linear combination of the node’s children count and level
Node fitness f (n)
Difference between node’s enrichment pipeline F-measureand weighted complexity, f (n) = F (n)− ω.c(n)
ω controls the tradeoff between
Greedy search (ω = 0)Search strategies closer to breadth-first search (ω > 0).
Most promising node
The leaf node with the maximum fitness through thewhole refinement tree
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 19/26
Motivation Approach Evaluation Conclusion and Future Work
Most Promising Node Selection
Node complexity c(n)
Linear combination of the node’s children count and level
Node fitness f (n)
Difference between node’s enrichment pipeline F-measureand weighted complexity, f (n) = F (n)− ω.c(n)
ω controls the tradeoff between
Greedy search (ω = 0)Search strategies closer to breadth-first search (ω > 0).
Most promising node
The leaf node with the maximum fitness through thewhole refinement tree
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 19/26
Motivation Approach Evaluation Conclusion and Future Work
Most Promising Node Selection
Node complexity c(n)
Linear combination of the node’s children count and level
Node fitness f (n)
Difference between node’s enrichment pipeline F-measureand weighted complexity, f (n) = F (n)− ω.c(n)
ω controls the tradeoff between
Greedy search (ω = 0)Search strategies closer to breadth-first search (ω > 0).
Most promising node
The leaf node with the maximum fitness through thewhole refinement tree
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 19/26
Motivation Approach Evaluation Conclusion and Future Work
Outline
1 Motivation
2 Approach
3 Evaluation
4 Conclusion and Future Work
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 20/26
Motivation Approach Evaluation Conclusion and Future Work
Experimental Setup
Datasets
1 manual experimental enrichment pipelines for Jamendo
2 manual experimental enrichment pipelines for DrugBank
5 manual experimental enrichment pipelines for DBpedia(AdministrativeRegion)
Learning Algorithm
6 atomic enrichment functions
Termination criterion:
Maximum number of iterations of 10Optimal enrichment pipeline found (F-score = 1)
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 21/26
Motivation Approach Evaluation Conclusion and Future Work
Experimental Setup
Datasets
1 manual experimental enrichment pipelines for Jamendo
2 manual experimental enrichment pipelines for DrugBank
5 manual experimental enrichment pipelines for DBpedia(AdministrativeRegion)
Learning Algorithm
6 atomic enrichment functions
Termination criterion:
Maximum number of iterations of 10Optimal enrichment pipeline found (F-score = 1)
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 21/26
Motivation Approach Evaluation Conclusion and Future Work
Configuration of the Search Strategy
Node fitnessf (n) = F (n)− ω.c(n)
ω controls the tradeoff between
Greedy search (ω = 0)Search strategies closer tobreadth first search (ω > 0).
Result: ω = 0.75 leads to thebest results
ω P R F
0 1.0 0.99 0.990.25 1.0 0.99 0.990.50 1.0 0.99 0.990.75 1.0 1.0 1.01.0 1.0 0.99 0.99
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 22/26
Motivation Approach Evaluation Conclusion and Future Work
Effect of Positive Examples
Manual Examples Size of Time Size of Time Learn IterationsM count M M(KB) learned
M′M′(KB) Time count F -score
M1DBpedia
1 1 0.2 1 1.6 1.3 1 1.02 1 0.2 1 1.8 1.3 1 1.0
M2DBpedia
1 2 23.3 1 0.1 0.2 1 0.992 2 15 2 17 0.3 9 0.99
M3DBpedia
1 3 14.7 3 15.2 6.1 9 0.992 3 15 2 15.1 0.1 9 0.99
M4DBpedia
1 4 0.4 2 0.1 0.7 2 0.992 4 0.6 2 0.3 0.9 2 0.99
M5DBpedia
1 5 22 2 0.1 0.7 2 1.02 5 25.5 2 0.2 0.9 2 1.0
M1DrugBank
1 2 3.5 1 4.1 0.1 10 0.992 2 3.6 1 3.4 0.1 10 0.99
M2DrugBank
1 3 25.2 1 0.1 0.1 10 0.992 3 22.8 1 0.1 0.1 10 0.99
M1Jamendo
1 1 10.9 2 10.6 0.1 2 0.992 1 10.4 2 10.4 0.1 1 0.99
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 23/26
Motivation Approach Evaluation Conclusion and Future Work
Outline
1 Motivation
2 Approach
3 Evaluation
4 Conclusion and Future Work
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 24/26
Motivation Approach Evaluation Conclusion and Future Work
Conclusion and Future Work
Conclusion
Presented self-configuring atomic enrichment functions
Presented an approach for learning enrichment pipelinesbased on a refinement operator
Showed that our approach can easily reconstructmanually created enrichment pipelines
Future Work
Parallelize the algorithm on several CPUs as well as loadbalancing
Support directed acyclic graphs as enrichmentspecifications by allowing to split and merge datasets
Pro-active enrichment strategies and active learning
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 25/26
Motivation Approach Evaluation Conclusion and Future Work
Conclusion and Future Work
Conclusion
Presented self-configuring atomic enrichment functions
Presented an approach for learning enrichment pipelinesbased on a refinement operator
Showed that our approach can easily reconstructmanually created enrichment pipelines
Future Work
Parallelize the algorithm on several CPUs as well as loadbalancing
Support directed acyclic graphs as enrichmentspecifications by allowing to split and merge datasets
Pro-active enrichment strategies and active learning
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 25/26
Motivation Approach Evaluation Conclusion and Future Work
Thank You!
Questions?Mohamed Sherif
Augustusplatz 10D-04109 Leipzig
[email protected]://aksw.org/MohamedSherif
http://aksw.org/Projects/DEER
#akswgroup
Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann — DEER 26/26
