DEER - RDF Data Extraction and Enrichment Framework

1. Motivation Approach Evaluation Conclusion and Future Work DEER RDF Data Extraction and Enrichment Framework Mohamed Ahmed Sherif September 15, 2015 Mohamed Ahmed Sherif — DEER 1/31

2. Motivation Approach Evaluation Conclusion and Future Work Outline 1 Motivation 2 Approach 3 Evaluation 4 Conclusion and Future Work Mohamed Ahmed Sherif — DEER 2/31

4. Motivation Approach Evaluation Conclusion and Future Work RDF Extraction & Enrichment Need for enriched datasets Tourism Question Answering Enhanced Reality ... RDF extraction and enrichment Triples to be added to the original KB and/or Triples to be deleted from the original KB Mohamed Ahmed Sherif — DEER 4/31

5. Motivation Approach Evaluation Conclusion and Future Work Example GeoSuPhat mo:MusicArtist ... artist name of George (Pete) Peterson. Born and raised near the shores of Lake Michigan and its cold windy winters he was Inﬂuenced by bands like Pink Floyd, Kraftwerk, Todd Rundgren, ELP, Hawkwind and his love of computers and synthesis. After serving in the military and a few years going to college in Colorado where he studied electronics and computer programming. George moved to Europe and settled down in Germany where he had met his wife Christine. ... jamendo:336993 foaf:name a mo:biography Mohamed Ahmed Sherif — DEER 5/31

6. Motivation Approach Evaluation Conclusion and Future Work DEER Enrichment Functions Idea Generate enrichment data based on existing data Input/output are RDF datasets Current Enrichment Functions Dereferencing Linking NLP Conformation Filter Mohamed Ahmed Sherif — DEER 6/31

7. Motivation Approach Evaluation Conclusion and Future Work DEER Enrichment Operators Idea Create complex workﬂows by connecting modules Input/output are RDF datasets Current Enrichment Operators Clone Merge Mohamed Ahmed Sherif — DEER 7/31

8. Motivation Approach Evaluation Conclusion and Future Work DEER Speciﬁcation Paradigm d1 Dereferencing d2 cloned3 NLP d4 Filter d5 d6Merge d7 AuthorityConform d8 Mohamed Ahmed Sherif — DEER 8/31

9. Motivation Approach Evaluation Conclusion and Future Work Manual DEER Speciﬁcation 1 @ p r e f i x : <http :// geoknow . org / s p e c s o n t o l o g y/> . 2 @ p r e f i x r d f s : <http ://www. w3 . org /2000/01/ rdf−schema#> . 3 @ p r e f i x geo : <http ://www. w3 . org /2003/01/ geo/ wgs84 pos#> . 4 : d1 a : Dataset ; 5 : hasUri <http :// dbpedia . org / r e s o u r c e / B e r l i n> ; 6 : fromEndPoint <http :// dbpedia . org / sparql> . 7 : d2 a : Dataset . 8 : d3 a : Dataset . 9 : d4 a : Dataset . 10 : d5 a : Dataset . 11 : d6 a : Dataset . 12 : d7 a : Dataset . 13 : d8 a : Dataset ; 14 : o u t p u t F i l e ” D e e r B e r l i n . t t l ” ; 15 : outputFormat ” T u r t l e ” . 16 : d e r e f a : Module , : DereferencingModule ; 17 r d f s : l a b e l ” D e r e f e r e n c i n g module” ; 18 : hasInput : d1 ; 19 : hasOutput : d2 ; 20 : hasParameter : derefParam1 . 21 : derefParam1 a : ModuleParameter , : DereferencingModuleParameter ; 22 : hasKey ” i n p u t P r o p e r t y 1 ” ; 23 : hasValue geo : l a t . 24 : c l o n e a : Operator , : CloneOperator ; 25 r d f s : l a b e l ” Clone o p e r a t o r ” ; 26 : hasInput : d2 ; 27 : hasOutput : d3 , : d4 . 28 : nlp a : Module , : NLPModule ; 29 r d f s : l a b e l ”NLP module” ; 30 : hasInput : d3 ; 31 : hasOutput : d5 ; 32 : hasParameter : nlpPram1 , : nlpPram2 .Mohamed Ahmed Sherif — DEER 9/31

10. Motivation Approach Evaluation Conclusion and Future Work Manual KB Enrichment Manual customized enrichment pipelines ⊕ Leads to the expected results Time consuming Cannot be ported easily to other datasets Mohamed Ahmed Sherif — DEER 10/31

11. Motivation Approach Evaluation Conclusion and Future Work Automatic KB Enrichment Enrichment pipeline M : K → K that maps KB K to an enriched KB K with K = M(K). M is an ordered list of atomic enrichment functions m ∈ M M = φ if K = K , (m1, . . . , mn), where mi ∈ M, 1 ≤ i ≤ n otherwise. Research questions 1 How to create self-conﬁguring atomic enrichment functions m ∈ M? 2 How to automatically generate an enrichment pipeline M? Mohamed Ahmed Sherif — DEER 11/31

12. Motivation Approach Evaluation Conclusion and Future Work Automatic KB Enrichment Enrichment pipeline M : K → K that maps KB K to an enriched KB K with K = M(K). M is an ordered list of atomic enrichment functions m ∈ M M = φ if K = K , (m1, . . . , mn), where mi ∈ M, 1 ≤ i ≤ n otherwise. Research questions 1 How to create self-conﬁguring atomic enrichment functions m ∈ M? 2 How to automatically generate an enrichment pipeline M? Mohamed Ahmed Sherif — DEER 11/31

14. Motivation Approach Evaluation Conclusion and Future Work Running Example Dataset DrugBank Goal Gather information about companies related to drugs for a market study :Aspirin :Paracetamol :Ibuprofen :Quinine :Drug a a a a Mohamed Ahmed Sherif — DEER 13/31

15. Motivation Approach Evaluation Conclusion and Future Work Running Example Dataset DrugBank Goal Gather information about companies related to drugs for a market study :Aspirin :Paracetamol :Ibuprofen :Quinine db:Ibuprofen db:Aspirin Ibuprofen was extracted by the research arm of Boots company during the 1960s ... :Drug a a a a owl:sameAs owl:sameAs rdfs:comment Mohamed Ahmed Sherif — DEER 13/31

16. Motivation Approach Evaluation Conclusion and Future Work Atomic Enrichment Functions I. 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 :Drug a a a a Mohamed Ahmed Sherif — DEER 14/31

17. Motivation Approach Evaluation Conclusion and Future Work Atomic Enrichment Functions I. 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 arm of Boots company during the 1960s ... :Drug a a a a owl:sameAs owl:sameAs rdfs:comment Mohamed Ahmed Sherif — DEER 14/31

18. Motivation Approach Evaluation Conclusion and Future Work Atomic Enrichment Functions I. 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 arm of Boots company during the 1960s ... :Drug a a a a owl:sameAs owl:sameAs rdfs:commentrdfs:comment Mohamed Ahmed Sherif — DEER 14/31

19. Motivation Approach Evaluation Conclusion and Future Work Atomic Enrichment Functions I. 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 arm of Boots company during the 1960s ... Ibuprofen was extracted by the research arm of Boots company during the 1960s ... :Drug a a a a owl:sameAs owl:sameAs rdfs:commentrdfs:comment rdfs:comment Mohamed Ahmed Sherif — DEER 14/31

20. Motivation Approach Evaluation Conclusion and Future Work Self-Conﬁguration I. Dereferencing Enrichment Functions Finds the set of predicates Dp from the enriched CBDs that 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 arm of Boots company during the 1960s ... :Drug :BootsCompany a :relatedCompany owl:sameAs rdfs:comment Dp = {:relatedCompany, rdfs:comment} Mohamed Ahmed Sherif — DEER 15/31

24. Motivation Approach Evaluation Conclusion and Future Work Self-Conﬁguration I. 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 arm of Boots company during the 1960s ... :Drug a a a a 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 arm of Boots company during the 1960s ... :Drugaowl:sameAs rdfs:comment Mohamed Ahmed Sherif — DEER 16/31

28. Motivation Approach Evaluation Conclusion and Future Work Atomic Enrichment Functions II. 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 arm of Boots company during the 1960s ... :Drugaowl:sameAs rdfs:comment Mohamed Ahmed Sherif — DEER 17/31

31. Motivation Approach Evaluation Conclusion and Future Work Atomic Enrichment Functions II. 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 arm of Boots company during the 1960s ... :Drug :BootsCompany a fox:relatedTo owl:sameAs rdfs:comment Mohamed Ahmed Sherif — DEER 17/31

32. Motivation Approach Evaluation Conclusion and Future Work Self-Conﬁguration II. 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 arm of Boots company during the 1960s ... :Drugaowl:sameAs rdfs:comment Mohamed Ahmed Sherif — DEER 18/31

33. Motivation Approach Evaluation Conclusion and Future Work Self-Conﬁguration II. 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 arm of Boots company during the 1960s ... :Drugaowl:sameAs rdfs:comment Mohamed Ahmed Sherif — DEER 18/31

34. Motivation Approach Evaluation Conclusion and Future Work Self-Conﬁguration II. 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 arm of Boots company during the 1960s ... :Drug :BootsCompany a fox:relatedTo owl:sameAs rdfs:comment Mohamed Ahmed Sherif — DEER 18/31

35. Motivation Approach Evaluation Conclusion and Future Work Atomic Enrichment Functions III. Predicate conformation atomic enrichment function Enriched datasets may contain diverse ontologies Predicate conformation maps a set of a pre-speciﬁed predicates to a target ontology :Ibuprofendb:Ibuprofen Ibuprofen was extracted by the research arm of Boots company during the 1960s ... :Drug :BootsCompany a fox:relatedTo owl:sameAs rdfs:comment Mohamed Ahmed Sherif — DEER 19/31

36. Motivation Approach Evaluation Conclusion and Future Work Atomic Enrichment Functions III. Predicate conformation atomic enrichment function Enriched datasets may contain diverse ontologies Predicate conformation maps a set of a pre-speciﬁed predicates to a target ontology :Ibuprofendb:Ibuprofen Ibuprofen was extracted by the research arm of Boots company during the 1960s ... :Drug :BootsCompany a fox:relatedTo owl:sameAs rdfs:comment Mohamed Ahmed Sherif — DEER 19/31

37. Motivation Approach Evaluation Conclusion and Future Work Atomic Enrichment Functions III. Predicate conformation atomic enrichment function Enriched datasets may contain diverse ontologies Predicate conformation maps a set of a pre-speciﬁed predicates to a target ontology :Ibuprofendb:Ibuprofen Ibuprofen was extracted by the research arm of Boots company during the 1960s ... :Drug :BootsCompany a fox:relatedTo:relatedCompany owl:sameAs rdfs:comment Mohamed Ahmed Sherif — DEER 19/31

38. Motivation Approach Evaluation Conclusion and Future Work Self-Conﬁguration III. Predicate conformation Enrichment Function Finds list of predicates Ps and Pt from the source resp. target datasets with the same subject and objects Changes each Ps with its respective Pt NLP enriched CBD of Ibuprofen :Ibuprofendb:Ibuprofen Ibuprofen was extracted by the research arm of 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 arm of Boots company during the 1960s ... :Drug :BootsCompany a :relatedCompany owl:sameAs rdfs:comment Mohamed Ahmed Sherif — DEER 20/31

39. Motivation Approach Evaluation Conclusion and Future Work Self-Conﬁguration III. Predicate conformation Enrichment Function Finds list of predicates Ps and Pt from the source resp. target datasets with the same subject and objects Changes each Ps with its respective Pt NLP enriched CBD of Ibuprofen :Ibuprofendb:Ibuprofen Ibuprofen was extracted by the research arm of 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 arm of Boots company during the 1960s ... :Drug :BootsCompany a :relatedCompany owl:sameAs rdfs:comment Mohamed Ahmed Sherif — DEER 20/31

40. Motivation Approach Evaluation Conclusion and Future Work Self-Conﬁguration III. Predicate conformation Enrichment Function Finds list of predicates Ps and Pt from the source resp. target datasets with the same subject and objects Changes each Ps with its respective Pt NLP enriched CBD of Ibuprofen :Ibuprofendb:Ibuprofen Ibuprofen was extracted by the research arm of 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 arm of Boots company during the 1960s ... :Drug :BootsCompany a :relatedCompany owl:sameAs rdfs:comment Mohamed Ahmed Sherif — DEER 20/31

41. Motivation Approach Evaluation Conclusion and Future Work KB Enrichment Reﬁnement Operator Input Set of atomic enrichment functions M Set of positive examples E Reﬁnement Operator ρ(M) = ∀m∈M M ++ m ( ++ is the list append operator) Output Enrichment pipeline M Mohamed Ahmed Sherif — DEER 21/31

44. 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 arm of Boots company during the 1960s ... :Drug :BootsCompany a :relatedCompany owl:sameAs rdfs:comment Enriched CBD of Ibuprofen Mohamed Ahmed Sherif — DEER 22/31

45. 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 — DEER 23/31

46. 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 — DEER 23/31

47. 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 — DEER 23/31

48. 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 — DEER 23/31

49. 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 — DEER 23/31

50. 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 — DEER 23/31

51. 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 — DEER 23/31

52. 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 — DEER 23/31

53. 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-measure and 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 the whole refinement tree Mohamed Ahmed Sherif — DEER 24/31

57. 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 10 Optimal enrichment pipeline found (F-score = 1) Mohamed Ahmed Sherif — DEER 26/31

58. 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 10 Optimal enrichment pipeline found (F-score = 1) Mohamed Ahmed Sherif — DEER 26/31

59. Motivation Approach Evaluation Conclusion and Future Work Configuration of the Search Strategy Node fitness 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). Result: ω = 0.75 leads to the best results ω P R F 0 1.0 0.99 0.99 0.25 1.0 0.99 0.99 0.50 1.0 0.99 0.99 0.75 1.0 1.0 1.0 1.0 1.0 0.99 0.99 Mohamed Ahmed Sherif — DEER 27/31

60. Motivation Approach Evaluation Conclusion and Future Work Eﬀect of Positive Examples Manual Examples Size of Time Size of Time Learn Iterations M count M M(KB) learned M M (KB) Time count F-score M1 DBpedia 1 1 0.2 1 1.6 1.3 1 1.0 2 1 0.2 1 1.8 1.3 1 1.0 M2 DBpedia 1 2 23.3 1 0.1 0.2 1 0.99 2 2 15 2 17 0.3 9 0.99 M3 DBpedia 1 3 14.7 3 15.2 6.1 9 0.99 2 3 15 2 15.1 0.1 9 0.99 M4 DBpedia 1 4 0.4 2 0.1 0.7 2 0.99 2 4 0.6 2 0.3 0.9 2 0.99 M5 DBpedia 1 5 22 2 0.1 0.7 2 1.0 2 5 25.5 2 0.2 0.9 2 1.0 M1 DrugBank 1 2 3.5 1 4.1 0.1 10 0.99 2 2 3.6 1 3.4 0.1 10 0.99 M2 DrugBank 1 3 25.2 1 0.1 0.1 10 0.99 2 3 22.8 1 0.1 0.1 10 0.99 M1 Jamendo 1 1 10.9 2 10.6 0.1 2 0.99 2 1 10.4 2 10.4 0.1 1 0.99 Mohamed Ahmed Sherif — DEER 28/31

62. Motivation Approach Evaluation Conclusion and Future Work Conclusion and Future Work Conclusion Introduced DEER Presented self-configuring atomic enrichment functions Presented an approach for learning enrichment pipelines based on a refinement operator Future Work Parallelize the algorithm on several CPUs as well as load balancing Support directed acyclic graphs as enrichment specifications by allowing to split and merge datasets Pro-active enrichment strategies and active learning Implement more enrichment function and operators Mohamed Ahmed Sherif — DEER 30/31

63. Motivation Approach Evaluation Conclusion and Future Work Conclusion and Future Work Conclusion Introduced DEER Presented self-configuring atomic enrichment functions Presented an approach for learning enrichment pipelines based on a refinement operator Future Work Parallelize the algorithm on several CPUs as well as load balancing Support directed acyclic graphs as enrichment specifications by allowing to split and merge datasets Pro-active enrichment strategies and active learning Implement more enrichment function and operators Mohamed Ahmed Sherif — DEER 30/31

64. Motivation Approach Evaluation Conclusion and Future Work Thank You! Questions? Mohamed Sherif Augustusplatz 10 D-04109 Leipzig sherif@informatik.uni-leipzig.de http://aksw.org/MohamedSherif http://aksw.org/Projects/DEER Automating RDF Dataset Transformation and Enrichment by Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo, and Jens Lehmann in 12th Extended Semantic Web Conference, Portoroz, Slovenia, 2015 Mohamed Ahmed Sherif — DEER 31/31

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