Declarative Data Transformations for Linked Data Generation: the case of DBpedia

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Ethelbert Lang
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1 Declarative Data Transformations for Linked Data Generation: the case of DBpedia Ben De Meester, Wouter Maroy, Anastasia Dimou, Ruben Verborgh, and Erik Mannens Ghent University imec IDLab, Belgium

3 In loving memory of the Barack Obama examples in Semantic Web conferences

4 How to create Linked Barack? dbp:name "Barack dbr: Barack_ Obama dbo:birthplace dbp:termstart dbr: Hawaii dbp:birthdate

5 Linked Barack is based on schema and data

6 A specific case Source handle WikiText

7 A specific case Source handle WikiText Schema transformations use custom schema (DBpedia ontology)

8 A specific case Source handle WikiText Schema transformations use custom schema (DBpedia ontology) Data transformations parse manually entered input data

9 needs a specific solution? select extract transform schema transform data

10 Data transformations are hard-coded in the DBpedia EF Hard-coded means case specific coupled with the implementation You can t use the DBpedia EF for other cases use the parsing functions outside the DBpedia EF

11 Declarative schema transformations are great Use-case independent Decoupled from the implementation

12 Declarative schema transformations (i.e., semantic annotation rules) are great, so why not also for data transformations? Declarative data transformations makes Linked Data generation

13 Outline The current situation existing approaches disadvantages What we provide our approach implementation

14 Outline The current situation existing approaches direct mappings successive steps embedded data transformations hard-coded disadvantages What we provide our approach implementation

15 direct mappings successive steps embedded data transformations hard-coded From original data to RDF with minimal change e.g., CSVW, JSON(-LD) Restricted: No schema nor data transformations [[Honolulu]], [[Hawaii]], U.S.? dbr: Hawaii dbr: Honolulu

16 direct mappings successive steps embedded data transformations hard-coded First data, then schema transformations (or vice versa) e.g., R2RML Restricted: depends on underlying system for data transformations e.g., SQL views for R2RML Uncombinable: combine transformations? e.g., Born should return a date

17 direct mappings successive steps embedded data transformations hard-coded Tool supports limited set of data transformations e.g., OpenRefine Restricted: limited set of data transformations parsing is more than splitting a string or one regular expression Coupled: types of data transformations depend on the tool

18 direct mappings successive steps embedded data transformations hard-coded DBpedia EF select extract transform schema transform data

22 select

23 extract {{Infobox president name = Barack Obama image = President Barack Obama.jpg office = President of the United States vicepresident = [[Joe Biden]] birth_place = [[Honolulu]], [[Hawaii]], U.S. term_start = January 20, 2009 term_end = January 20, 2017 birth_date = {{birth date and age }} birth_name = Barack Hussein Obama II

24 transform schema dbp:name Barack Obama dbr: Barack_ Obama dbo:birthplace dbp:termstart [[Honolulu]], [[Hawaii]], U.S. January 20, 2009 dbp:birthdate {{birth date and age }}

25 transform data dbp:name "Barack dbr: Barack_ Obama dbo:birthplace dbp:termstart dbr: Hawaii dbp:birthdate

26 Hard-coded: disadvantages Coupled: data tranformations only usable in that implementation Case-specific: only for one use case

27 Outline The current situation existing approaches disadvantages What we provide our approach implementation

28 Disadvantages of current approaches Restricted Uncombinable Coupled Case-specific

29 What do we want? Unrestricted data transformations Combinable schema and data transformations Uncoupled with the implementation Case-independent solution

30 Outline The current situation existing approaches disadvantages What we provide our approach implementation

31 Aligned declarative schema and declarative data transformations Aligned combine data and schema transformations Declarative data transformations no restriction re-use outside generation framework Aligned declaratives not use case, nor implementation specific

32 Outline The current situation existing approaches disadvantages What we provide our approach implementation declaratives tools

33 Outline The current situation existing approaches disadvantages What we provide our approach implementation declaratives tools

34 Declaratives Declarative schema transformations source agnostic, schema agnostic RDF Mapping Language (RML) Declarative data transformations implementation agnostic Aligned Function Ontology (FnO) FunctionMap / functionvalue Connection between RML and FnO

35 RML mapping Person_ Mapping source WikiText subject dbr:{wiki_label} birthdate_ Mapping predicate dbp:birthdate reference birth_date dbr: Barack_ Obama dbp:birthdate {{birth date and age }}

36 FnO mapping DBP_Parsing_ Function executes DBpedia_ date_parser inputstring birth_date

37 Separate RML and FnO Person_ Mapping source WikiText subject dbr:{wiki_label} birthdate_ Mapping predicate dbp:birthdate reference birth_date DBP_Parsing_ Function executes DBpedia_ date_parser inputstring birth_date

38 Aligned RML and FnO Person_ Mapping source WikiText subject dbr:{wiki_label} birthdate_ Mapping predicate dbp:birthdate Function Map DBP_Parsing_ Function executes DBpedia_ date_parser inputstring birth_date

39 Aligned RML and FnO Person_ Mapping source WikiText subject dbr:{wiki_label} birthdate_ Mapping predicate dbp:birthdate Function Map DBP_Parsing_ Function executes DBpedia_ date_parser inputstring birth_date

40 Outline The current situation existing approaches disadvantages What we provide our approach implementation declaratives tools

41 Practical - Implementation RMLProcessor include WikiText extractor support FunctionMap / functionvalue connect to FunctionProcessor FunctionProcessor dynamically load and call function External DBpedia Parsing functions

42 Function Processor select extract transform schema + transform data RML_FnO-doc

43 Function Processor select extract transform schema + transform data RML_FnO-doc

44 Function Processor select extract transform schema + transform data RML_FnO-doc

45 Function Processor select extract transform schema + transform data RML_FnO-doc

46 Function Processor select extract transform schema + transform data RML_FnO-doc

47 Function Processor select extract transform schema + transform data RML_FnO-doc

48 Function Processor select extract transform schema + transform data RML_FnO-doc

49 Function Processor select extract transform schema + transform data RML_FnO-doc

50 Outline The current situation existing approaches disadvantages What we provide our approach implementation

51 Our approach generates the same DBpedia data, and:

52 Our approach generates the same DBpedia data, and: You don t depend on the implementation

53 Our approach generates the same DBpedia data, and: You don t depend on the implementation You don t depend on the use case

54 Our approach generates the same DBpedia data, and: You don t depend on the implementation You don t depend on the use case DBpedia parsing functions can be reused elsewhere

55 Our approach generates the same DBpedia data, and: You don t depend on the implementation You don t depend on the use case DBpedia parsing functions can be reused elsewhere Data transformations can use existing or new external libraries

56 Our approach generates the same DBpedia data, and: You don t depend on the implementation You don t depend on the use case DBpedia parsing functions can be reused elsewhere Data transformations can use existing or new external libraries

57 See it in action! Booth

Semi-Automatic Example-Driven Linked Data Mapping Creation

Semi-Automatic Example-Driven Linked Data Mapping Creation Pieter Heyvaert, Anastasia Dimou, Ruben Verborgh, and Erik Mannens IDLab, Department of Electronics and Information Systems, Ghent University