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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
In loving memory of the Barack Obama examples
in Semantic Web conferences
How to create Linked Barack?
dbr:
Barack_
Obama
dbp:name
dbo:birthPlace
dbp:termStart
dbp:birthDate
"Barack
Obama"@en
dbr:
Hawaii
“20-01-2009”
“04-08-1961”
…
Linked Barack is based on
schema and data
A specific case…
Source
handle WikiText
Schema transformations
use custom schema (DBpedia ontology)
Data transformations
parse manually entered input data
https://en.wikipedia.org/wiki/Barack_Obama
https://en.wikipedia.org/wiki/Leopold_II_of_Belgium
… needs a specific solution?
…
select extract transform schema transform data
https://github.com/dbpedia/extraction-framework
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
Declarative schema transformations
are great
Use-case independent
Decoupled from the implementation
Declarative data transformations
makes Linked Data generation 🚀🚀🚀🚀
Declarative schema transformations
(i.e., semantic annotation rules)
are great,
so why not also for data transformations?
Outline
The current situation
existing approaches
disadvantages
What we provide
our approach
implementation
Outline
The current situation
existing approaches
direct mappings | successive steps
embedded data transformations| hard-coded
disadvantages
What we provide
our approach
implementation
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:
Honolulu
dbr:
Hawaii
?
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
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
direct mappings | successive steps
embedded data transformations| hard-coded
…
select extract transform schema transform data
https://github.com/dbpedia/extraction-framework
DBpedia EF
select
{{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|1961|8|4}}
|birth_name = Barack Hussein Obama II
…
extract
dbr:
Barack_
Obama
dbp:name
dbo:birthPlace
dbp:termStart
dbp:birthDate
[[Honolulu]],
[[Hawaii]],
U.S.
{{birth date and
age|1961|8|4}}
…
transform schema
Barack Obama
January 20, 2009
dbr:
Hawaii
dbr:
Barack_
Obama
dbp:name
dbo:birthPlace
dbp:termStart
dbp:birthDate
"Barack
Obama"@en
dbr:
Hawaii
“20-01-2009”
“04-08-1961”
…
transform data
……
Hard-coded: disadvantages
Coupled: data tranformations only usable in
that implementation
Case-specific: only for one use case
Outline
The current situation
existing approaches
disadvantages
What we provide
our approach
implementation
Disadvantages of current approaches
Restricted
Uncombinable
Coupled
Case-specific
What do we want?
Unrestricted data transformations
Combinable schema and data transformations
Uncoupled with the implementation
Case-independent solution
Outline
The current situation
existing approaches
disadvantages
What we provide
our approach
implementation
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
Outline
The current situation
existing approaches
disadvantages
What we provide
our approach
implementation
declaratives | tools
Outline
The current situation
existing approaches
disadvantages
What we provide
our approach
implementation
declaratives | tools
Declaratives
Declarative schema transformations
source agnostic, schema agnostic
RDF Mapping Language (RML) | http://RML.io
Declarative data transformations
implementation agnostic
Function Ontology (FnO) | http://FnO.io
Aligned
FunctionMap / functionValue
Connection between RML and FnO
RML mapping
source
subject
dbp:birthDate
birth_date
WikiText
dbr:{wiki_label}
predicate
reference
Person_
Mapping
birthDate_
Mapping
dbr:
Barack_
Obama
dbp:birthDate
{{birth date and
age|1961|8|4}}
FnO mapping
executes
inputString
DBpedia_
date_parser
birth_date
DBP_Parsing_
Function
“04-08-1961”
Separate RML and FnO
source
subject
dbp:birthDate
birth_date
WikiText
dbr:{wiki_label}
predicate
reference
Person_
Mapping
birthDate_
Mapping
executes
inputString
DBpedia_
date_parser
birth_date
DBP_Parsing_
Function
Aligned RML and FnO
source
subject
dbp:birthDate
executes
inputString
WikiText
dbr:{wiki_label}
DBpedia_
date_parser
birth_date
predicate
DBP_Parsing_
Function
Function
Map
Person_
Mapping
birthDate_
Mapping
Aligned RML and FnO
source
subject
dbp:birthDate
executes
inputString
WikiText
dbr:{wiki_label}
DBpedia_
date_parser
birth_date
predicate
DBP_Parsing_
Function
Function
Map
Person_
Mapping
birthDate_
Mapping
Outline
The current situation
existing approaches
disadvantages
What we provide
our approach
implementation
declaratives | tools
Practical - Implementation
RMLProcessor
include WikiText extractor
support FunctionMap / functionValue
connect to FunctionProcessor
FunctionProcessor
dynamically load and call function
External DBpedia Parsing functions
…
RML_FnO-doc
Function Processor
…
select extract transform schema +
transform data
…
RML_FnO-doc
Function Processor
…
select extract transform schema +
transform data
…
RML_FnO-doc
Function Processor
…
select extract transform schema +
transform data
…
RML_FnO-doc
Function Processor
…
select extract transform schema +
transform data
…
RML_FnO-doc
Function Processor
…
select extract transform schema +
transform data
…
RML_FnO-doc
Function Processor
…
select extract transform schema +
transform data
…
RML_FnO-doc
Function Processor
…
select extract transform schema +
transform data
…
RML_FnO-doc
Function Processor
…
select extract transform schema +
transform data
Outline
The current situation
existing approaches
disadvantages
What we provide
our approach
implementation
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
See it in action!
Booth 49
https://fnoio.github.io/dbpedia-demo/
https://github.com/RMLio/RML-Mapper/tree/extension-fno
https://github.com/FnOio/function-processor-java
https://github.com/FnOio/dbpedia-parsing-functions-scala

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ESWC2017 In-Use - Declarative Data Transformations for Linked Data Generation: the case of DBpedia

Editor's Notes

  1. I’d like to give a small warning here.. Even though numerous people told us not to…
  2. I am going to use barack obama. It’s probably one of the last times we can use our mascot
  3. Barack’s data in DBpedia…
  4. schema is very specific (so are the schema transformations) data tfs are very specific
  5. Is mostly generated using the DBpedia EF (specifically for the infoboxes): relevant pages are selected, the infoboxes are extracted, the values are put in a certain schema (so schema tfs), finally, the values themselves are transformed (so data transformations).
  6. However, they are embedded in the EF. That’s a pity, because dbpedia is so widely used etc. The EF has been tested on 1000s of wikipages, but you can’t use it for other use cases, and you can’t use the parsing functions outside the DBpedia EF. as we will see later, no current solutions can cope with more advanced data transformations
  7. More clear
  8. So, declarative schema transformations exist. They make generating linked data possible without depending on implementation or use case. Great. However, due to high data tfs demands, current generation approaches cannot be used. What about declarative data tfs?! That would be awesome!
  9. split?
  10. Is mostly generated using the DBpedia EF (specifically for the infoboxes): relevant pages are selected, the infoboxes are extracted, the values are put in a certain schema (so schema tfs), finally, the values themselves are transformed (so data transformations).
  11. so, you have all pages
  12. you select the ones with relevant infoboxes
  13. The infoboxes are extracted (just using following as simplified examples)
  14. use custom mapping doc for the schema tfs
  15. Then, the data is parsed to get ‘good data’. This is a very important part of DBpedia, as the data values are entered in wikipedia (so manually), the input data can be very diverse, typo’s different ways of writing things. A large deal of effort has been done into creating these parsing functions, and they are really good.
  16. not the only solution
  17. instead of use raw value directly…
  18. use value after being parsed by underlying function
  19. use value after being parsed by underlying function
  20. support wikitext: was easy (RMLProcessor is made for that, natural thing to do)
  21. [ ] 2 zinnen recap