1.
What Factors Influence the Design of
a Linked Data Generation Algorithm?
Anastasia Dimou, Pieter Heyvaert, Ben De Meester, Ruben Verborgh
imec.be - IDLab.technology
Anastasia.Dimou@ugent.be
@natadimou
23/04/2018

2.
Semantic Web technologies for
intelligent software agents

3.
intelligent software agents
not enough Linked Data
limited interaction with heterogeneous data

4.
human agents
do not put effort
to provide Linked Data
until
there are software agents that use it

5.
Semantic Web and Linked Data
vicious cycle

6.
facilitate Linked Data generation by
reducing data & semantic heterogeneity
increasing Linked Data quality

7.
How successful are we after all
with the design of our
Linked Data generation algorithms?

8.
Discuss SotA
Reflect on factors
Outline observations

9.
Discuss SotA
Reflect on factors
Outline observations

10.
data owner
custom implementation
Linked
Data
my
data

11.
data owner
Linked
Data
Linked
Data
Linked
Data
Linked
Data
DB CSV XML JSON
format-specific solutions - multiple data sources

12.
data owner
Linked
Data
Linked
Data
Linked
Data
Linked
Data
DB CSV XML JSON
rules definition & execution separation
R2RML

13.
Ultrawrap
Ontop
https://github.com/ontop/ontop
Morph
https://github.com/oeg-upm/morph-rdb
R2RMLParser
https://github.com/antidot/db2triples
R2RML implementations

14.
Ultrawrap
Sequeda et al. Ultrawrap: SPARQL execution on relational
data. JWS 2013
Ontop
Rodriguez-Muro et al. Efficient SPARQL-to-SQL with
R2RML mappings. JWS 2015
Morph
Priyatna et al. Formalisation and Experiences of R2RML
based SPARQL to SQL query translation using Morph.
WWW2014
R2RMLParser
Konstantinou et al. An Approach for the Incremental Export
of Relational Databases into RDF Graphs. IJAIT 2015
R2RML implementations

15.
Ultrawrap
Sequeda et al. Ultrawrap: SPARQL execution on relational
data. JWS 2013
Ontop
Rodriguez-Muro et al. Efficient SPARQL-to-SQL with
R2RML mappings. JWS 2015
Morph
Priyatna et al. Formalisation and Experiences of R2RML
based SPARQL to SQL query translation using Morph.
WWW2014
R2RMLParser
Konstantinou et al. An Approach for the Incremental Export
of Relational Databases into RDF Graphs. IJAIT 2015
R2RML implementations
SPARQLtoSQL
translation/rewriting algorithm
incremental RDF graph generation

16.
data owner
Linked
Data
Linked
Data
Linked
Data
Linked
Data
DB CSV XML JSON
rules definition & execution separation
R2RML

17.
data owner
DB CSV XML JSON
uniform declaration & execution
Linked
Data

18.
any data source
Logical Source
non-relational DBs
Nested Relational Model
RML
Dimou et al. RML: A Generic Language for Integrated RDF
Mappings of Heterogeneous Data. LDOW2014
D2RML
Chortaras et al. D2RML: Integrating Heterogeneous Data
and Web Services into Custom RDF Graphs. LDOW2018
xR2RML
Michel et al. Translation of relational and non-relational
databases into RDF with xR2RML. WEBIST2015
KR2RML
Slepicka et al. KR2RML: An Alternative Interpretation of
R2RML for Heterogeneous Sources. COLD2015
R2RML extensions for heterogeneous data sources

19.
XSPARQL
Akhtar et al. XSPARQL: Traveling between the XML and RDF
Worlds – and Avoiding the XSLT Pilgrimage. ESWC2008
SPARQL-Generate
Lefrancois et al. A SPARQL extension for generating RDF
from heterogeneous formats. ESWC2017
Datalift
Scharffe et al. Enabling Linked Data Publication with the
Datalift Platform. AAAI2012
Morph-streams
Morph-streams: SPARQLStream OBDA in action. SR4LD2014
I’m sorry if I didn’t mention your language!
other languages for heterogeneous data sources
parallel XML & DB pipelines
SPARQL-based mapping language
direct mapping - SPARQL construct
import data streams in relational DB

20.
RML
Dimou et al. RML: A Generic Language for Integrated RDF
Mappings of Heterogeneous Data. LDOW2014
D2RML
Chortaras et al. D2RML: Integrating Heterogeneous Data
and Web Services into Custom RDF Graphs. LDOW2018
xRML
Michel et al. Translation of relational and non-relational
databases into RDF with xR2RML. WEBIST2015
KR2RML
Slepicka et al. KR2RML: An Alternative Interpretation of
R2RML for Heterogeneous Sources. COLD2015
XSPARQL
Akhtar et al. XSPARQL: Traveling between the XML and RDF
Worlds – and Avoiding the XSLT Pilgrimage. ESWC2008
SPARQL-Generate
Lefrancois et al. A SPARQL extension for generating RDF
from heterogeneous formats. ESWC2017
Datalift
Scharffe et al. Enabling Linked Data Publication with the
Datalift Platform. AAAI2012
Morph-streams
Morph-streams: SPARQLStream OBDA in action. SR4LD2014
I’m sorry if I didn’t mention your language!
other languages for heterogeneous data sourcesR2RML extensions for heterogeneous data sources

21.
We have diverse languages to define Linked Data generation rules!
We look into languages with respect to
their expressivity OR effort to edit OR ...,
BUT how systematic are our evaluations?
Do we focus too much on languages &
we forget about implementations?
speculations on languages

22.
data owner
DB CSV XML JSON
Linked
Data

23.
RML
https://github.com/RMLio/RML-Mapper
https://github.com/carml/carml
D2RML
xR2RML
https://github.com/frmichel/morph-xr2rml
KR2RML
https://github.com/usc-isi-i2/Web-Karma/tree/master/kar
ma-common/src/main/java/edu/isi/karma/kr2rml
XSPARQL
https://github.com/semantalytics/xsparql
SPARQL-Generate
https://github.com/thesmartenergy/sparql-generate
Datalift
https://gforge.inria.fr/scm/?group_id=2935
Morph-streams
https://github.com/jpcik/morph-streams
I’m sorry if I don’t mention your implementation!
other implementations for heterogeneous data sourcesR2RML extensions for heterogeneous data sources

24.
For each language, there is its own implementations :-(
We start having more implementations/language :-) BUT
is the gain due to the language or the implementation?
how much implementations differ among each other?
should we look into languages wrt
the implementations they allow OR
the automation they allow on top of them OR….?
speculations on languages and their implementations

25.
We start dealing with heterogeneity :-)
data format (DBs, CSV, XML, JSON)
interfaces (DBs, Web APIs, )
Big Data
BUT we mainly focus on static data :-(
Do we cover all cases? What about streaming data?
Can we generate a Linked Data stream from a raw data stream?
speculations on implementations - input data

26.
We start dealing with data retrieval :-)
streaming static big data instead of loading on memory
NOT just import data in DB BUT Nested Relational model
incremental Linked Data generation
BUT is this all we can do wrt data retrieval?
speculations on implementations - input data complexity

27.
Explored algorithms related to SPARQLtoSQL translation/rewriting
BUT how mature is our SPARQLtoSQL translation/rewriting?
MORE
should we also focus on
optimizations wrt rules executions?
execution planning? others?
speculations on implementations - query complexity

28.
What factors influence the design of
your Linked Data generation algorithm?

29.
Discuss SotA
Reflect on factors
Outline observations

30.
Factors
purpose
materialization
diversity
dynamicity
direction
location
driving force
trigger
complexity

31.
Factors
purpose
materialization
diversity
dynamicity
direction
location
driving force
trigger
complexity

32.
data owner
Linked
Data
raw
data
data consumer
Linked
Data
raw
data
production-driven consumption-driven
purpose

33.
Factors
purpose (production Vs consumption-driven)
materialization
diversity
dynamicity
direction
location
driving force
trigger
complexity

34.
dumping on-the-fly
data owner
Linked
Data
raw
data
data owner
Linked
Data
raw
data
materialization

35.
Factors
purpose (production Vs consumption-driven)
materialization (dumping Vs on-the-fly)
diversity
dynamicity
direction
location
driving force
trigger
complexity

36.
homogeneity heterogeneity
table CSV XML JSON
Linked
Data
CSV CSV CSV
Linked
Data
diversity

37.
Factors
purpose (production Vs consumption-driven)
materialization (dumping Vs on-the-fly)
diversity (homogeneity Vs heterogeneity)
dynamicity
direction
location
driving force
trigger
complexity

38.
static dynamic
dynamicity

39.
Factors
purpose (production Vs consumption-driven)
Materialization (dumping Vs on-the-fly)
diversity (homogeneity Vs heterogeneity)
dynamicity (static Vs dynamic)
direction
location
driving force
trigger
complexity

40.
target-centric source-centric
table CSV XML JSON
Linked
Data
table CSV XML JSON
Linked
Data
Linked
Data
Linked
Data
Linked
Data
Linked
Data
direction

41.
Factors
purpose (production Vs consumption-driven)
materialization (dumping Vs on-the-fly)
diversity (homogeneity Vs heterogeneity)
dynamicity (static Vs dynamic)
Direction (target Vs source-centric)
location
driving force
trigger
complexity

42.
in-situ
table CSV
Linked
Data
XML JSON
Linked
Data
XML JSON
Linked
Data
remote
location

43.
Factors
purpose (production Vs consumption-driven)
materialization (dumping Vs on-the-fly)
diversity (homogeneity Vs heterogeneity)
dynamicity (static Vs dynamic)
direction (target Vs source-centric)
location (in-situ Vs remote)
driving force
trigger
complexity

44.
Linked
Data
raw
data
mapping-driven data-driven
rules rules
Linked
Data
raw
data
1
2
2
1
driving force

45.
Factors
purpose (production Vs consumption-driven)
materialization (dumping Vs on-the-fly)
diversity (homogeneity Vs heterogeneity)
dynamicity (static Vs dynamic)
direction (target Vs source-centric)
location (in-situ Vs remote)
driving force (mapping Vs data-driven)
trigger
complexity

46.
Linked
Data
raw
data
on demand real-time
rules rules
Linked
Data
raw
data
1
2
2
1
data
owner
data
consumer
raw
data
raw
data
rules
Linked
Data
raw
data
2
1
data
owner
trigger

47.
Factors
purpose (production Vs consumption-driven)
materialization (dumping Vs on-the-fly)
diversity (homogeneity Vs heterogeneity)
dynamicity (static Vs dynamic)
direction (target Vs source-centric)
location (in-situ Vs remote)
driving force (mapping Vs data-driven)
trigger (on-demand Vs real-time)
complexity

48.
Linked
Data
raw
data
data / rules query translation/rewriting
rules rules
Linked
Data
raw
data
query
complexity

49.
Factors
purpose (production Vs consumption-driven)
materialization (dumping Vs on-the-fly)
diversity (homogeneity Vs heterogeneity)
dynamicity (static Vs dynamic)
direction (target Vs source-centric)
location (in-situ Vs remote)
driving force (mapping Vs data-driven)
trigger (on-demand Vs real-time)
complexity (data/rules, SPARQLtoSQL translation)

50.
Discuss SotA
Reflect on factors
Outline observations

51.
consumption-driven tools
both static and dynamic data
only homogeneous data
used for production purposes
NOT optimized for production
optimized for SPARQLtoSQL
translation/rewriting
conclusions
production-driven tools
heterogeneous BUT static data
dynamic from otherwise static data
to deal with Big Data (latest trend)
NO optimization for complexity

52.
NO tools which support
data-driven approach
real-time data
(data/rules) complexity
Certain design patterns are frequently applied,
covering certain alternatives of a few of factors,
whereas alternatives are never explored
NO adequate tools for Linked Data generation for certain occasions OR
inadequate and inefficient tools are chosen as best alternatives
conclusions

53.
We still lack an in-depth understanding of
complexity & many degrees of freedom
in designing algorithms to generate Linked Data

54.
This prevents agents
from effortlessly generating Linked Data
and profiting from Semantic Web technologies

55.
Should we perhaps reconsider
the directions we take
for Linked Data generation?

56.
Once we exploit how Linked Data
is best generated in different cases,
intelligent software agents
will have enough Linked Data to work with
but ‘till then we should not consider
Linked Data availability as granted

57.
What Factors Influence the Design of
a Linked Data Generation Algorithm?
Anastasia Dimou, Pieter Heyvaert, Ben De Meester, Ruben Verborgh
imec.be - IDLab.technology
Anastasia.Dimou@ugent.be
@natadimou
23/04/2018