RML.io
Generating High Quality
Linked Open Data
from Open or Not Data
Anastasia Dimou
Data Science Lab, Ghent University - iMinds
anastasia.dimou@ugent.be
@natadimou

What is the Semantic Web?

The Semantic Web
is the extension of the World Wide Web

Are you the owner of your data?
OR
is the application that hosts your data?

The Semantic Web
is the extension of the World Wide Web
enables sharing content beyond
the boundaries of applications & websites

The Web for humans, thanks to HTML,
is understandable & constant
BUT
is the Web for machines too?

The Semantic Web
is the extension of the World Wide Web
enables sharing content beyond
the boundaries of applications & websites
allows machines to understand the
meaning of hyperlinked information

Semantic Web enabled applications
rely on data represented as Linked Data

What is Linked (Open) Data?

Linked (Open) Data
a standardized way of
expressing the relationships between data
semantically annotated the data
with different vocabularies or ontologies
describe domain-level knowledge
understandable by humans & machines

How is Linked Data published?

Linked (Open) Data
published in the form of RDF datasets

Resource Description Framework (RDF)
is the prevalent data model
for describing Linked (Open) Data
driven by unique identifiers (URIs)
allows establishing a shared meaning
predicate
subject object

How is Linked Data derived
from (semi-)structured data?

How is Linked Data derived
from (semi-)structured data?
id firstname lastname lab city
1 Anastasia Dimou DSLab Ghent
2 Ruben Verborgh DSLab Ghent
3 Erik Mannens DSLab Ghent

Person 1
Data Science
Labworks
“Anastasia Dimou”
locatedDataScience
Lab
Ghent
Person 2
Data Science
Labworks
“Ruben Verborgh”
Person 3
DataScience
Labworks
“Erik Mannens”

Person {id}
{lab}
Assign unique identifiers (URIs)
“{firstname}
{surname}”
http:://ex.
com{id}
http://ex.com
{lab}
“{firstname}
{surname}”

Annotate data relationships with ontologies
http:://ex.
com{id}
http://ex.com
{lab}
“{firstname}
{surname}”
http:://ex.
com{id}
http://ex.com
{lab}
“{firstname}
{surname}”

ex:1 ex:DSLabex:works
“Anastasia Dimou”
ex:locatedex:DSLab ex:Ghent
ex:2 ex:DSLabex:works
“Ruben Verborgh”
ex:3 ex:DSLabex:works
“Erik Mannens”

ex:{id}
ex:{lab}
ex:located
ex:{lab} ex:{city}
sets of triples of a dataset have repetitive patterns
“{firstname}
{surname}”

ex:{id}
ex:{lab}
sets of triples of a dataset have repetitive patterns
“{firstname}
{surname}”
RDF dataset generation tools
rely their implementation on repetitively
applying those patterns to input data
ex:located
ex:{lab} ex:{city}

What are the different
Linked Data Generation approaches?

Linked Data generation approaches
case-specific solutions
OR
format and source specific

R2RML mappings R2RML processor
Data OWNER / PUBLISHER
defines
RDF
DB CSV JSONXML
RDF RDF RDF

RDF Terms (focusing on IRIs) are…
generated independently
disregarding their possible prior definitions
manually replicated
by reconstructing the same URIs (if possible)
manually aligned afterwards
links with other datasets are defined after
the RDF terms are published

Why not a uniform approach?

Uniform and declarative RDF generation
from heterogeneous data sources
mappings processor
Data OWNER / PUBLISHER
defines
RDF
DB CSV JSONXML RDF

RDF Mapping Language (RML)
generic scalable mapping language
for generating and interlinking
RDF data from heterogeneous resources
in an integrable and interoperable fashion
superset of the W3C standardized
R2RML mapping language
http://rml.io

Uniform and declarative RDF generation
from heterogeneous data sources
RML mappings processor
Data OWNER / PUBLISHER
defines
RDF
DB CSV JSONXML RDF

Defining Mappings to generate Linked Data
Retrieving Input Data
Assessing Quality
Generating Metadata
Editing Mappings

Defining Mappings to generate RDF data
Retrieving Input Data
Assessing Quality
Generating Metadata
Editing Mappings

RML describes
how to generated RDF
from structured data
predicate
subject object
Predicate MapSubject
Map
Object
Map
<#TriplesMap>

rr:constant
ex:located
rr:template
“http://ex.com/{lab}”
rr:template
“http://ex.com/{city}”
rr:template
“http://ex.com/{id}”
rr:template
“http://ex.com/{lab}”
<#ResearcherMap>
<#LabMap>
rr:template “{firstname} {surname}”
rr:termType rr:Literal
RDF Mapping Language (RML)

Extraction Module Mapping Module
RML Processor

Defining Mappings to generate Linked Data
Retrieving Input Data
Assessing Quality
Generating Metadata
Editing Mappings

Triples Map
RDF Mapping Language (RML)
Predicate
Object Map
Subject
Map
Predicate
Map
Object Map

RML describes
rules to map any structured data to RDF
RML supports any data independently of
which structure and format they have
where they originally reside
how they are accessed & retrieved

Specifying data
which data form a data input
how to reference data input extracts
Accessing & Retrieving data
data input from original source(s)

Specifying data
which data form a data input
how to reference data input extracts
Accessing & Retrieving data
data input from original source(s)

Triples Map
RDF Mapping Language (RML)
Predicate
Object Map
Subject
Map
Predicate
Map
Object Map
Logical
Source

Support data in Heterogeneous Structures and Formats
tabular-structured
tables in DBs or CSV files …
hierarchical-structured
JSON or XML …
(semi-)structured
HTML …
… … …

rr:template
“http://ex.com/{id}”
rr:template
“http://ex.com/{lab}”
<#ResearcherMap> rr:template “{firstname} {surname}”
rr:termType rr:Literal
id firstname surname lab
1 Anastasia Dimou DSLab
2 Ruben Verborgh DSLab
3 Erik Mannens DSLab
tabular-structured data

rr:constant
ex:located
rr:template
“http://ex.com/
{/labs/lab/short}”
rr:template
“http://ex.com/
{/labs/lab/location/city}”
<#LabMap>
<labs>
<lab>
<short>MMLab</short>
<title>Multimedia Lab</title>
<location>
<city>Ghent</city>
</location>
</lab>
<lab> …. </lab>
…
</labs>
hierarchical-structured data

Triples Map
RDF Mapping Language (RML)
Predicate
Object Map
Subject
Map
Predicate
Map
Object Map
Logical
Source
Reference
Formulation

<labs>
<lab>
<short>MMLab</short>
<title>Multimedia Lab</title>
<location>
<city>Ghent</city>
</location>
</lab>
<lab> …. </lab>
…
</labs>
<#Lab
Logical
Source>
ql:XPath
rr:constant
ex:located
rr:template
“http://ex.com/
{/labs/lab/short}”
rr:template
“http://ex.com/
{/labs/lab/location/city}”
<#LabMap>

Triples Map
RDF Mapping Language (RML)
Predicate
Object Map
Subject
Map
Predicate
Map
Object Map
Logical
Source
Reference
Formulation
iterator

<labs>
<lab>
<short>MMLab</short>
<title>Multimedia Lab</title>
<location>
<city>Ghent</city>
</location>
</lab>
<lab> …. </lab>
…
</labs>
<#Lab
Logical
Source>
ql:XPath
“/labs/lab”
rr:constant
ex:located
rr:template
“http://ex.com/
{/labs/lab/short}”
rr:template
“http://ex.com/
{/labs/lab/location/city}”
<#LabMap>

Specifying data
which data form a data input
how to reference data input extracts
Accessing & Retrieving data
data input from original source(s)

Input
data
Input
data
Input
data
Output
RDF
Mapping
module
RML
Processor
Map
doc

Data
source
Access
interface
Input
data
Input
data
Input
data
Output
RDF
Mapping
module
RML
Processor
Map
doc
Data
source
Access
interface
Data
source
Access
interface
Retrieval
module
Source
description

Support different Locations and Access Interfaces
Local File(s)
Database connectivity
D2RQ
Web source(s) (Web API/service)
DCAT, CSVW, Hydra, VOiD (Dataset)
RDF source(s)
VOiD (Endpoint), SPARQL-SD

Triples Map
RDF Mapping Language (RML)
Predicate
Object Map
Subject
Map
Predicate
Map
Object Map
Logical
Source
Reference
Formulation
iterator
Source

file.xml
WEBAPI
DCAT
XML
data
JSON
data
tabular
data
Output
RDF
Mapping
module
RML
Processor
Map
doc
Data
repo
WEBAPI
Hydra
Data
base
JDBC
D2RQ
Retrieval
module
Source
description
Triple
store
SPARQL

Defining Mappings to generate Linked Data
Retrieving Input Data
Assessing Quality
Generating Metadata
Editing Mappings

http://example.com/
Giddeon_Massie
dbo:Event
"1981-08-27"
xsd:gYear
http://example.com/
Brick_Bronsky
dbo:Event
"1964"
xsd:gYear
http://example.com/
Steve_Meilinger
dbo:Event
"1930-12-12"
xsd:gYear
dbo:birthDate
http://example.com/
Chuck_Bednarik
dbo:Event
"1925-05-01"
xsd:gYear
http://example.com/
Matt_McBride
dbo:Event
"1985-05-23"
xsd:gYear
dbo:birthDate
dbo:birthDate
dbo:birthDate
dbo:birthDate

dbo:birthDate range  xsd:date
dbo:birthDate domain  dbo:Person
http://example.com/
Chuck_Bednarik
dbo:Event
"1925-05-01"
xsd:gYear
dbo:birthDate

Violations
Most frequent violations are
related to how
vocabularies or ontologies
are applied to the data
dbo:birthDate range  xsd:date
dbo:birthDate domain  dbo:Person
http://example.com/
Chuck_Bednarik
dbo:Event
"1925-05-01"
xsd:gYear
dbo:birthDate

RDF DQA with RDFUnit
test-driven data-debugging framework
based on SPARQL-patterns
dbo:birthDate
http://example.com/
Chuck_Bednarik
dbo:Event
"1925-05-01"
xsd:gYear
http://rdfunit.aksw.org

DQA: Dataset Quality Assessment
Adjustments to the dataset
are manually but rarely applied
but not at the root (hard to identify)
are overwritten if a new version of
the original data is mapped & published
violations
DQA

Instead of applying Quality Assessment
to the already published RDF dataset
as part of data consumption
Apply Quality Assessment to the Mappings
that generate the RDF dataset

MQA: Mapping Quality Assessment
discover violations before
they are even generated
specify the origin of the violation
easily apply structural adjustments
to the mappings

sets of triples of a dataset have repetitive patterns
dbo:birthDatehttp://example.com/
{Name}_{Surname}
dbo:Event
“Birth"
xsd:gYear
Mapping languages
formalize patterns into rules
to generate the RDF dataset
from the original data

MQA with RDFUnit over RML
dbo:birthDate
http://example.com/
Chuck_Bednarik
dbo:Person
"1925-05-01"
xsd:date
DEL: <#ObjectMap> rr:datatype xsd:gYear
ADD: <#ObjectMap> rr:datatype xsd:date

data map doc
Mapping
Processor
violations
MDQA
MDQA: Uniform Mapping & Dataset
Quality Assessment

Dataset Vs Mapping Quality Assessment
Dataset Quality Assessment Mapping Quality Assessment
size time size time
DBPedia EN 62M 16h 115K 11s
DBPedia NL 21M 1.5h 53K 6s
DBpedia all 511K 32s
* http://mappings.dbpedia.org/validation
Live update of DBpedia Mapping Quality Assessment results every night! 

Defining Mappings to generate Linked Data
Retrieving Input Data
Assessing Quality
Generating Metadata
Editing Mappings

Metadata
manually defined by
data publishers (person-agents),
rather than produced by
applications (software-agents)

Consider mapping rules to
automatically generate
self-descriptive
provenance and other metadata

W3C standardized Metadata
PROV
provenance information
VoID
expressing RDF dataset metadata
general metadata
structural metadata,
links between datasets
DCAT
describe datasets in data catalogs

Defining Mappings to generate Linked Data
Retrieving Input Data
Assessing Quality
Generating Metadata
Editing Mappings

Semantic Web experts Vs. Data specialists
Modeling Domain Knowledge
as Linked (Open) Data
is not straightforward for
Data Specialists
Data context
is not straightforward for
Semantic Web experts

Semantic Web experts Vs. Data specialists
Data Specialists
should be able to specify the mappings,
modify and extend them at any time

Approaches for Editing Mappings

RML Editor
http://rml.io/RMLeditor

Defining Mappings to generate Linked Data
Retrieving Input Data
Assessing Quality
Generating Metadata
Editing Mappings

The five stars of the Linked Open Data scheme
should not be approached as a set of
consecutive steps

Well-considered policy regarding
mapping and interlinking of data
in the context of a certain
knowledge domain

RML.io
Generating High Quality
Linked Open Data
from Open or Not Data
Anastasia Dimou
Data Science Lab, Ghent University - iMinds
anastasia.dimou@ugent.be
@natadimou