Do it on your own - From 3 to 5 Star Linked Open Data with RMLio

RML.io Generating High Quality

Linked Open Data from Open or Not Data

Anastasia Dimou Data Science Lab, Ghent University - iMinds

[email protected] @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?


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?


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

Lab works

“Anastasia Dimou”

located DataScience Lab

Ghent

Person 2 Data Science

Lab works

“Ruben Verborgh”

Person 3 DataScience

Lab works

“Erik Mannens”

Person {id}

{lab}

Assign unique identifiers (URIs)

“{firstname} {surname}”

http:://ex.com{id}

http://ex.com{lab}


Annotate data relationships with ontologies

http:://ex.com{id}

http://ex.com{lab}


http:://ex.com{id}

http://ex.com{lab}


ex:1 ex:DSLab ex:works

“Anastasia Dimou”

ex:located ex:DSLab ex:Ghent


“Ruben Verborgh”


“Erik Mannens”

ex:{id}

ex:{lab}

ex:located ex:{lab} ex:{city}

sets of triples of a dataset have repetitive patterns


ex:{id}

ex:{lab}



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 JSON XML

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


defines

RDF

DB CSV JSON XML 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


defines

RDF

DB CSV JSON XML 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 Map Subject 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}”


<#ResearcherMap>

<#LabMap>

rr:template “{firstname} {surname}” rr:termType rr:Literal


Extraction Module Mapping Module

RML Processor


Triples Map


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)



Triples 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}”


<#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:template “http://ex.com/

{/labs/lab/short}”


{/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



Subject Map

Predicate Map

Object Map

Logical Source

Reference Formulation


<#Lab Logical

Source>

ql:XPath






<#LabMap>

Triples Map



Subject Map

Predicate Map

Object Map

Logical Source


iterator


<#Lab Logical

Source>

ql:XPath

“/labs/lab”






<#LabMap>



Input data

Input data

Input

data

Output RDF

Mapping module

RML Processor

Map doc

Data source

Acce

ss in

terface

Input data

Input data

Input data

Output RDF

Mapping module

RML Processor

Map doc

Data source

Acce

ss in

terface

Data source

Acce

ss in

terface

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



Subject Map

Predicate Map

Object Map

Logical Source


iterator

Source

file.xml

WEB

AP

I D

CA

T

XML data

JSON data

tabular data

Output RDF

Mapping module

RML Processor

Map doc

Data repo

WEB

AP

I H

ydra

Data base

JDB

C

D2

RQ

Retrieval module

Source description

Triple store

SPAR

QL


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


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


dbo:birthDate http://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


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!


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


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


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

[email protected] @natadimou

Presentations & Public Speaking

Do it on your own - From 3 to 5 Star Linked Open Data with RMLio