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|pdfUrl=https://ceur-ws.org/Vol-1550/article-03.pdf
|volume=Vol-1550
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==None==
https://ceur-ws.org/Vol-1550/article-03.pdf
Publishing the 15th Italian Population and Housing
Census in Linked Open Data
Raffaella Aracri, Stefano De Francisci, Andrea Pagano,
Monica Scannapieco, Laura Tosco, Luca Valentino
Istat – Istituto Nazionale di Statistica
{name.surname}@istat.it
Abstract. The paper describes the first project that Istat, the Italian National In-
stitute of Statistics, has set up for publishing data in LOD on its own SPARQL
endpoint. Both the publication process and the underlying technical architecture
are described with a focus on design choices (e.g. the adoption of RDF Data
Cube Vocabulary for multidimensional data representation and the usage of
R2RML for mapping rules specification) and on the adopted technological plat-
form.
1 Introduction
National Statistical Institutes (NSIs) play an important role as data producers, by pub-
lishing Official Statistics in the service of citizens and policy-makers. Statistical pro-
duction processes are indeed intended to produce “data” as their final output. An im-
portant phase of such processes is the dissemination phase, dedicated to the design
and development of publication tools aimed to reach the widest possible range of
users. In this respect, the Linked Data paradigm [1] appears to be extremely promis-
ing as part of the dissemination strategy of NSIs.
The increasing Internet penetration and the subsequent proliferation of electronic data
exchanges, resulted in the study of new protocols, models and formats specific for the
statistical data domain. Ten years work on searching and defining “a common lan-
guage and a common perception of the structure of classifications and the links be-
tween them” has originated the Neuchâtel model, the first relevant standard for data
and metadata representation [2].
In recent years, further standard statistical data models have emerged, namely GSIM
and SDMX. GSIM (Generic Statistical Information Model) [3] is a reference frame-
work of internationally agreed concepts, attributes and relationships that describes the
conceptual view of information relevant to Official Statistics production. SDMX (Sta-
tistical Data and Metadata Exchange) [4] is an ISO international standard, based on
XML, available since 2004. It provides a framework (i.e., models, formats, guide-
lines, software tools, etc.) with the purpose of supporting the exchange of data and
metadata. In addition to GSIM and SDMX, DDI (Document Data Initiative) [5] is
also emerging as an XML based standard that can be possibly used for data and
metadata representation in the statistical domain.
With respect to data standardization activities, NSIs are moving towards two direc-
tions; on the one hand they are promoting the development of models that are “ad-
hoc” to the statistical domain, like SDMX; on the other hand, they are instead work-
ing for the purpose of extending the base of data users, going also beyond the estab-
lished statistical users. The approach to achieve this latter goal involves the use of
worldwide standard models and formats for data sharing, mainly Semantic Web
standards. Indeed, several NSIs have set up a SPARQL endpoint for publishing their
data in Linked Open Data (LOD) format, including INSEE (France) [20], EL.STAT
(Greece) [19] and CSO (Ireland) [21].
In this paper, we describe a project by Istat (the Italian National Institute of Statistics)
aimed to publish indicators of the 2011 Population Census as Linked Open Data.
The project, named Census-LOD, is a flagship project that also has the ambitious goal
to pave the way for the introduction of LOD as a stable channel for Official Statistics
dissemination.
The remaining of the paper is structured as follows. After providing an overview of
the dissemination activities related to the latest Italian Population and Housing Census
in Section 2, we present the main contribution of the paper in Section 3 by describing
the process followed to publish Census data in LOD. In Section 4, we briefly discuss
the certified publication of Istat data, while in Section 5 we conclude with final re-
marks.
2 The 15th Italian Population and Housing Census: an
Overview
The data collection phase of the 15th Italian General Population and Housing Census
begun in October 2011 and ended in early 2012. The first outputs were disseminated
during 2012 (namely, provisional data and legal population). After the dissemination
of the legal population, the correction and validation phase for data relating to indi-
viduals, households and buildings started. A large amount of statistical analyses are
being available since May 2014 through the I.Stat system, the Istat Web warehouse
[6].
The reports disseminated via I.Stat have municipalities as the lowest detail of the
territorial dimension. As for the previous Census editions, Istat plans to publish a
defined subset of indicators also at the territorial level of Census section, which is a
sub-municipality level.
Currently, the Italian territory is divided into 400000 Census sections, so this level of
publication concerns a huge amount of data that, in the past Census editions, were
distributed as CSV or MS Excel files. A provisional version of these files is already
available on the Web with a list of 43 indicators related to the population measures.
The number of published indicators is expected to grow to around 200 by December
2014 with the inclusion of population, households, dwellings and buildings.
3 The Census-LOD Project
The Census-LOD project aims to make available the Census data at the Census sec-
tion level in LOD.
The Istat dissemination architecture is so far based mainly on SDMX; in particular,
the machine-to-machine data exchange is implemented by a set of Web services that
return SDMX data (the system is collectively called SEP - Single Exit Point).
Following the guidelines for the enhancement of the quality of public information [7]
distributed by the Agency for Digital Italy, Istat realized the need to broaden the dis-
semination to non-statistical/non-SDMX users and, in 2012, it started a project im-
plementing a translator from SDMX to RDF Data Cube Vocabulary (RDF-QB) [8].
The realized translator was validated on real Istat datasets, which were selected in
order to maximize their diversity with respect to both dataset number of observations
and number of dimensions [9]. We are working on the inclusion of the translator into
the SEP architecture with the goal of reusing the huge work on metadata already car-
ried out for SDMX-based dissemination.
Later, the possibility of publishing data directly according to LOD paradigm, was
taken into consideration independently from the SDMX-based publishing process. In
particular, with the Census-LOD project, we decided to develop a platform for the
LOD dissemination of: (i) the Population Census indicators at the territorial level of
Census sections, linked to (ii) the “Territory” dataset describing the Italian territorial
structure including regions, provinces, municipalities and Census sections and to (iii)
the “Geonames” ontology that is an international ontology for the description of phys-
ical and other territorial characteristics.
In more details, the Census-LOD project consisted of three main phases, specifically:
1. Domain analysis and ontology definition;
2. Triples generation;
3. LOD publishing.
In the following sections, we describe the above phases in details.
3.1 Domain Analysis and Ontology Definition Phase
As a first step of the project, we made an in-depth domain analysis, resulting in a
conceptual model of the domain of interest.
We started analyzing two datasets, named Censpop and Territory; such datasets were
used to be published in previous Census editions as CSV and XLS files.
The Censpop dataset describes the population Census indicators at the territorial level
of Census section. Table 1 shows an excerpt of one of the Censpop files; only three of
the total amount of about 200 indicators are shown, namely: (i) total population, (ii)
male population and (iii) female population.
Population
Province Municipa Census Total Population Population …
Code lity Code Section Population Male Female
5 1 50010000005 9 6 3 …
5 5 50050000343 34 17 17 …
5 118 51180000013 13 7 6 …
5 120 51200000001 292 141 151 …
5 121 51210000037 23 11 12 …
Table 1. Excerpt of Censpop Dataset
The Territory dataset describes the Italian territorial features from both administra-
tive and geographical perspectives. Fig. 1 shows an excerpt of one of the Territory
file composed by several sheets, describing the territory in its different aspects as
localities, municipalities, administrative zones etc.
The amount of involved data is huge: there are about 402903 Census sections, 74482
localities, 2200 Census areas, 3631 geomorphological entities and 43 indicators for
each entity (e.g., “Resident Population – Males”, “Resident Population – age > 74
years”, “Foreigners and stateless persons resident in Italy – Males” and so on).
In order to design the ontologies necessary for the data publication, we met periodi-
cally domain experts and interviewed them. In addition, we followed an abstraction
process starting from the concrete data representations that we had at hand (i.e. CSV
and Excel files).
We designed two distinct ontologies: (i) the Territorial Ontology, and (ii) the Census
Data Ontology.
The Territorial Ontology is an OWL [10] ontology and describes the administrative
and the geographical organization of the territory. It is composed by:
95 entities, describing regions, provinces, municipalities, locations, census sec-
tions, special areas, special units (e.g., abbeys or hospitals), and so on.
About 200 roles, describing relationships between entities, e.g. appartieneAC-
DASC links a municipality with its sub-municipalities components. Moreover, the
ontology describes for each role its cardinalities and its inverse roles. For the enti-
ties having a corresponding definition in the Geonames ontology a relation of
EquivalentTo has been defined with the relative Geonames entity. Finally, sub-
ClassOf roles have been defined to descript the hierarchies between entities.
The Census Data Ontology has been written using the Data Cube Vocabulary [8],
which is in turn based on OWL: it describes the Census data in terms of measures and
dimensions for a total of:
6 dimensions, including sex, age classes, citizenship, territory which is the territory
defined in the Territorial Ontology;
2 measures: number of residents, foreigners and stateless resident in Italy.
Fig. 1. Excerpt of Territory dataset
Fig. 2 shows an example of an observation expressed with the Data Cube vocabulary.
Both ontologies make use of meta Ontologies as: (i) SKOS [11] for the description of
classifications, (ii) ADMS [12] for the description of interoperability assets, and (iii)
PROV ontology [13] for the description of the provenance of the data in terms of
information about entities, activities, and people involved in the data production pro-
cess.
Fig. 2. Exampple of a Data Cu
ube observation
n
3.2 Trriples Genera
ation Phase
The triplees generation phase
p was perrformed in ord
der to transforrm the initial ddatasets in
the LOD format.
Fig. 33. Triples generation
Fig. 3 shoows the designed workflow w. As already mentioned,
m th
he datasets aree original-
ly producced as CSV files;
f thereforee, the first steep involves th he loading of CSV files
into stagiing tables thaat reflect the CSV file stru ucture. This step
s can be eeasily per-
formed using the direct load utility aavailable in reelational datab bases. Subsequuently, we
defined a set of mapp ping rules to ggenerate the RDF R triples; such
s rules per
ermitted to
map (i) thhe concepts stored into thee database into o (ii) the conccepts defined in the on-
tologies. The rules deffinition phasee should be preceded by a URI policy ddesign for
which wee followed thee best practice s descripted in n [14].
In order to specify mapping rules we used R2RML [15] that is the language for ex-
pressing customized mappings from relational databases to RDF datasets recom-
mended by W3C. In Fig. 4Fig. 3 an example of R2RML mapping rule is shown. In
particular, the rule maps each row in the logical table (base table, view or a valid SQL
Query) to a number of RDF triples.
The mapping implementation can be on-demand or made by data materialization; in
the first case the triples are created as a response to a query while, in the second case,
they are materialized into the triple store. The triple store loading phase is completed
by adding the new triples obtained as result of a reasoning phase; these rules are spec-
ified by the defined ontologies and are not coded by explicit mapping rules.
Fig. 4. Mapping of “Area in Dispute” to the corresponding subject with predicate “Disput-
edBy” and object “Municipaliy”
3.3 LOD Publishing Phase
The publishing phase includes a first step related to the choice of the Web application
layer needed to access the triple store: specifically we provide three access points to
cover the requirements of the different possible users interested to LOD data.
As shown in Fig. 5, the design of the Web site consists of three different components:
(i) a SPARQL endpoint, (ii) a Linked Data Interface (Faceted/Graph browser) and
(iii) an ad-hoc GUI for datasets downloading.
Advanced users are supposed to be SPARQL knowledgeable and can directly access
the SPARQL endpoint, which in turn can also be used for machine-to-machine com-
munication. Basic users can instead use the Linked Data Interface to browse data or
the ad-hoc application that provides a set of predefined queries and functionalities to
build customized queries.
The LOD D publishing phase
p results inn the publicatiion of the SPA
ARQL endpoiint and the
related Web
W containerrs. This phasee is currently y in progress; the expectedd data for
Census-LLOD Web site publication iss December 2014.
2
Fig.. 5. Web site deesign
This phasse also includ des the definitiion of the tech
hnological ennvironment. Thhe core of
the platfoorm is the tripple store. Wee analyzed sev veral platform
ms for publishhing LOD,
both openn and commeercial. The ressult of such an a analysis waas the choice of Oracle
“Spatial & Graph” solution, as triplle store and SPARQL
S querry engine, beinng it fully
compliannt with the IT T infrastructurre already exiisting in Istat. This solutioon has the
major advvantage to scale up to billiions of tripless [9], which is an importannt require-
ment for the platform that
t will suppoort the Istat LO OD disseminaation channel.
A furtherr benefit of the Oracle platfform, is the ussage of the R2 2RML languaage [15] in
order to have
h a way to o specify mappping rules thaat is generalizzed, i.e. indeppendent on
the speciffic platform.
The otherr important architectural chhoice concernss the dissemin nation platform m; as men-
tioned in the previous section, we pllan to deploy a SPARQL endpoint and tw two differ-
ent interffaces for basicc and advanceed users. The SPARQL
S dpoint we use is Apache
end
Jena Adaapter (Joseki API) an opeen platform in ntegrated withh the Oracle SPARQL
query enggine. As Grap ph Browser, w we adopted th he ELDA fram mework [17],, the open
source im
mplementation n of the Linkedd Data API sp pecification released by Epiimorphics.
The Linked Data API provides a configurable way to access RDF data using simple
RESTful URLs that are translated into queries to a SPARQL endpoint. ELDA cus-
tomization consists of writing down, using the turtle syntax [18], an API that specifies
how to translate URLs into queries.
Fig. 6 shows the complete technological architecture.
Fig. 6. Functional and technological stacks
4 Certifying Istat Data
People make trust judgments based on data provenance that may or may not be ex-
plicitly offered to them. Since the data represented in LOD format, are accessible via
machine-to-machine communication, it is necessary to explicitly represent prove-
nance information in an accessible way to machines, so that software agent can make
trust judgments.
This issue has been recognized extremely important, and, indeed, the Italian guide-
lines for the enhancement of public information [7], point out the issue of data prove-
nance suggesting the usage of the W3C PROV Ontology [13]. We used the PROV
framework to certify the origin of the published data and the role of Istat as official
data producer. In more details, we associated to data provenance metadata that specify
who is responsible for the data, which are the entities, activities and agents involved
in the generation/manipulation processes, allowing to certify data quality and reliabil-
ity.
5 Conclusions
Census-LOD is the first project that deploys Istat data on a SPARQL endpoint that is
owned by Istat itself. The publication of Census indicators at the sub-municipality
level will be available in December 2014. Published data will be enriched with infor-
mation related to the administrative and geo-morphological division of the national
territory. All published data will be accompanied by their own formal semantics spec-
ification through the Territory and Census ontologies.
The LOD-based data dissemination will provide several benefits, including:
Fostering harmonization of information concepts also within Istat. Indeed, a sche-
ma integration step is necessary in order to publish data and this will also provide a
positive feedback by forcing statistical processes to share a common data seman-
tics.
Improving machine-to-machine data provisioning by Istat, by providing an RDF-
based channel in addition to the already available SDMX one.
Providing final users with advanced functionalities like navigational querying and
information discovery.
References
1. Linked Data: http://linkeddata.org/
2. Neuchâtel Model:
http://www1.unece.org/stat/platform/pages/viewpage.action?pageId=14319930
3. GSIM:
http://www1.unece.org/stat/platform/display/metis/Generic+Statistical+Information+Mode
l
4. SDMX: http://sdmx.org/
5. DDI: http://www.ddialliance.org/Specification/DDI-Lifecycle/3.1/
6. I.Stat: http://dati.istat.it
7. Linee Guida per la valorizzazione del patrimonio informativo pubblico (in Italian):
http://www.agid.gov.it/sites/default/files/linee_guida/patrimoniopubblicolg2014_v0.6.pdf,
2014.
8. RDF Data Cube Vocabulary: http://www.w3.org/TR/2013/CR-vocab-data-cube-
20130625/, 25 June 2013
9. Aracri R., De Francisci S., Pagano A., Scannapieco M., Tosco L., Valentino L.. :
“Integrating Statistical Data with the Semantic Web: The ISTAT Experience” negli Atti
della Congresso Nazionale AICA “Frontiere Digitali: dal Digital Divide alla Smart
Society" 8-20 Settembre 2013
10. Ontology Web Language (OWL): http://www.w3.org/TR/owl-ref/, 10 February 2004
11. Simple Knowledge Organization System (SKOS): http://www.w3.org/TR/2009/REC-skos-
reference-20090818/, 18 August 2009
12. Asset Description Metadata Schema (ADMS):
https://joinup.ec.europa.eu/asset/adms/home
13. PROV Ontology: http://www.w3.org/TR/2013/NOTE-prov-overview-20130430/, 30 April
2013.
14. W3C URI guidelines:
http://www.w3.org/2011/gld/wiki/223_Best_Practices_URI_Construction, 2011.
15. RDB to RDF Markup Language (R2RML): http://www.w3.org/TR/r2rml/, 27 September
2012
16. Michel F., Montagnat J., Faron-Zucker C: “A survey of RDB to RDF translation ap-
proaches and tools”, Research report - http://hal.archives-ouvertes.fr/hal-00903568 No-
vember 2013
17. ELDA: http://www.epimorphics.com/web/tools/elda.html
18. TTL: http://www.w3.org/TeamSubmission/turtle/, 28 March 2011
19. EL.STAT RDF site: http://linked-statistics.gr/
20. INSEE RDF site: http://rdf.insee.fr/
21. CSO RDF site: http://data.cso.ie/