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https://ceur-ws.org/Vol-1550/article-05.pdf
Publishing Official Classifications in Linked
Open Data
Giorgia Lodi1 , Antonio Maccioni1
Monica Scannapieco2 , Mauro Scanu2 , Laura Tosco2
1
Agenzia per l’Italia Digitale
(giorgia.lodi,antonio.maccioni)@agid.gov.it
2
Istituto Nazionale di Statistica
(scanu,scannapi,tosco)@istat.it
Abstract. Data interoperability is well recognized as a basic step for
developing integrated services supporting inter-organizations communi-
cation. The issue of ensuring data interoperability has been tackled by
many different communities in order to address various problems. In
particular, the (over-)national institutes of statistics deeply concern the
issuing of official and shared classifications (i.e., taxonomies, schemes,
code-lists) to be used in the jurisdiction of reference. On a different per-
spective, there has been much work from the Web data management
community to publish data on the Web in an interoperable way. The
efforts have converged on a series of standards and practices gathered
under the Semantic Web stack. Clearly, the two mentioned scenarios are
complementary as they can benefit one to another. To this concern, the
Italian Institute of Statistics (Istat) and the Agency for Digital Italy
(AgID) have launched an initiative aiming at producing official classifi-
cations under the form of Linked Open Data to be published in the Web
of data using standard ontologies. The paper describes and motivates
this initiative.
1 Introduction
In the Official Statistical (OS) domain, the issue of data interoperability has
been present since decades: both National and International exchanges of data
resulting from statistical processes are made possible only by adopting common
metadata models and formats.
From this point of view, a specific effort has been performed in such a domain
to standardize classifications, and hence to introduce the concept of “official”
classification, that is one conform with internationally accepted standards. In-
deed, most of the exchanged data in OS are multidimensional data represented
as measures and related dimensions. These latter are coded according to spe-
cific classifications, and hence official ones have played a significant role in data
exchange processes among National Statistical Institutes (NSIs).
The Linked Data initiative [7] is more and more affirming as the principal
mean for data interoperability, by permitting to create and interlink arbitrary
volumes of structured data across the Web. In particular, the Linked Data ini-
tiative is made possible by the widespread adoption of Web standards for pub-
lishing data according to the Resource Description Framework (RDF) model.
RDF allows to uniquely identifying resources on the Web, by means of a specific
URI (Uniform Resource Identifier). This feature has several advantages, includ-
ing (i) the possibility of a direct access to resources via a query language and
(ii) the ability to link data together in order to access them in an integrated
way (with the clear positive side-effect of higher quality, more information more
easily accessed, and so on).
The Linked Open Data (LOD) project is concerned with the publication of
Linked Data that are “Open”. There are several LOD datasets already available.
The so-called LOD cloud [8] covers more than an estimated 50 billion facts
from many different domains like geography, media, biology, chemistry, economy,
energy, etc.
The LOD project has had also an immediate and widespread success in the
e-government sector: several public administrations (PAs) and institutions are
starting publishing their data as LOD. To this end, in the specific Italian con-
text, at the end of 2012, the Agency for Digital Italy (AgID) published national
guidelines [11] that paved the way to the use of LOD as the data paradigm for
enabling semantic interoperability in the collaboration between PAs. Since then,
AgID continued to exercise its role of national Public Sector Information (PSI)
enabler by annually releasing a number of strategic documents for PAs. One
of these documents is the so-called national agenda which includes principles
(e.g., interoperability, usability, accessibility, data quality) and objectives to be
achieved by PAs within a year in order to implement, and sustain in the long
term, the PSI enhancing process. Following the G8 open data charter experience
[3], the agenda introduces the concept of key datasets to be released as high
quality open data. Among the key datasets, AgID identified “official” classifica-
tions as cross-domain data to be published in LOD so that to foster an effective
integration between even heterogeneous data.
In view of this scenario, Istat, the Italian National Institute of Statistics, and
AgID launched a joint project whose objectives can be summarised as follows:
– to model “official” classifications such as Ateco 2007 (Classification of Eco-
nomic Activity) and COFOG (Classification of the Functions of Government)
in LOD using standard ontologies (e.g., SKOS - Simple Knowledge Organi-
zation System, XKOS - eXtended Knowledge Organization System, ADMS
- Asset Description Metadata Schema, etc);
– to certify data by provenance using the PROV framework. In particular, the
framework has been used to document the overall process of the creation of
the classifications by Istat, as well as of the creation of their LOD versions
and of their publication on the SPARQL endpoint by AgID.
The project also helped AgID and Istat to delineate guidelines and artefacts
for LOD publication that could raise the awareness on the need to certify data
quality and reliability, thus enabling an effective data reuse and interoperability
in the Italian PSI context.
Similar projects have been carried out by other institutions, such as the
publication of official classifications by INSEE [5], or the publication of the
NACE official classification by the European Community [9].
The paper describes this project and the data model that has been adopted
and implemented in LOD for the specific Ateco 2007 standardized classification.
The remaining of the paper is structured as follows. Sections 2 and 3 provide
the background on Ateco 2007 and PROV, respectively. Section 4 introduces the
data modelling in LOD of the Ateco classification and Section 5 concludes the
paper.
2 Background: Official Classifications and ATECO 2007
Classifications have been one of the first metadata set that NSIs started to
store, with the objectives of (i) reusing them in different production processes
and (ii) promoting harmonization. The main result was the Neuchâtel Group
that issued version 2.1 of the Neuchâtel Terminology Model Classification [10]
database object types and their attributes in 2004. The principal purpose of the
work was to arrive at a common language and perception of the structure of
classifications as well as of the links between them.
More recently, the Generic Statistical Information Model (GSIM) [4] was
proposed with the objective to describe the information objects and flows in the
statistical business process: in the Concepts module, classifications are described
in detail. Some of the elements characterizing GSIM classifications are:
– A Classification Family is a group of Classification Series. Classification Se-
ries is an ensemble of one or more Statistical Classifications.
– A Statistical Classification is a set of Categories. The Categories at each
Level of the classification structure must be mutually exclusive and jointly
exhaustive of all objects/units in the population of interest.
– A Statistical Classification has Categories that are represented by Classifica-
tion Items. These Classification Items are organised into Levels determined
by the hierarchy. A Level is a set of Concepts that are mutually exclusive
and jointly exhaustive.
– Statistical Classifications can be versions or variants. A variant type of Sta-
tistical Classification is based on a version type of Statistical Classification. In
a variant the Categories of the version may be split, aggregated or regrouped
to provide additions or alternatives to the standard order and structure of
the original Statistical Classification.
Figure 1 shows a part of the GSIM model schema representing the above men-
tioned concepts.
The Classificazione delle ATtività ECOnomiche (ATECO) [1] is the Italian
counterpart of the NACE [9] classification. Its series is composed of the different
versions: 2002, 2007, and so on every time there is a new definition. Each of these
versions is organized into levels. As far as the ATECO-version 2007 is concerned,
its levels are: sezioni, divisioni, gruppi, classi, categorie and sottocategorie. Up
Fig. 1. GSIM Model Schema
to the level classe, it coincides with the NACE classification, which is a modifi-
cation of the ISIC Rev. 4 [6] managed by the United Nations. Variants of these
classifications managed in the Istat Sistema Unitario dei Metadati (SUM) are
those used for specific purposes in Istat with the following distinctions:
– Variants organized by specific Istat systems (e.g. the system devoted to dis-
semination, the one devoted to data collection and so on). Each of these
variants include additional categories, as the codes in the Main Industrial
Groupings.
– Variants used in specific data structures (either of macro or micro data) in
order to show the level of detail in which the dimension Economic Activity
is given. For instance some disseminated data use all the codes of the first
ATECO level, another a subgroup of them and some Main Industrial Group-
ings, another one focuses on one or two codes of the first ATECO level and
then decomposes it up to the the last ATECO level.
3 Background: PROV and XKOS Ontologies
As mentioned, standardizing a classification is a complex process that involves
different actors: the PROV ontology described in Section 3.1 helps tracking this
aspect.
Moreover, statistical classifications are a specific type of classifications that
needs to be modeled in an ad-hoc way. This is described by the focus on the
XKOS ontology in Section 3.2.
3.1 PROV Ontology
Provenance information is relevant to certify data quality and reliability. In
more detail, it is very important to publish, together with data, who is the
responsible for them and which are the entities, activities and agents involved
in the generation/manipulation processes; namely, the following concepts:
– Responsible of the data: person/institution/administration that man-
ages/creates/manipulates the data.
– Certified data: i.e. data published by their responsible.
– Provenance: set of detailed information regarding entities and processes in-
volved in the production and publication of data.
We tested the PROV Ontology [12], a W3C recommendation, to certify the role
of Istat as official producer of ATECO 2007 classification published as linked
open data.
Fig. 2. Provenance Ontology Model Schema
In Figure 2, the principal elements of the PROV data model are shown. The
PROV Ontology (PROV-O) is an OWL2 ontology that expresses the PROV
Data Model (PROV-DM), which provides a set of classes, properties, and re-
strictions to represent provenance information. A PROV framework is available
and is composed by a set of documents describing different aspects of the prove-
nance issue. In detail, the framework consists of the following documents:
– PROV-DM: describes the data model; that is, entity, activity and agent
concepts.
– PROV-O (PROV-Ontology): describes the data model using the OWL2 lan-
guage.
– PROV-N: describes the data model using the human-readable notation N3.
– PROV-XML describes the data model using the XML notation.
– PROV-Constraints: describes the integrity constraint for writing correctly
the provenance information using the PROV ontology.
– PROV-Sem: describes the PROV data model semantic.
– PROV-Dictionary: defines an extension of the PROV-DM for collections and
dictionaries.
– PROV-Links: describes an extension of the PROV-DM for the correct de-
scription of multiple data sources.
– PROV-AQ: describes some methods to access and query data.
– PROV-DC: allows to referencing provenance data expressed in the Dublin
Core Ontology.
The specific usage we made of the PROV ontology is described in Section 4.
3.2 XKOS Ontology
XKOS (eXtended Knowledge Organization System)[2] is an extension of SKOS
(Simple Knowledge Organization System) [13] for managing statistical classifi-
cations and concept management systems.
With respect to SKOS, XKOS enables the representation of statistical clas-
sifications with their structure and textual properties, as well as the relations
between classifications. Moreover, XKOS refines SKOS semantic properties al-
lowing the usage of more specific relations between concepts for describing statis-
tical classifications. In more detail, SKOS concepts are defined from the point of
view of a thesaurus, thus it only defines the following relationships: (i) broader,
(ii) narrower, and (iii) related to. Otherwise, statistical classifications rely on
hierarchical relations (generic-specific and whole-part), thus XKOS introduces
the definition of these relations structuring data into levels; a level corresponds
to all those concepts that are at the same distance from the top of the hierarchy.
Finally, XKOS defines causal, sequential, and temporal relations. See Figure 3
showing the XKOS concepts.
4 Modeling the Classification
This section describes the process that we conducted in order to create the
LOD version of the official classification Ateco 2007 we call LinkedAteco2007,
Fig. 3. XKOS Ontology Model Schema
Note that, the same process has been also applied to another classifica-
tion named COFOG that has been recently published by AgID in collabora-
tion with Istat. Both classifications are available at AgID SPARQL endpoint:
http://spcdata.digitpa.gov.it/.
The activity involved the use of different ontologies and vocabularies and
a customization of existing models. It is worth noticing that this activity was
also described as a best practice within the Italian national guidelines for PSI
valorization in order to guide PAs in (i) certifying the provenance of their data
(crucial especially in the collaboration between the local and central levels of
government), and (ii) using standard and common ontologies when describing
their data.
The following subsections detail the activity carried out by Istat and AgID.
4.1 Provenance Modeling
In order to deal with the complexity of the process of standardizing classifica-
tions, in our modeling we gathered as much of the information related to such
a process as possible, leaving in any case to other users the flexibility to extend
the classification with variants and further versions. In this respect, the first
phase of the modeling was to represent the provenance of the data that form the
LinkedAteco2007. Figure 4 shows the resulting diagram that includes the activ-
ities, entities and actors involved in the process, all modeled through the PROV
ontology. From the diagram, we can observe that the activity of publishing the
LinkedAteco2007 (i.e., Generation Linked Ateco2007 ) was carried out by two
different actors: Ufficio RST/B and AgID. Ufficio RST/B is an organizational
unit of the DCIT belonging to ISTAT. The diagram also specifies that AgID
is the publisher of the LinkedAteco2007, whereas Ufficio RST/B is the creator.
In addition, the original classification was influenced by the Eurostat classifica-
Fig. 4. Provenance Modeling
tion NACE2006v2 and derived from the Ateco2002, a previous variant of the
Ateco classification. All the Ateco classifications are grouped together through
the Ateco collection.
The model is sufficiently flexible to allow an external user (e.g., a local admin-
istration) to extend the Ateco2007 for its own purposes (e.g., add a non-existent
activity): in this case, the user can define a variant from the LinkedAteco2007
as illustrated by the Variation Ateco 2007 box at the bottom of Figure 4.
4.2 Classification Distribution
To enrich the metadata of the classification, we exploited the use of standard
and well-known ontologies. Figure 5 illustrates the metadata enrichment. In par-
ticular, we used two ontologies; namely, DCAT3 and ADMS4 .The former allows
us to insert the classification as dataset of our data catalogue and to decouple
the abstract entity notion of dataset from its actual implementation. This also
allows us to state the fact that the conceptual model of the dataset is expressed
using the RDF framework and that we have produced such classification in dif-
ferent formats (e.g., RDF/N3, RDF/XML, etc.). These different productions are
instances of the class dcat:Distribution and some of them are downloadable.
3
http://www.w3.org/TR/vocab-dcat/
4
http://www.w3.org/TR/vocab-adms/
Fig. 5. Classification Distribution
The ADMS ontology allows us to specify and remark that the classification is
a semantic asset, since it can be effectively used as integration element between
different data, thus enabling semantic interoperability.
Finally, Figure 5 shows that the LinkedAteco2007 entity is a Taxonomy rep-
resented using SKOS (see next subsection).
4.3 Classification Modeling
To model the content of the classification we used the wide-used ontology
SKOS [13] and its extension for statistical data XKOS [2].
SKOS allows us to express that LinkedAteco2007 is a ConceptSchema and
XKOS allows us to represent the full hierarchy of classification levels (i.e., the
instances of the class xkos:ClassificationLevel ). Each level of the classification is
defined by its depth (i.e., xkos:depth) and is connected to all its member (i.e.,
skos:member ).
Figure 7 completes the description of the classification by showing how each
member is described. Specifically, a member has a notation (i.e., skos:notation),
a label (i.e., skos:prefLabel ), a textual description in the attributes note (i.e.,
skos:note) and a comment (i.e., skos:comment). It is worth seeing that every
element is explicitly related to the upper level member; that is, it is a specification
expressed through skos:specializes, and to the lower level member; that is, it is
a generalization expressed through skos:generalizes.
Fig. 6. Classification Modeling using SKOS and XKOS
Implementation. In general the implementation process of the previously de-
scribed modelling adheres to a methodology proposed and used by AgID in other
LOD projects [15]. In particular, the implementation consisted in the following
four steps:
1. import the tabular data (i.e., a CSV file) from the public available data
source5 in a relational database;
2. clean and prepare the data (for instance adding new attributes with com-
posed existing fields) for processing;
3. model the RDF data following the diagrams illustrated above and trans-
form them accordingly using common tools. In our case, we used the D2RQ
framework [14].
4. publish the resulting classification on the Web portal SPCData6 and on the
corresponding linked data cloud, and making available the LOD dataset for
querying through the SPCData SPARQL endpoint.
5 Conclusions
The paper describes a real project by Istat and AgiD related to the publication of
Official Classifications as LOD in the Italian Public Sector context. The technical
5
http://sistemaclassificazioni.istat.it/class/sistemaclassificazioni/index.php
6
http://spcdata.digitpa.gov.it/classificazioni.html
Fig. 7. Classification Levels with XKOS
contributions of the paper is focused on the modeling aspects of the classifications
by making use of ontologies that are specific of the statistical domain, like XKOS,
as well as of more general purpose ontologies, like PROV.
Besides technical contributions, the paper also provides a relevant method-
ological contribution to foster data interoperability among different institutions.
Indeed, data interoperability is made possible by twofold efforts:
– shared formats and models, i.e., technological standards like RDF framework
and ontologies like XKOS.
– content harmonization, i.e., common domain-specific information concepts.
We think that publishing OS classifications in LOD is a first important step to-
wards “content” harmonization, which can consistently speed up the cooperation
among different institutions based on data sharing and exchanges.
References
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charter-and-technical-annex
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