Open Data for Improving Youth Policies

Domenico Beneventano, Sonia Bergamaschi, Luca Gagliardelli and Laura Po

Dipartimento di Ingegneria ”Enzo Ferrari”, Universit

a di Modena e Reggio Emilia, Modena, Italy

Keywords:

Open Government Data, Linked Open Data, Youth Policies, Emilia Romagna Region, Municipality of

Modena.

Abstract:

The Open Data philosophy is based on the idea that certain data should be made available to all citizens, in an

open form, without any copyright restrictions, patents or other mechanisms of control. Various government

have started to publish open data, ﬁrst of all USA and UK in 2009, and in 2015, the Open Data Barometer

project (www.opendatabarometer.org) states that on 77 diverse states across the world, over 55 percent have

developed some form of Open Government Data initiative. We claim Public Administrations, that are the

main producers and one of the consumers of Open Data, might effectively extract important information by

integrating its own data with open data sources. This paper reports the activities carried on during a one-

year research project on Open Data for Youth Policies. The project was mainly devoted to explore the youth

situation in the municipalities and provinces of the Emilia Romagna region (Italy), in particular, to examine

data on population, education and work. The project goals were: to identify interesting data sources both from

the open data community and from the private repositories of local governments of Emilia Romagna region

related to the Youth Policies; to integrate them and, to show up the result of the integration by means of a

useful navigator tool; in the end, to publish new information on the web as Linked Open Data. This paper also

reports the main issues encountered that may seriously affect the entire process of consumption, integration

till the publication of open data.

1 INTRODUCTION

In recent years, the number of international confer-

ences on Open Government and Open Data has been

increasing. Showing that the attention of governments

to the transparency and the interest of the scientiﬁc

and economic community to exploit these data is rais-

ing.

Many governments now release large quantities

of data into the public domain, often free of charge

and without administrative overhead. These data are

called Open Government Data (OGD). OGD has let

to several improvements, such as transparency and

democratic control, citizen participation, innovation,

improved efﬁciency and effectiveness of government

services, impact measurement of policies, the creation

of new knowledge from combined data sources and

patterns in large data volumes.

The powerful of open data is that many areas can

take advantage from their value. And also many dif-

ferent groups of people and organizations can beneﬁt

from the availability of open data, including govern-

ment itself.

In Italy, in recent years, the open data approach

has been adopted by a growing number of Public Ad-

ministrations and, in some cases, an additional effort

has been made to supply Linked Open Data (LOD).

On June 2013, the leaders of the G8 signed an

agreement committing to advance open data in their

respective countries. In Italy a number of open data

activities started. At the beginning of June 2015, the

open data portal of the italian public administration

which since 2011 hosts a catalog of open data pub-

lished by ministries, regions and local authorities, has

been revamped in order to promote transparency, ac-

countability, diffusion and reuse of open data.

Among all the open datasets, the Emilia Romagna

Region is present with 642 datasets

and 14 open

data portals. The Emilia Romagna Region has nine

provinces (Piacenza, Parma, Reggio Nell’Emilia,

Modena, Bologna, Ferrara, Forl

ı-Cesena, Rimini,

Ravenna) and 340 municipalities. The open data por-

tals of the Emilia Romagna region include one re-

gional

, eight municipalities (Piacenza, Bologna, An-

www.dati.gov.it

information available on dati.gov.it at 31st July 2015

http://dati.emilia-romagna.it/

118

Beneventano, D., Bergamaschi, S., Gagliardelli, L. and Po, L..

Open Data for Improving Youth Policies.

In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015) - Volume 2: KEOD, pages 118-129

ISBN: 978-989-758-158-8

zola Emilia, Ferrara, Ravenna, Faenza, Cesena, Rim-

ini), three provinces (Parma, Bologna, Forl

ı-Cesena )

and the public transport company (Passenger Trans-

port Emilia-Romagna).

This paper describes the activities performed dur-

ing a one-year research project on Open Data for

Youth Policies. The project is called “Open Linked

Data of the youth observatory of the Emilia-Romagna

Region” and has been funded by the Municipality of

Modena. However, the main dimensions of analy-

sis of the project concern all the municipalities and

provinces of the Emilia Romagna region.

The project goals were: to identify interesting

data sources both from the open data community and

from the private repositories of local governments of

Emilia Romagna region related to the Youth Policies,

in particular, to the topics of population, education

and work; to integrate them and, to show up the re-

sult of the integration by means of a useful navigator

tool; in the end, to publish new information as Linked

Open Data.

We experienced practical problems in selecting,

using, integrating heterogeneous data and building a

set of concrete metrics to assess the quality of dis-

closed data. Moreover, it was challenging to support

the transition towards LOD.

There are many Open Data initiatives in Italy for

publich Open Data, for example: OpenCoesione

and

Open Bilanci

that show the public administration ex-

penses. In these websites the user can view ﬁxed indi-

cators, chosen by the application’s but he cannot com-

bine the indicators as he likes. In these open dataset

it is not possible for the user to perform customized

searches, morever, the data can be navigated only sep-

arately, in example, the user cannot integrate data on

population with data on provided fundings. On the

contrary our work permits to integrate different data

sources, thus correlating open data sets.

The project has been developed in four phases.

The ﬁrst phase was devoted to a deep and wide analy-

sis of the available data sources (local, regional, na-

tional and international) in order to individuate the

most relevant ones. The second phase made use of

the open source data integration system, MOMIS

to integrate the selected data sources in virtual global

views. The third phase provided an easy-to-use dash-

board, i.e. MOMIS dashboard, to visualize the in-

formation emerging from aggregated data. Finally,

the fourth phase aimed to make the resulting value-

added information, public and searchable on the Web

as Linked Open Data.

http://www.opencoesione.gov.it/

http://www.openbilanci.it

http://www.datariver.it/data-integration/momis/

The key partners in this project were: the Munic-

ipality of Modena, the Department of Culture, Youth

and Policies for the Legality of the Emilia Romagna

region and the DBGroup

The remainder of the paper is structured as fol-

lows. Section 2 illustrates the set of tools we used

to reach the project goals. The next four sections de-

scribe the phases of the project. Finally, Section 7

sketches the conclusion and the main difﬁculties faced

during the project.

2 TOOLS

The data integration process was performed with

MOMIS, a data integration system developed by the

DBGroup (Beneventano et al., 2003) of the University

of Modena and Reggio Emilia and now distributed by

the DATARIVER spin-off

. The integration results of

the project have been shown by the MOMIS Dash-

board, a web application developed by Datariver8.

For the publication and navigation of Linked open

Data, the D2R Server (Bizer and Cyganiak, 2007) and

the LODeX tool (Benedetti et al., 2014a; Benedetti

et al., 2014b) have been used.

The MOMIS Data Integration System

In the following, we brieﬂy present the MOMIS’s ar-

chitecture, a more detailed description can be found in

(Beneventano et al., 2003) (Bergamaschi et al., 2001).

Figure 1: The MOMIS Data Integration Process.

Given a set of heterogeneous and distributed data

sources MOMIS generates in a semi-automatic way

a uniﬁed schema called Global Schema (GS), that al-

lows users to formulate queries on that schema like

they are querying a single database. The system per-

forms the integration task, by following an Global-

As-View (GAV) approach for creating the mappings

between the GS and local schemas of the integrated

http://dbgroup.unimo.it/

http://www.datariver.it

Open Data for Improving Youth Policies

119

data sources. MOMIS uses a virtual approach for

achieving an integration that preserves the autonomy

and security of the local sources.

The integration process is composed of four main

phases:

1. Local Schema Acquisition: (Figure 1-1) the ex-

traction of Local Source Schemas is performed by

wrappers that automatically extract the schema of

each local source and convert it into the common

language ODL

2. Local Sources Annotation: (Figure 1-2) the de-

signer can perform automatic annotation and/or

can manually select a base form and the appropri-

ate WordNet meaning(s) (i.e. synset(s)) for each

term. Moreover, the designer can extend Word-

Net with Domain Glossaries. Annotation consists

in associating to each class and attribute name,

one or more meanings w.r.t. a common lexical ref-

erence, i.e. domain glossaries/WordNet

(Miller

et al., 1990). The Local Source Annotation phase

is performed by the Global Schema Designer tool

(see Figure 2)

3. Semantic Relationships Extraction: (Figure 1-

3) starting from the annotated local schemas,

MOMIS derives a set of intra and inter-schema

semantic relationships in the form of: synonyms

(SYN), broader terms/narrower terms (BT/NT)

and related terms (RT) relationships. The set of

semantic relationships is incrementally built by

adding: structural relationships (deriving from

the structure of each schema), lexical relation-

ships (deriving from the element annotations,

by exploiting the WordNet semantic network),

designer-supplied relationships (representing spe-

ciﬁc domain knowledge) and inferred relationship

(deriving from Description Logics equivalence

and subsumption computation). The Semantic

Relationship Extraction phase is performed by the

Global Schema Designer tool (see Figure 2).

4. GS Generation: (Figure 1-4) starting from the

discovered semantic relationships and the local

sources schemas, MOMIS generates a GS con-

sisting of a set of global classes, plus a corre-

sponding set of Mapping Tables which contain the

GAV mappings connecting the global attributes

of each global class with the local source at-

tributes. The GS generation is a process where

WordNet is a thesaurus for the English language, that

groups terms (called lemmas in the WordNet terminology)

into sets of synonyms called synsets, provides short deﬁni-

tions (called gloss), and connects the synsets through a wide

network of semantic relationships.

classes describing the same or semantically re-

lated concepts in different sources are identiﬁed

and clustered into the same global class. The de-

signer may interactively reﬁne and complete the

proposed integration result through the GUI pro-

vided by the Global Schema Designer tool. In par-

ticular, she/he can: modify the proposed global

classes and mappings; select the appropriate Join

function for each global class; deﬁne Transforma-

tion Functions to transform the local attribute val-

ues into the corresponding global attribute values

and solve possible data conﬂicts through the deﬁ-

nition of Resolution Functions. Resolution Func-

tions are applied to each global attribute to solve

conﬂicts arising from different values of local at-

tributes mapped into the same global attribute.

Figure 2: The MOMIS Architecture.

Finally, once obtained the desired integration re-

sult, a user can pose queries on the GS by using the

Query Manager tool (see Figure 2). As MOMIS fol-

lows a GAV approach, the query processing is per-

formed by means of query unfolding. The query un-

folding process generates for each global query (i.e. a

query on the GS) a Query Plan composed by a set of

queries:

• a set of local queries that have to be executed

on the local sources simultaneously by means of

wrappers,

• a mapping query for merging the partial results

(deﬁned by means of the join function),

• a ﬁnal query to apply the resolution functions and

residual clauses.

MOMIS provides another query capability, the

Query Manager Web Service, which permits to

easily integrate MOMIS with other applications (e.g.

Business Intelligence solutions) and a user-friendly

Web Application, i.e. the MOMIS Dashboard (Figure

2) to easily visualize synthetic information.

More information on MOMIS and exam-

ples can be found on DATARIVER website at

KEOD 2015 - 7th International Conference on Knowledge Engineering and Ontology Development

120

the following URL http://www.datariver.it/data-

integration/momis/tutorials/

For showing the integration results of our project,

we used the MOMIS Dashboard web application de-

veloped by Datariver8.

The MOMIS Dashboard

The advantages of using visualization tools to explore

the correlation among data are numerous (Tufte and

Graves-Morris, 1983). The analysts do not have to

learn any sophisticated method to be able to inter-

pret the resulting graphs. Effective visualizations help

users in analyzing and reasoning about data and evi-

dence and make complex data more accessible, un-

derstandable and usable.

The MOMIS Dashboard is a interactive visualiza-

tion tool that offers several views on a set of data. It

makes easier to compare data and capture useful in-

formation. It allows to ﬁlter the data and visualize the

results through different charts. In particular, it is pos-

sible to display line charts (for showing trends), bar

charts, pie charts, bubble charts on a Google Maps,

or show the data in a tabular view. Figure 3 presents

some examples of charts that can be generated with

the MOMIS Dashboard.

Figure 3: Different charts created by using the MOMIS

Dashboard.

The D2R Server and the LODeX Tool

In order to publish in the LOD cloud the project re-

sults, we needed a tool for mapping a relational source

in RDF. The W3C RDB2RDF Incubator Group (Sa-

hoo et al., 2009) had the mission to examine and clas-

sify existing approaches to mapping relational data

into an RDF source. The tools for automatic mapping

generation deﬁne a set of mappings between RDB and

RDF namely: an RDB record is a RDF node, the col-

umn name of an RDB table is a RDF predicate and an

RDB table cell is a value. Among these tools, we se-

lected D2RQ (Bizer and Cyganiak, 2007) as it allows

users to deﬁne customized mappings.

Once a LOD dataset is available, a tool to nav-

igate, explore and query it is necessary. We ex-

ploited LODeX(Benedetti et al., 2014a; Benedetti

et al., 2014b), a tool able to provide a summary of

a LOD source starting from scratch, thus supporting

users in exploring and understanding the contents of

a dataset. Moreover, LODEX provides a visual query

interface to easily compose queries, that are automat-

ically translated in Sparql and executed on a LOD

source.

3 DATA SOURCE SELECTION,

EXTRACTION AND CLEANING

In the ﬁrst phase of the project, we focused on the

selection of the most relevant data sources w.r.t the

main dimensions of analysis of the project, i.e. mu-

nicipalities and provinces of the Emilia Romagna Re-

gion. We have used and analyzed both proprietary and

open data sources, the proprietary data sources was

provided by the Emilia Romagna region, the sources

were:

• A Database of Current Spending Projects: it

contains information about the fundings provided

for projects on youth populations actuated in the

different provinces of the region;

• An Excel File of Capital Spending Projects: it

contains information about funding provided for

long period investments (e.g. build structures,

equipments);

• A database of Social Centers (SAG - “Spazi di

Aggregazione Giovanile”) : it lists all the places

where young people get together or places where

recreational activities for youth are organized;

• Three Excel Files Related to the Youth Infor-

mation Project (“Progetto Informagiovani”):

The information centers provide data at local, na-

tional and international level on different topics of

interest for young people aged 13 to 35 years. The

main areas covered are relate to study, work, con-

tinuing education, travel and holidays, study and

work abroad, leisure, social life and health. These

ﬁles contain statistics about the number of visitors

of the website

, the number of points on the re-

gion and the number of editors;

• An Excel File about the Young ER Card

Project: YoungERcard

is the new card designed

by the Emilia Romagna region for young peo-

ple aged between 14 and 29 residents, students

or workers in Emilia Romagna. The card is dis-

tributed for free and reserve holders a series of fa-

cilities for the enjoyment of cultural and sportive

informagiovanionline.it

https://www.youngercard.it/

Open Data for Improving Youth Policies

121

Figure 4: Conceptual schema of the Regional Statistical Service.

events and discounts at various shops. The ﬁle re-

ports information related to the Young Emilia Ro-

magna Card distribution and extensiveness in the

region.

Regarding open data sources, several sites and

portals that publish information regarding the youth

population have been investigated. Not all sources

have revealed to be of interest/usable for the project

and thus have not been included in the data sources to

be integrated.

• ISTAT - the Italian National Institute of Statis-

tics

provides different thematic datasets. We fo-

cused on some areas of interest: education and

work. In the education dataset information re-

garding the number of students per type of school

(primary, secondary, etc.) are provided, while

in the work dataset the rate of employment, un-

employment and wages are presented. Although

these data were useful for the project goals, they

were not included due to the coarse granularity

of the data, indeed, the minimum level of detail

available was at the regional level.

• Alma Graduate - The Alma Graduate website

yields information on the proﬁle of the graduates

and their employment status. The proﬁle of a

graduate student shows information such as de-

mographics, the study path and other statistics;

while the employment status reports data on the

type of job, salary, satisfaction, etc. These data

could have been useful for the project, but were

not included as they are not free accessible and

downloadable (data collected by AlmaLaurea are

usually sold to companies).

www.istat.it

www.almalaurea.it/universita/statistiche

• “Il Mulino” Youth Report - The database of the

Youth Report contains data derived from the sur-

vey conducted on a sample of 9000 youth aged

from 18 to 29. The survey concerns the values,

expectations, projects and life choices of young

people, their trust in institutions, their role in civil

society and the relationship between the genera-

tions. For each theme, you can choose a question-

naire, in which it is granted the choice by region,

gender, age, marital status and educational qualiﬁ-

cation. This source was not included as the survey

questions do not match our goals and, moreover,

it is difﬁcult to meaningfully integrate data from

poll and from statistics (due to the different level

of reliability).

• “Orienter” - Database of Training Courses -

This source

collects information of all training

courses ﬁnanced or authorized by the Emilia - Ro-

magna Region. The source was not included, be-

cause it shows only the active courses and it was

not possible to access historical data.

• Emilia Romagna Labor Statistics - This web-

site

presents some data on the labor market of

the Emilia - Romagna region. Employment data,

trends in the labor markets and social security

beneﬁts are collected and periodically updated.

By analyzing the source, we realized that the pub-

lished data are a rearrangement of other sources,

in particular, several data are taken from the IS-

TAT portal.

• Emilia Romagna Statistical Service - This web-

http://orienter.regione.emilia-romagna.it

http://formazionelavoro.regione.emilia-

romagna.it/analisi-sul-mercato-del-

lavoro/approfondimenti/statistiche-sul-lavoro-in-emilia-

romagna

KEOD 2015 - 7th International Conference on Knowledge Engineering and Ontology Development

122

Figure 5: Conceptual schema of the National Student Register.

site

is divided into several thematic sites that re-

port information on population, transport, sports,

productive sectors, etc.. For our project, we fo-

cused on the “population” and “education” sites.

The population website

contains data organized

on the basis of different measures (age, sex, year

of the survey, place ...). The education web-

site

reports the number of schools, the number

of classes and the number of students per type of

school and province. The source was selected as

a relevant source, a detailed analysis was made to

understand how the data were structured (see the

analysis in the following).

• National Student Register - MIUR - This reg-

ister

, provided by the Italian Ministry of Edu-

cation, University And Research, contains infor-

mation on the number of students and graduates

in the various degree courses of Italian universi-

ties. The source was selected as a relevant source

for the project, a detailed analysis was made to

understand how the data were structured (see the

analysis in the following).

Our work of selecting sources and extracting rel-

evant data was driven by few relevant questions aris-

ing by the local and regional politicians: “how many

funding was provided in any province compared to

the number of youth residents (aged 15-34)?”, “which

is the higher education rate in each province?”. These

questions have to be answered in an historical per-

spective, thus monitoring the data of each province

http://statistica.regione.emilia-romagna.it/

servizi-online/

http://statistica.regione.emilia-romagna.it/

servizi-online/statistica-self-service/popolazione/

popolazione-per-eta-e-sesso

http://statistica.regione.emilia-romagna.it/

servizi-online/statistica-self-service/istruzione/

http://anagrafe.miur.it

over the years. To effectively answer these questions,

we selected, among the available sources, the ones

that supply the number of young residents in each

province and their level of education. The data on the

youth population have been extracted from the Emilia

Romagna Statistical Service, while the data on edu-

cation have been extracted from the National Student

gated by province and year, therefore a preliminary

study on which dimensions were provided on these

data sources was needed. Figures 4 and 5 show the

conceptual schema of the two sources. As you can

see, the sources have different dimensions and dif-

ferent level of granularity, thus we needed to select

appropriate dimensions to allow a successful integra-

tion.

From the Emilia Romagna Statistical Service, we

extracted the number of male, female and total popu-

lation with respect to the following dimensions: Year,

and Province. From the National Student Register,

we extracted the number of matriculates, graduates

and registered with respect to the following dimen-

sions: Academic Year, and Course Province. Since

the academic year is described by a couple of years

(e.g. 2012/2013), we applied a conversion function

to transform it to a single year. Our decision was

to take the ﬁrst part of the academic year for rep-

resent the enrolling year (usually people enroll in

the ﬁrst part of the academic year) and the second

part to represent the year of graduation (usually stu-

dents get their degree in the second part of the aca-

demic year). Some naming conﬂicts occur on the

province names. The main problems were found

on the provinces of Reggio Nell’Emilia and Forl

ı-

Cesena that were written in different ways, for exam-

ple “Reggio Emilia”, “Reggio-Emilia”, “Forl

ı e Ce-

sena”, “Forli-Cesena”. For solving this problem, we

choose as golden standard the names used on the Re-

Open Data for Improving Youth Policies

123

gional Statistical Service, namely “Forl

ı-Cesena” and

“Reggio Nell’Emilia”, and we converted any other

forms to the gold standard.

The data were extracted and saved in a set of

MySQL tables. From the Regional Statistical Service,

we import data into a table with province, year, num-

ber of young residents (aged 15-34 years) attributes;

from the National Student Register we import data

into two distinct tables with province, year, number

of enrolled/graduated students.

4 DATA INTEGRATION

At the end of the ﬁrst phase of data extraction and

transformation, we had the following data sources:

S1. Current Spending Projects: information about

funding provided for projects on youth popula-

tions actuated in the different provinces of the re-

gion;

S2. Capital Spending Projects: information about

funding provided for long period investments (e.g.

build structures) organized for each province and

year;

S3. Number of Youth Residents: number of youth

residents in each province of the region Emilia

Romagna and for each year;

S4. Number of Youth Information Centers: num-

bers of points of the Youth Information Centers

located in each province and year.

S5. Number of Editors in the Youth Information

Centers: number of member in the editorial staff

operating in the Youth Information Centers for

each province and year.

S6. Number of Web Site Visitors Informagiovan-

ionline: information about the number of the vis-

itors to the website Informagiovanionline for each

province and year.

S7. Young ER Card: data on the Young ER Card for

each province and year.

S8. Number of Graduates Supply by MIUR: infor-

mation about the number of graduated for each

province and year.

S9. Number of Matriculations at the University

Supply by MIUR: information about the number

of matriculations for each province and year.

All these sources have two dimensions in com-

mon: the year and the province. We conducted two

analysis for evaluating the data coverage over the

two dimensions. We found no lack of data over the

provinces, meaning that the data are spread in all the

provinces. Instead, focusing on the entire interval of

years (2006-2014), we found a relevant number of

missing data, that is reported in Table 1.

Table 1: Data coverage over the years. The red cells repre-

sent missing data, the green represent that data are present.

Source

Years

2006

2007

2008

2009

2010

2011

2012

2013

2014

Since MOMIS allows the creation of more Global

Schemas (for more information see chapter 2), to

avoid a proliferation of null values in the integration

result, we created three different Global Schemas:

• GS-Projects: this GS considers only S1, S2, S3,

S8, S9.

• GS-Global: this GS contains the data from all the

nine sources;

• GS-Projects-Informagiovani: this GS excludes

the Young ER Card from the integration;

4.1 GS-Projects

As shown in Table 2, only few sources includes data

over the entire period (2006-2014). In this case, we

have taken all data from ﬁve of the sources, in or-

der to create charts on the entire period that show the

correlation between founded projects, population and

education.

Table 2: Data coverage of the GS-Projects.

Source

Years

2006

2007

2008

2009

2010

2011

2012

2013

2014

4.2 GS-Global

The GS-Global is the only that includes data on the

Young ER Card, it contains all the nine sources, as

KEOD 2015 - 7th International Conference on Knowledge Engineering and Ontology Development

124

showed in Table 3. On the GS-Global, we created a

view (highlighted in blue in the Table 3) that it is used

in the MOMIS Dashboard and includes only data of

2014.

Table 3: Data coverage of the GS-Global.

Source

Years

2006

2007

2008

2009

2010

2011

2012

2013

2014

4.3 GS-Projects-Informagiovani

Table 1 highlights that the Young ER Card (S7) have

data only for the 2014. Therefore, in the GS-Projects-

Informagiovani we included all sources except S7, as

showed in Table 4. On this integration, we were in-

terested to show the correlations between the data of

the Informagiovani project and the data of education

and founded projects; thus we created a view on the

period 2009 - 2013, where most of these sources have

available data.

Table 4: Data coverage of the GS-Projects-Informagiovani.

Source

Years

2006

2007

2008

2009

2010

2011

2012

2013

2014

5 DATA VISUALIZATION

We designed several charts on the MOMIS Dashboard

in order to answer at the questions provided from the

politicians of the Emilia Romagna region.

The ﬁrst questions were focused on the funding

compared to the number of youths: “Are the funding

Figure 6: Fundings and youth population trends in the

province of Piacenza from 2006 to 2014.

Figure 7: Funded projects/areas in the province of Modena

on 2009.

provided for each province proportional to the num-

ber of youth?”, ‘How do they evolve over time?’

To visualize the answers of these questions, a bar

chart and a bubble chart on Google Maps were cre-

ated. These charts are activated when the user ﬁlters

the data by selecting one or more provinces and a sin-

gle year. Moreover, we created a line chart that is ac-

tivated in case the user selects one or more provinces

and more than one year; these charts show a compari-

son between the youth population and the investments

for each province.

Figure 8 shows the bar chart for the 2014 year,

here you see in dark green the fundings for the capital

spending projects (in euro), in red the fundings for the

current spending projects (in euro) and in light green

the number of youth residents.

Figure 8: A bar chart comparing fundings and youth popu-

lation.

The same result can be shown with a different vi-

sualization in a bubble chart on a map (Figure 9).

In this case, the number of youth is in light green,

Open Data for Improving Youth Policies

125

the fundings for the capital spending projects are dis-

played in red and the funding for the current spending

projects in blue. The map can be zoomed in and out.

Figure 9: A map comparing fundings and youth population.

Finally, it is possible to show over the time

how many fundings were provided in one or more

provinces in the years, and compare them to the num-

ber of youth.

For example, Figure 6 shows for the province of

Piacenza in the period 2006-2014 in light green the

number of youth resident, in dark green the founding

for capital spending projects (that were provided only

in 2012 and 2014), and in red the founding for the

current spending projects.

The fundings for projects are assigned on different

areas: education, culture, free time, etc. The politi-

cians were interested to see how the fundings were

distributed in the different areas. Thus, we devised a

pie chart that is activated when the user selects a sin-

gle province and a single year, that shows the funding

provided divided per area. Figure 7 shows the areas

that have been funded for the province of Modena on

2009.

Another question that later was arisen is: “How

many youth from each province visits the website

informagiovani.it compared to the number of resi-

dents?”.

Using statistical data from Google Analytics that

was provided by the region, we were able to create

the line chart showed in Figure 11. This chart reports,

for the 2013 year, the number of accesses from each

province in green, and the number of youth residents

in red. As it can be noted, the number of accesses

is really low compared to the number of youth, so

maybe it is possible to conclude that a more inten-

sive advertisement might increment the visits of the

website.

For comparison purposes, we evaluate Tableau

and Qlik

(cited as a leaders in the Gartner’s Magic

Quadrant for Business Intelligence and Analytics

http://www.tableau.com

http://www.qlik.com

Platforms (Sallam et al., 2015)). The Tableau Desk-

top, i.e. the tool to produce visualizations, is very in-

tuitive and the creation of new charts is very quick.

The same effectiveness appears in the Tableau reader,

i.e. the tool for anyone that consumes the visualiza-

tions. Instead, the Qlik platform requires more initial

knowledge to design the ﬁrst chart.

Tableau shows to the user different tabs that con-

tain one or more charts. In each tab, some ﬁlters

that allow the user to change the data represented in

the charts can be deﬁned. The mode of operation of

Tableau is opposite to the MOMIS dashboard. In the

MOMIS Dashboard, each tab contains only one chart,

and the charts are activated after the ﬁlters setting (e.g.

the selection of more than one year enables the line

chart).

Both Tableau and Qlik are able to generate the

same charts as the one available in the MOMIS dash-

board. In the scenario of our application, we observed

some limitations of the tools: the maps in Tableau can

not show more bubbles representing more measures

(differently from the MOMIS Dashboard see Figure

9); the pie chart can be generated on a single mea-

sure, thus the one in Figure 7 can not be generated

with Tabluea or Qlik; the line charts can not contain

series belonging to different dimensions (in this case,

the charts are rendered separately, as shown in Figure

10).

6 LINKED OPEN DATA

PUBLICATION AND

EXPLORATION

In the following, we describe the step by step LOD

publication and exploration process.

1. Select which portion of data to publish

2. Publish the dataset as Linked Open Data using

D2R Server and map the database schema to RDF

using the D2RQ Mapping Language.

3. Explore and query the dataset using the LODeX

tool.

Source Selection - The global view containing all

the integrated data (GS-Global), as well as, each of

the local sources have been selected for publication

as LOD.

Linked Open Data Publication - We used the

D2RQ Platform

for publishing the dataset in RDF.

The tool allows to access relational databases as

virtual, read-only RDF graphs, therefore it avoids the

replication of information into an RDF store.

http://d2rq.org

KEOD 2015 - 7th International Conference on Knowledge Engineering and Ontology Development

126

Figure 10: A comparison of line charts generated with different tools: Tableau, Qlik, MOMIS Dashboard. The charts show

the current spending projects, capital spending projects and the number of youth residents in each province during the years.

Figure 11: Number of accesses at the informagiovani.it

website (green) compared to the number of youth residents

(red) in each province.

D2RQ provides a SPARQL access to the content

of the database as Linked Data over the Web, and sev-

eral other opportunities like RDF dumps, API calls,

HTML views.

The declarative D2RQ mapping language is used

to deﬁne a set of mappings between the database

schema and the RDFS vocabulary or OWL ontology.

Another key aspect in publication of LOD is the

identiﬁcation of vocabularies that can be used to de-

scribe the dataset. In our case, we recognized two

potential vocabularies: GeoNames and DBpedia.

An example of D2RQ mapping to an external vo-

cabulary is reported below:

map:Statistics_anno a d2rq:PropertyBridge;

d2rq:belongsToClassMap map:Statistics;

d2rq:property dbpedia-owl:Year;

d2rq:propertyDefinitionLabel "anno";

d2rq:column "gs_totale.anno";

d2rq:datatype xsd:integer;

Here the column gs totale.anno is mapped to the

property dbpedia-owl:Year;.

We also use of the D2RQ mappings to link in-

stances of the global view (GS-Global) with instances

of the local sources, as in the following example.

map:Statistics_informagiovani

a d2rq:PropertyBridge;

d2rq:belongsToClassMap map:Statistics;

d2rq:property vocab:Informagiovani;

d2rq:refersToClassMap map:Informagiovani;

d2rq:join "gs_totale.provincia =

informagiovani.provincia";

d2rq:join "gs_totale.anno =

informagiovani.anno";

A speciﬁc procedure is needed to connect the in-

stances of our source with instances of other sources

in the LOD cloud.

In this case, we wanted to add geographical infor-

mation to our dataset, thus we linked to the Emilia Ro-

magnas provinces deﬁned in DBpedia and in GeoN-

ames.

We have created a table (see ﬁgure 13) contain-

ing references to instances of external sources and we

have included this table in our dataset.

By using these data, each time we refer to a

province in this table, we link the instance to the spe-

ciﬁc province in DBpedia and in GeoNames.

LOD Exploration and Querying - Once the

dataset is selected and the mappings deﬁned, D2RQ

automatically creates a SPARQL access to the LOD

source. By using tools such as LODeX(Benedetti

et al., 2014a)(Benedetti et al., 2014b) for browsing

and querying a LOD source, we can explore a graph-

ical representation of the source and exploit a conve-

nient visual query panel to extract information from

the dataset. The user can take advantage of the

Schema Summary produced by LODeX that repre-

sents the selected source (classes, properties and other

statistical information) and by picking graphical ele-

ments out of the Schema Summary, he/she can cre-

ate a visual query. The tool also supports the user

in browsing the results and, eventually, reﬁning the

query.

The prototype has been evaluated on the SPARQL

endpoint of the GS-Global and the visualization is

shown in Figure 12 and is available online at http://

dbgroup.unimo.it/lodex2/ok#!/schemaSummary/999.

7 CONCLUSION

We have shown the results gained during the “Open

Linked Data of the youth observatory of the Emilia-

Romagna Region” project .

Open Data for Improving Youth Policies

127

Figure 12: The visualization of the LOD youth policies dataset.

Table 5: Main issues handled during the project and their criticality (expressed through a three star rating).

Phase Operation Criticality

Data selection, extraction

and cleaning

Requirements deﬁnition of the datasets to be searched for ***

Discovery of relevant open data sources **

Investigation on how open data sources are structured and

what information they contain (most of the time the open

data sources do not provide a schema nor an high level

view of the data)

***

Discovery of the overlaps among different sources (het-

erogeneity in the format and in the granularity of the

available data might affect this task)

Data cleaning **

Data integration

Selection of the dimensions for the integration *

Availability of data in relation to the chosen dimensions **

Data displaying

Deciding of what data to show and to compare **

Selection of the more suitable chart formats to display the

data

Selection of ﬁlters to be applied on the data **

Linked Open Data publica-

tion and exploration

Build an ontology for describing the data *

Selection of tools to convert data into LOD format **

Choose whether it is convenient to publish data in a static

rdf ﬁle or to convert on the ﬂy data located in a relational

database

Looking for similar instances in external datasets (e.

Geonames or DBpedia) and building links

Figure 13: References to instances of external sources.

This paper has exempliﬁed how a Public Admin-

istrations can beneﬁt from the use of Open Data and

can effectively extract new and important information

by integrating its own datasets with open data sources.

We consider our work can be helpful for future

open government projects aiming to exploit and pub-

lish open data. To outline some guidelines, we iden-

tiﬁed some issues as important factors that may seri-

ously affect the entire process of identiﬁcation, con-

sumption, integration till the publication of open data.

Table 5 summarizes the main problems handled dur-

ing the project and their criticality.

ACKNOWLEDGEMENTS

This project has been realized thanks to the collabora-

tion of the Department of Culture, Youth and Policies

for the Legality of the Emilia Romagna region, the

Municipality of Modena (Italy), and the DBGROUP

of the University of Modena and Reggio Emilia.

We are thankful to Sergio Ansaloni, Paola Fran-

KEOD 2015 - 7th International Conference on Knowledge Engineering and Ontology Development

128

cia, Giulio Guerzoni, Marina Mingozzi, Fabio Poggi,

Antonio Volpone for many fruitful discussions on the

various aspects of integrating and releasing open gov-

ernment data covered in this paper.

REFERENCES

Benedetti, F., Bergamaschi, S., and Po, L. (2014a). On-

line index extraction from linked open data sources.

Linked Data for Information Extraction (LD4IE)

Workshop held at International Semantic Web Confer-

ence.

Benedetti, F., Bergamaschi, S., and Po, L. (2014b). A visual

summary for linked open data sources. International

Semantic Web Conference (Posters & Demos).

Beneventano, D., Bergamaschi, S., Guerra, F., and Vincini,

M. (2003). Synthesizing an integrated ontology. IEEE

Internet Computing, 7(5):42–51.

Bergamaschi, S., Castano, S., Vincini, M., and Beneven-

tano, D. (2001). Semantic integration of hetero-

geneous information sources. Data Knowl. Eng.,

36(3):215–249.

Bizer, C. and Cyganiak, R. (2007). D2rq-lessons learned.

In W3C Workshop on RDF Access to Relational

Databases, page 35.

Miller, G. A., Beckwith, R., Fellbaum, C., Gross, D.,

and Miller, K. (1990). Wordnet: An on-line lexi-

cal database. International Journal of Lexicography,

3:235–244.

Sahoo, S. S., Halb, W., Hellmann, S., Idehen, K., Thi-

bodeau Jr, T., Auer, S., Sequeda, J., and Ezzat, A.

(2009). A survey of current approaches for mapping

of relational databases to rdf. Technical report, W3C

RDB2RDF Incubator Group Report.

Sallam, R. L., Hostmann, B., Schlegel, K., Tapadinhas, J.,

Parenteau, J., and Oestreich, T. W. (2015). Magic

quadrant for business intelligence and analytics plat-

forms. Technical report, Gartner. Gartner Research

Note G00270380.

Tufte, E. R. and Graves-Morris, P. (1983). The visual dis-

play of quantitative information, volume 2. Graphics

press Cheshire, CT.

Open Data for Improving Youth Policies

129