=Paper=
{{Paper
|id=None
|storemode=property
|title=Publishing Linked Data - There is no One-Size-Fits-All Formula
|pdfUrl=https://ceur-ws.org/Vol-877/poster5.pdf
|volume=Vol-877
}}
==Publishing Linked Data - There is no One-Size-Fits-All Formula==
https://ceur-ws.org/Vol-877/poster5.pdf
Publishing Linked Data - There is no
One-Size-Fits-All Formula
Boris Villazón-Terrazas, Daniel Vila-Suero, Daniel Garijo,
Luis M. Vilches-Blázquez, Marı́a Poveda-Villalón,
José Mora, Oscar Corcho, and Asunción Gómez-Pérez
OEG-DIA, FI, Universidad Politécnica de Madrid, Spain
{bvillazon,dvila,dgarijo,lmvilches,mpoveda,jmora,ocorcho,asun}@fi.upm.es
Abstract. Publishing Linked Data is a process that involves several de-
sign decisions and technologies. Although some initial guidelines have
been already provided by Linked Data publishers, these are still far from
covering all the steps that are necessary (from data source selection to
publication) or giving enough details about all these steps, technolo-
gies, intermediate products, etc. Furthermore, given the variety of data
sources from which Linked Data can be generated, we believe that it is
possible to have a single and unified method for publishing Linked Data,
but we should rely on different techniques, technologies and tools for
particular datasets of a given domain. In this paper we present a general
method for publishing Linked Data and the application of the method
to cover different sources from different domains.
Key words: Linked Data, Publishing Linked Data
1 Introduction and Motivation
So far, Linked Data principles and practices are being adopted by an increasing
number of data providers, getting as result a global data space on the Web
containing hundreds of LOD datasets [3]. Moreover, Linked Data generation
and publication does not follow a set of common and clear guidelines to scale
out the generation and publication of Linked Data.
Furthermore, given the variety of data sources from which Linked Data can
be generated, we believe that it is possible to have a single and unified method for
publishing Linked Data, but we should rely on different techniques, technologies
and tools for particular datasets of a given domain. The rest of the paper is
organized as follows: Section 2 introduces our method for publishing linked data,
then, Section 3 describes the application of the method to cover different sources
from different domains, and finally, Section 4 presents the conclusions.
2 A Method for Publishing Linked Data
In previous work [6] we have already presented the Linked Data Generation
Process as one that follows an iterative incremental life cycle model. In that
work we proposed a method that covers the following activities (1) specification,
for analyzing and selecting the data sources, (2) modelling, for developing the
model that represents the information domain of the data sources, (3) genera-
tion, for transforming the data sources into RDF, (4) linking, for creating links
between the RDF resources, of our dataset, with other RDF resources, of ex-
ternal datasets, (5) publication, for publishing the model, RDF resources and
links generated, and (6) exploitation, for developing applications that consume
the dataset. Each activity is decomposed into one or more tasks, and some tech-
niques, technologies and tools are provided for carrying out them. It is worth
mentioning that the order of the activities and tasks might be changed base on
particular needs of the data owners and publishers. Moreover, we are continu-
ously getting feedback about this method, and therefore, we are improving it
constantly. Figure 1 depicts the main activities.
Fig. 1. Main Activities for Publishing Linked Data (extended from [6])
3 Application of the Method to Different Domains
In this section we present the application of the method to cover different sources
from different domains.
3.1 GeoLinkedData
GeoLinkedData1 aims at enriching the Web of Data with Spanish geospatial
data into the context of INSPIRE themes2 . This initiative has started off by
1
http://geo.linkeddata.es/
2
The INSPIRE Directive addresses 34 spatial data themes needed for environmental
applications. http://inspire.jrc.ec.europa.eu/index.cfm/pageid/2/list/7
publishing diverse information sources belonging to the National Geographic
Institute of Spain, IGN, and the National Statistic Institute in Spain, INE. Such
sources are made available as RDF knowledge bases according to the Linked
Data principles [3].
Regarding the specification activity, the data sources taken for this project
are composed of (1) a set of spreadsheets published by the Spanish Statistical
Institute (INE), (2) a set of geospatial relational databases, Oracle and MySQL,
from the Spanish Geographical Institute (IGN). Within this activity we also
have analyzed the data sources and defined the URI design for the resources.
After the specification activity, we need to define the ontologies to be used
for modelling the domain of those data sources. We have created a modular on-
tology network3 by reusing available knowledge resources. The ontology network
consist of the following subontologies (1) SCOVO4 for modelling the statistical
data, (2) FAO Geopolitical Ontology5 for representing the Spanish administra-
tive division, (3) hydrOntology6 for describing the hydrographical domain, (4)
WGS847 vocabulary for representing information about spatially-located things,
and (5) Time Ontology8 for describing temporal concepts.
As for the generation activity we have used (1) ODEMapster9 for transform-
ing relational database content into RDF, (2) NOR2O10 for converting spread-
sheets into RDF, and (3) geometry2rdf11 for transforming geospatial databases
into RDF. It is worth mentioning that the geometry stored in the databases
were simple (stored in normal database columns) and complex (stored in BLOB
columns).
Regarding the linking activity, we have used Silk12 [1] for discovering owl:sameAs
links between our dataset and external datasets, i.e., GeoNames13 and DBpe-
dia14 . We also used sameAs validator15 , an application for validating the links
discovered by Silk.
Once we had the legacy data transformed into RDF, we needed to store and
publish the data in a triplestore. In our scenario we decided to rely on Virtuoso
3
http://geo.linkeddata.es/web/guest/modelos
4
http://vocab.deri.ie/scovo
5
http://www.fao.org/countryprofiles/geoinfo.asp
6
http://www.oeg-upm.net/index.php/es/ontologies/107-hydrontology
7
http://www.w3.org/2003/01/geo/
8
http://www.w3.org/TR/owl-time/
9
http://www.oeg-upm.net/index.php/en/technologies/9-r2o-odempaster
10
http://www.oeg-upm.net/index.php/en/technologies/57-nor2o
11
http://www.oeg-upm.net/index.php/en/technologies/151-geometry2rdf
12
http://www4.wiwiss.fu-berlin.de/bizer/silk/
13
http://geonames.org/
14
http://dbpedia.org/About
15
http://oegdev.dia.fi.upm.es:8080/sameAs/
Universal Server16 and Pubby17 . For the metadata information we have used
VoID18 for expressing the metadata about our dataset.
Finally, for the exploitation we have developed a prototype application19 that
browses and visualizes the RDF data.
3.2 AEMETLinkedData
In AEMETLinkedData20 we have generated Linked Data from Spanish meteoro-
logical data. Within this initiative we are publishing information resources from
the Agencia Estatal de Meteorologı́a (Spanish Meteorological Office, AEMET),
as Linked Data.
Within the specification activity, among all of the data made available in the
FTP server from AEMET, we have focused on surface meteorological observing
stations, and more precisely in measurements taken in ten minute interval times.
AEMET has around 250 automatic weather stations of this network, registering
pressure, temperature, humidity, precipitation and wind data every 10 minutes.
Data from the different stations are publicly available online in comma separated
values (CSV) files, compressed with gzip, updated every hour and kept for seven
days. This means, every hour six new files are added, corresponding with periods
of ten minutes, and every day a new folder is created to store the files for that
day.
As for the modelling, the development of the AEMET ontology network21 has
been performed following an iterative approach based on the reuse of existing
knowledge resources, both ontological (including ontologies and Ontology Design
Patterns ) and non-ontological resources as proposed by the NeOn methodology
[5]. The AEMET ontology network follows a modular structure consisting of a
central ontology that links together a set of subontologies that describe differ-
ent sub domains involved in the modelling of the meteorological measurements.
The subontologies are (1) Measurement ontology that models the knowledge
related to meteorological observations and reuses the SSN Ontology22 , (2) Loca-
tion ontology that models knowledge about locations and reuses the WGS8423
vocabulary, such as administrative limits and coordinates, (3) Time ontology
that reuses the W3C Time Ontology24 and models knowledge about time such
as temporal units, temporal entities, instants, intervals, etc., and (4) Sensor on-
tology that models the network of sensors and weather stations.
16
http://virtuoso.openlinksw.com/dataspace/dav/wiki/Main/
17
http://www4.wiwiss.fu-berlin.de/pubby/
18
http://www.w3.org/TR/void/
19
http://geo.linkeddata.es/web/guest/visualizacion-beta
20
http://aemet.linkeddata.es/
21
http://aemet.linkeddata.es/models_en.html
22
http://www.w3.org/2005/Incubator/ssn/wiki/Report_Work_on_the_SSN_
ontology
23
http://www.w3.org/2003/01/geo/
24
http://www.w3.org/TR/owl-time/
The RDF generation is produced with ad-hoc Python scripts25 . These scripts
are executed in two processing steps, integrating with ease the generation of RDF
and tasks as crawling the FTP server where the CSV files are located. The first
step generates the information about the automatic stations, which is static, and
thus needs to be executed only once. The second step generates the information
about the observations on a regular basis, keeping the data updated.
Regarding the publication, the generated RDF is stored in Virtuoso, which
integrates with Pubby for the publication of the results and makes them available
for humans as well as computers.
As for the exploitation, we have developed a simple application26 that con-
sists on a viewer based on a modified version of map4rdf27 , a tool that uses
Google Web Toolkit framework to visualize and access Linked Data resources.
Each automatic weather station is geolocated, enabling its representation in the
map with a marker. Since there are approximately 300 stations, they can be all
represented at the same time.
3.3 El Viajero
El Viajero is a dataset that exposes descriptive metadata and provenance of
news, blogs and posts belonging to the Prisa Digital Group28 in the domain of
travel journalism. El Viajero makes use of several heterogeneous datasets, and it
is integrated within a service which exploits it in order to help users navigating
and browsing the contents of the dataset.
Regarding the specification, the data sources analyzed come from a variety
of newspapers and digital platforms belonging to the Prisa Digital Group: “Su-
plemento El Paı́s”, “Guı́as Aguilar”, and “Canal Viajar”. Users can also create
their own blogs to post their traveling experiences (around 600 different blogs
exist), apart from commenting the news posted by the reporters of the group.
The news are stored using an extension over the International Press Communica-
tions Council (IPTC)29 Standard called News Industry Text Format (NITF)30 ,
which is also used by other major publishing agencies in Europe, such as AFP31 ,
ANSA32 or AP Digital33 . The data is separated from the metadata in different
kinds of files: one for the structure of the news, another for the content of each
piece of news, another one for the metadata of the sources of the news like images
or videos and another one for storing statistics about the news (such as views,
date of the last view, etc). The blogs and posts are stored in two different private
databases: one with metadata about the posts (creator, number of comments,
25
http://aemet.linkeddata.es/technology_en.html
26
http://aemet.linkeddata.es/browser_en.html
27
http://oegdev.dia.fi.upm.es/map4rdf/
28
http://www.prisa.com
29
http://www.iptc.org/site/Home/
30
http://www.iptc.org/cms/site/index.html?channel=CH0107
31
http://www.afp.com/
32
http://www.ansa.it/
33
http://www.apdigitalnews.com/
body, date of creation, etc.) and the other one with metadata about the blogs
(title, header image, description, owner, etc).
The resources have been modeled following a layered approach. In the first
level DC34 , SIOC35 , MPEG736 and WGS8437 vocabularies are used for describ-
ing the metadata of the different resources on the platform (e.g., post metadata,
photo and video metadata, location metadata, etc.), but not their evolution. In
the second level, our ontology38 connects the previous vocabularies to the Open
Provenance Model (OPM) [4] by extending the OPM core ontology (OPM-O39 )
in the third layer.
As for the generation activity, we have developed and reused parsers to pro-
duce RDF from the Prisa Digital datasets automatically. The system has been
developed using the JENA Framework, combined with the TDB and Virtuoso
repositories for the proper publication of the dataset. The data is exposed using
Pubby, and additional metadata about the query used to retrieve the exposed
data is captured with the Provenance Vocabulary (along with metadata of the
dataset itself).
Since El Viajero belongs to a Spanish media company, many of the guides0
locations refer to Spanish locations. GeoLinkedData contains accurate metadata
about the majority of these locations, so we have linked to this dataset the
guides within the Spanish territory. For the guides referring to the rest of the
world we linked to DBpedia, the most linked dataset in the Linked Data cloud,
and which provides many additional details about the locations. We relied on
the Silk framework for finding the links to these two datasets, curating manually
the results obtained by the tool. Additional links from travel recommendations
have been linked to the Linked User Feedback (LUF) dataset40 .
Thanks to the publication of El Viajero as Linked Data, users have available
more than 6600 resources about almost 1000 locations around the world. In order
to help them organize, browse and explore the contents of the dataset, we have
developed an application [2] to expose these contents in an eye catching way by
representing them in a map.
3.4 datos.bne.es
datos.bne.es publishes bibliographic data from the Spanish National Library
(BNE) catalogue. This initiative has kicked off with the publication, under
Linked Data principles, of information from the bibliographic and authorities
catalogues, making them available as RDF knowledge bases. Furthermore, these
bases are interrelated with other knowledge bases existing within the Linking
34
http://dublincore.org/documents/dcmi-terms/
35
http://rdfs.org/sioc/spec/
36
http://metadata.net/mpeg7/
37
http://www.w3.org/2003/01/geo/
38
http://webenemasuno.linkeddata.es/Ontology/OWLDOC/index.html
39
http://openprovenance.org/model/opmo
40
http://lab.isoco.net/technologies/luf
Open Data initiative. With this initiative, Spain has joined the ranks of other
institutions such as the British Library and the Deutsche Nationalbibliothek
that have recently launched similar projects.
Regarding the specification activity the data source taken for this project is
composed of around 3.9 million bibliographic and 4.2 million authority records
in the MARC2141 format using the ISO 270942 encoding standard, provided as
data dump from the BNE catalogue. With the respect to the data quality, we
have assumed the records to present necessary quality features like consistency,
accuracy, or precision, as the records have been created and curated by highly
qualified professionals. Regarding the URI design we have put special focus on
minting URIs in a multilingual scenario, taking into account the design of TBox
and ABox URIs.
After the specification activity, we define the ontologies to be used for mod-
elling the domain of those data sources. Again, the most important recommen-
dation is to reuse as much as possible available vocabularies and ontologies. To
this end, we have decided to reuse a very well stablished set of models as the
IFLA Functional Requirements family43 and ISBD elements44 that have been
agreed upon and used by the library community.
As for the generation activity we have used MARiMbA45 that provides a tool
focused on enabling and easing the mapping from MARC21 to the RDF(S)/OWL
models, and the transformation into RDF. In this activity we have emphasized
the importance of the mapping in which we establish correspondences between
MAR21 records the chosen vocabularies. Furthermore, MARiMbA0 s main design
principle is to provide an easy-to-use mapping framework that allows domain
experts (i.e. librarians and cataloguers) to work independently from the IT team
and to establish the mapping rules without the need of high technical skills (e.g.
knowledge about XML, XSLT or other ad-hoc mapping languages).
Regarding the linking activity, we chose VIAF46 , the Virtual International
Authority File from the OCLC, as a candidate dataset since we had to deal with
catalogue authoring data. VIAF provides a cluster of authority records of the
same entity across several national authority files. This cluster has proven to
be useful to establish links to other authoritative datasets such as the German
National Library.
Once we have the legacy data transformed into RDF, we need to store and
publish the data in a triplestore. In our scenario we have decided to rely on Vir-
tuoso Universal Server and Pubby and to provide an additional web-developers-
friendly API (providing simple methods for retrieving persons, works, etc.), using
41
http://www.loc.gov/marc/bibliographic/
42
http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_detail.htm?
csnumber=7675
43
http://www.ifla.org/en/node/2016
44
http://metadataregistry.org/schema/show/id/25.html
45
www.oeg-upm.net/index.php/en/downloads/228-marimba
46
http://viaf.org/
Puelia47 . For the metadata information we have used VoID for expressing the
metadata about our dataset.
Finally, for the exploitation we have developed a prototype application that
provides easy full-text searching and navigation based on the FRBR model.
4 Conclusions
In this paper we claim that it is possible to have a single and unified method for
publishing Linked Data, but we should rely on different techniques, technologies
and tools for particular datasets of a given domain. Therefore, we have presented
our method for publishing Linked Data and the application of that method to
different domains. Table 1 summarizes the vocabularies, technologies, and appli-
cations used in the development of our initiatives. The first column represents the
six main activities of our methodological guidelines, i.e., modeling, RDF genera-
tion, links generation, publication and exploitation. The rest of columns include
the technological support used for each phase within the set of our initiatives.
47
Puelia is an implementation of the Linked Data API specification, see
http://code.google.com/p/puelia-php/
Table 1. Summary of the vocabularies, technologies, and applications used in our
initiatives.
Moreover, we have to take into account that there are (1) multiple features such
as geospatial, textual, and temporal among others; (2) multiple formats such
as csv, excel, pdf, databases, among others; (3) multiple data models such as
MARC21 for bibliographic data, simple or complex geometry for geographic in-
formation, etc; (4) multiple features or special characteristics of the data such as
dynamicity, provenance, clashes between ids, cleansing and curation of existing
data sources, etc; (5) multiple URI design decisions depending on the type of
data we are dealing with; (6) multiple types of license of the datasets, depending
on the creators and publishers; and (7) multilingualism of datasets and how this
affects the creation of mappings and RDF.
Acknowledgments.
This work has been supported by BabeLData (TIN2010-17550), myBigData
(TIN2010-17060), PlanetData (FP7-257641), and Webn+1 (TSI-020301-2009-
24) projects. We would like to thanks to all OEG members involved in the
Linked Data initiatives.
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