=Paper=
{{Paper
|id=Vol-1322/paper_10
|storemode=property
|title=Linked Open Data for Environment Protection in Smart Regions - The SmartOpenData Project Approach
|pdfUrl=https://ceur-ws.org/Vol-1322/paper_10.pdf
|volume=Vol-1322
}}
==Linked Open Data for Environment Protection in Smart Regions - The SmartOpenData Project Approach==
https://ceur-ws.org/Vol-1322/paper_10.pdf
Linked Open Data for Environment Protection in
Smart Regions
– The SmartOpenData Project Approach –
1
Phil Archer, 2Karel Charvat, 3Mariano Navarro De La Cruz,
4
Carlos Ángel Iglesias, 5John O'Flaherty, 4Tomás Robles,
7
Dumitru Roman*
1
ERCIM, France, 2 HSRS, Czech Republic, 3 TRAGSA, Spain,
4
Universidad Politecnica de Madrid, Spain, 5 MAC, Ireland,
7
SINTEF, Oslo, Norway
Dumitru.Roman@sintef.no
Abstract. Many different open information sources currently exist for
protecting the environment in Europe, mainly focused on Natura 2000 network,
and areas where environmental protection and activities like tourism need to be
balanced. Managing these data and integrating them for supporting decision
makers and for novel uses is a challenging task. The SmartOpenData project
(2013-1015) aims to define mechanisms for acquiring, adapting and using Open
Data provided by existing sources for environment protection in European
protected areas. Through target pilots in these areas, the project will harmonise
metadata, improve spatial data fusion and visualisation and publish the resulting
information according to user requirements and Linked Open Data principles to
provide new opportunities for use. SmartOpenData will be based on previous
experiences of Habitats project, which defined models and tools for managing
spatial data in environmental protection areas. This paper provides an
introduction to the SmartOpenData with a specific focus on the motivation,
goals, and technical focus of the project, and outlines the architecture of the
approach taken by SmartOpenData.
1 SmartOpenData Overview
There exist many different open data sources for protecting biodiversity and environmental
research in Europe in coastal zones, agricultural areas, forestry, etc., mainly focused on the
Natura 2000 network1, and areas where environmental protection and activities like agriculture,
forestry or tourism need to be balanced with the Habitats Directive2 and the European Charter
for Sustainable Tourism in Protected Areas3. Better understanding and managing these data not
* Authors listed in alphabetical order. Contact author: Dumitru Roman (dumitru.roman@sintef.no).
1 http://www.natura.org/
2 http://ec.europa.eu/environment/nature/legislation/habitatsdirective/index_en.htm
3 http://www.european-charter.org/home/
only can provide economic value of these areas (value currently largely unknown), but will
enable organizations to develop new services based on these data and open up new possibilities
for public bodies and rural and protected areas to benefit from using data in novel ways,
improving their knowledge and environment protection through new innovation ecosystems. In
this context, the SmartOpenData4 project has set its goals to:
Create a sustainable Linked Open Data5 infrastructure in order to promote
environmental protection data sharing among public bodies in the European Union;
Enhance Linked Open Data with semantic support by integrating semantic
technologies built upon connected Linked Open Data catalogues aiming at building
sustainable, profitable and standardised environment protection and climate change
surveillance services;
Define business models specially focused on SMEs and based on innovative services
as new opportunities to align research results, previous work and projects, tackling
active involvement of the whole value chain in Smart Regions at policy, industry and
society levels;
Demonstrate the impact of the sharing and exploiting data and information from
many varied resources, in rural and European protected areas by providing public
access to the data and developing demonstrators that will show how services can
provide high quality results in regional development working with semantically
integrated resources.
2 Building upon Previous Results: The HABITAS Project
The Habitats project6 was built as an environment that enables to share and combine data from
various sources. The project results were validated through the Habitats Reference Laboratory
and pilot applications. On the basis of different pilots, Habitats defined and tested
harmonisation rules for spatial environmental data and designed the concept of Reference
Laboratory as a tool for testing the interoperability and supporting unification of outputs across
different pilots.
The challenges faced by Habitats were mainly due to data availability, integration and
usage ability for decision-making and, in particular, in terms of its focus on Metatada, Data
Specifications, Network Services, Data and Service Sharing and Monitoring and Reporting.
Habitats is to support the EU INSPIRE Directive.
The specific usage scenarios, including the state of the art baseline and user requirements
coming from them represent the key input for the planned data and meta-data modeling
activities and the SDI services that were developed in the Habitats project. Generally speaking,
a positive correlation in all the pilots was detected, between service development and user
satisfaction, while on the other hand, it cannot be taken for granted that the new services
provided are also INSPIRE compliant. This can be due to several reasons, two of which seem
more prominent than others:
On the “supply side”, the cost of increasing the compliance, in terms of time,
resources, etc., from the perspective of the SDI “owner”;
On the “demand side”, lack of interest or simply ignorance of the advantages of
compliance, from the perspective of the end users.
4 SmartOpenData: "Linked Open Data for environment protection in Smart Regions” under the call FP7-ENV-2013-two-
stage concerning the Seventh Framework Programme (FP7) (2013-2015).
5 http://linkeddata.org/
6 http://www.inspiredhabitats.eu/
3 The SmartOpenData Approach
Linked Open Data is emerging as a source of unprecedented visibility for environmental data
that will enable the generation of new businesses as well as a significant advance for research
in the environmental area. In order for this envisioned strategy to become a reality, it is
necessary to advance the publication of existing environmental data, most of which is owned by
public bodies. How Linked Open Data can be applied generally to spatial data resource and
specifically to public open data portals, GEOSS Data-CORE, GMES, INSPIRE and voluntary
data (OpenStreetMap, GEP-WIKI, etc.), and how it can impact the economic and sustainability
progress in European Environment research and Biodiversity Protection are open questions that
need to be addressed in order to benefit from an improved understanding and management of
environmental data. The SmartOpenData project (2013-2015) will address these questions by
defining mechanisms for acquiring, adapting and using Open Data with a particular focus on
biodiversity and environment protection in rural and European protected areas and its National
Parks.
The vision of the SmartOpenData project is that environmental and geospatial data
concerning rural and protected areas can be more readily available and re-usable, better linked
with data without direct geospatial reference so different distributed data sources could be
easily combined together. SmartOpenData will use the power of Linked Open Data to foster
innovation within the rural economy and increase efficiency in the management of the
countryside. The project will prove this in a variety of pilot programmes in different parts of
Europe. SmartOpenData goal is making INSPIRE/GMES/GEOSS infrastructure better
available for citizens, as well as for public and private organization. On one hand, Europe and
EU invest hundreds of millions of Euros in building the INSPIRE infrastructure. On the other
hand, public and private organizations, as well as citizens use for their applications Google
maps. National and regional SDIs offer information which is not available on Google, but this
potential is not used. One of the main goals of SmartOpenData is making European Spatial
Data easily re-usable not only by GIS experts but also by various organizations and individuals
at a larger scale. To realize this, on a technical level, the project will:
Harmonise geospatial metadata (ISO19115/19119 based) with principles of Semantic
Web;
Provide spatial data fusion introducing principles of Open Linked Data;
Improve spatial data visualisation of Geospatial Open Linked Data;
Publish the resulting information according to user requirements and Linked Open
Data principles.
In the context of the SmartOpenData project, using linked data for spatial data means
identifying possibilities for the establishment of semantic connections between
INSPIRE/GMES/GEOSS and Linked Open Data spatial related content in order to generate
added value. The project requirements are within the environmental research domain. This will
be achieved by making existing “INSPIRE based” relevant spatial data sets, services and
appropriate metadata available through a new Linked Data structure. In addition, the proposed
infrastructure will provide automatic search engines that will crawl additional available
geospatial resources (OGC and RDF structures) across the deep and surface web. The main
motivation to utilise the potential of Linked Data is to enrich the INSPIRE spatial content to
enable improved related services to be offered and to increase the number, performance and
functionality of applications. In many cases querying data in INSPIRE (GEOSS) based data
infrastructure (driven mainly by relation databases) is time consuming and often it is not
sufficient and understandable for common Web users. In large databases such queries can take
minutes or hours. In the cases of distributed databases such a query is almost impossible or very
complicated. SmartOpenData aims to improve this situation dramatically.
The most advanced technical effort to reconcile the Linked Data and Geospatial Data
worlds is embodied by OGC's GeoSPARQL standard7. This merges the two technologies, with
the GeoSPARQL engine translating queries back and forth between RDF and geospatial
engines. The number of implementations of GeoSPARQL is growing8 but there remains some
debate as to whether it is the best approach. The NeoGeo vocabulary9 is favoured by French
mapping agency IGN and handles geospatial data differently by linking to it from the RDF,
rather than transporting large literals. The INSPIRE standards have been developed entirely in
an XML-centric manner and the European Commission's JRC is now working on making better
use of linked data. This is being done in a W3C Community Group focussing on locations and
addresses10. A related, but separate, Community Group is also considering better interplay
between Web and geospatial technologies11. What these activities all suggest is that there is
work to be done to allow geospatial and linked data specialists to communicate easily, avoiding
the so-called religious wars. SmartOpenData brings together specialists in both disciplines:
RDF to describe a location or point of interest, GI to define where it is on the Earth's surface.
Another important problem to be addressed in the context of SmartOpenData is
multilingualism. The problem of translating geographical data and metadata has not yet been
solved inside INSPIRE or GEOSS. It brings problems of global data utilisation by local
communities and local data by foreigners. Translation of geographical data is a big challenge
for everyone within the SDI community and its importance will grow in relation with growing
of SDI. The implementation of RDF should help ease the translation of geographic names or
keywords from vocabularies like GEMET or AgroVoc.
The research focus for SmartOpenData will address how to use existing GI data within an
RDF framework, or, from the other direction, how existing GI data can be accessed as part of
linked data. To achieve this, new algorithms will be developed that expose the wealth of
environmental data as linked data. This may require some human intervention in some cases
but such intervention will be minimised with a view to making it repeatable and scalable. For
example, the Open Refine tool12 allows the same operation to be carried out on tabular datasets
of unlimited size and is likely to be useful in this task, perhaps supported by a SmartOpenData
reconciliation API. In a linked data environment, the definition of points, lines and polygons
remains untouched but the relationships between features, the names of places and, in
particular, the identifiers, are handled differently. Separating those elements out and encoding
them as linked data, and doing so at scale, will be a significant challenge. Creating the data as
RDF and storing it in dedicated triple stores is only the first step, however. More difficult is the
discovery of links to data already available in the linked open data cloud, such as GeoSpecies.
The example given on the GeoSpecies Web site13 shows detail of the Cougar including links to
where it can be expected to be found. It is links between datasets that makes linked open data
so powerful and forging those links is an essential aspect of realising the objectives of
SmartOpenData.
There are two principal approaches to machine translation: rule-based and statistical.
Current state-of-the-art machine translation (MT) technology is based on the SMT (statistical
MT) paradigm, which assumes the application data to match the training data, used during the
learning phase to extract and generalise the parameters of the system. Combined methods are
also being investigated currently, bringing together the linguistic and translation knowledge
accumulated over the last 40 years with the SMT systems as deployed today. For SMT systems,
7 http://www.opengeospatial.org/standards/geosparql
8 http://geosparql.org, https://twitter.com/marin_dim/status/271573164268609536, http://www.strabon.di.uoa.gr
9 http://geovocab.org/doc/neogeo
10 http://www.w3.org/community/locadd
11 http://www.w3.org/community/geosemweb
12 http://openrefine.org (formerly Google Refine)
13 http://datahub.io/dataset/geospecies/resource/47e71c4c-9565-4185-b8c0-bdef6449278e
the more distant the actual data is from the data used for training, the worse the results are. As
we are concerned with environmental and geographical data, we will explore resource-limited
adaptation to those domains in the context of SmartOpenData.
Another area of research for the project will be the handling of large volumes of real time
data. This puts a strain on the infrastructure and so methods to reduce that stress will need to be
researched, possibly using the W3C POWDER technology14 as a data compression tool.
Tracking the provenance of any data is important of course but as yet there is no (standardised)
linkage within the Semantic Web technology stack between Provenance15 and SPARQL Update.
4 The SmartOpenData Architecture
The SmartOpenData infrastructure is depicted in the following figure where three main
elements can be identified.
Figure 1. SmartOpenData Infrastructure
In the lower level the external data sources are depicted. Data sources can be grouped in two
different sets. The first one is composed by data sources that fulfil some of the standards
supported by SmartOpenData (green boxes). The second group is composed by data sources
that does not fulfil those standards (blue boxes). In the upper layer, three different scenarios
14 http://www.w3.org/standards/techs/powder#w3c_all
15 http://www.w3.org/standards/techs/provenance#w3c_all
have being identified: scenario for researches, scenario for companies and scenario for end-
users. Each scenario will focus on one specific segment using the functionalities provided by
the SmartOpenData System, creating services that take advantage of such data and provide
valuable services for each community illustrating how the availability of such services and the
corresponding data can provide advantages for them.
Between the external data sources and the data consumer in the scenarios the
SmartOpenData System is placed providing key functionalities. The most basic element of the
SmartOpenData System is the harmonisation of data sources. This element offers an open data
source layer that exposes the external data sources fully adapted to the open data standards
supported by the project. If an external data source does not provide the information according
to the required standard, and adaptation is required, which is depicted in the figure as an extra
box, which provides such adaptation specifically tuned for each external data source. The open
data source layer provided both semantic information of the data and data themselves. Over this
open data source layer, three key functionalities are defined:
Distributed semantic indexing, which provides a service for searching and locating data
based on semantic information collected from all the available Data Sources;
Distributed data access, which provides data collected from external data sources, as an
extra data source for easier and uniform data gathering from the users at the identified
scenarios;
Administration and notification, which provides administration facilities for managing
users, workflows and data to data providers.
These three functional components are coordinated inside the SmartOpenData System,
creating a distributed service system which can be accesses transparently from the scenarios. It
is also important to note that it will be possible for services created on the scenarios to access
directly external data sources selected thought the distributed semantic indexing functionality
of the SmartOpenData System if they are provided using one standard as shown on the picture.
5 Outlook
SmartOpenData will create a Linked Open Data infrastructure (including software tools and
data) fed by public and freely available data resources, existing sources for biodiversity and
environment protection and research in rural and European protected areas and its National
Parks. SmartOpendata is going to evaluate infrastructure and tools by the development and
deployment of five advanced demonstrators focused on agroforestry management,
environmental research water monitoring respectively, forest sustainability and environmental
data reuse. This will provide opportunities for organizations to generate new innovative
products and services that can lead to new businesses in the environmental, regional decision-
making and policy areas among others. The value of the data will be greatly enhanced by
making it available through a common query language that gives access to related datasets
available in the linked open data cloud. Organizations such as Environmental Agencies and
National Parks will benefit by improving their knowledge of the biodiversity status,
maintenance and protection, including achievement of “the INSPIRE and Open Data Ready”
status for their digital (not only spatial) content. Public bodies, researchers, companies and
European citizens will take a central role in user-driven pilots developed to enhance the
potential of protected areas. Innovation by third party companies will be encouraged by the
promotion of royalty-free open standards and best practices generated, initiated or simply
highlighted by SmartOpenData. The project will also contribute and where possible benefit
from ongoing and upcoming related initiatives like Open government partnership16, INSPIRE
16 http://www.opengovpartnership.org/
maintenance and implementation framework17, European Union Location Framework18, or
Interoperability Solutions for European Public Administrations19 (e.g. Working Group on
Spatial Information and Services).
Acknowledgment. The SmartOpenData project is funded by the European Commission and is
a collaboration between 16 European organizations (Empresa de Transformación Agraria SA,
Universidad Politécnica de Madrid, The National Microelectronics Applications Centre LTD,
Sindice LTD, Mid-West Regional Authority, Environment Protection Regional Agency,
Fondazione Bruno Kessler, Spazio Dati, Help Service-Remote Sensing SRO, Forest
Management Institute, Czech Centre for Science and Society, Stiftelsen SINTEF, Latvijas
Universitates Matematikas Un Informatikas Instituts, Direção Geral do Território, Slovak
Environmental Agency, European Research Consortium for Informatics and Mathematics).
17 http://inspire.jrc.ec.europa.eu/index.cfm/pageid/5160
18 http://ec.europa.eu/isa/actions/documents/isa-2.13_eulf-strategic-vision-lite-v0-3_final_en.pdf
19 http://ec.europa.eu/isa/