Implementing a Semantic Catalogue of Geospatial Data
Helbert Arenas, Benjamin Harbelot and Christophe Cruz
Laboratoire Le2i, UMR-6302 CNRS,D
epartement d’Informatique , Universit
e de Bourgogne,
7 Boulevard Docteur Petitjean, 21078 Dijon, France
CSW, OGC, Triplestore, Metadata.
Complex spatial analysis requires the combination of heterogeneous datasets. However the identification of
a dataset of interest is not a trivial task. Users need to review metadata records in order to select the most
suitable datasets. We propose the implementation of a system for metadata management based on semantic
web technologies. Our implementation helps the user with the selection task. In this paper, we present a
CSW that uses a triplestore as its metadata repository. We implement a translator between Filter Encoding and
SPARQL/GeoSPARQL in order to comply to basic OGC standards. Our results are promising however, this
is a novel field with room for improvement.
There is a growing interest in the development of the
SDI (Spatial Data Infrastructure), a term that refers
to the sharing of information and resources between
different institutions.The term was first used by the
United States National Research Council in 1993. It
refers to the set of technologies, policies and agree-
ments designed to allow the communication between
spatial data providers and users (ESRI, 2010).
Currently vast amounts of information are be-
ing deployed in the internet through web services.
However, in order to profit of this information, po-
tential users need to first identify relevant and suit-
able datasets. Later, researchers and decision makers
would be able to implement smart queries. This is
a term first employed by Goodwin (2005). It refers
to the combination of heterogeneous data sources in
order to solve complex problems (Goodwin, 2005).
In the spatial domain this has been possible,
thanks, in a significant part to the standardization ef-
forts by OGC (Open Geospatial Consortium). OGC is
an international industry and academic group whose
goal is to develop open standards that enable com-
munication between heterogeneous systems (OGC,
2012). The tasks in which OGC is interested are:
publishing, finding and binding spatial information.
OGC provides standards that allow data providers and
users to communicate using a common language. The
information is offered through web services such as
WFS (Web Feature Service), WMS (Web Map Ser-
vice) or SOS (Sensor Observation Service). However,
in order to identify a dataset of interest the user needs
first to identify it, using a catalog service. The OGC
standard for catalog services is CSW (Catalogue Ser-
vice for the Web).
OGC defines the interfaces and operations to
query metadata records. There are both commer-
cial and opensource/freeware CSW implementations.
Among the commercials we can find ESRI ArcGIS
server and MapInfo Manager. Among the open-
source implementations we find Constellation, De-
gree and GeoNetworkCSW. The OGC standards do
not indicate specific software components. In the
case of CSW, developers are able to select the
metadata repository more suitable to their prefer-
ences/requirements. However, the OGC CSW stan-
dard defines operations, requests and metadata for-
mats that should be supported. For instance, queries
submitted to a CSW should be formatted as Filter En-
coding or as CQL. The former is a XML encoded
query language, while the later is a human readable
text encoded query language (OSGeo, 2012)(Vre-
tanos, 2005).
Most common implementations of CSW use a
relational database as the metadata records reposi-
tory. For instance, GeoNetwork currently one of
the most popular CSW implementations, uses by de-
fault a McKoiDB relational database, although it can
connect to MySQL, PostGreSQL and other RDBMS
(Dunne et al., 2012). Because of the nature of the
metadata repository, currently queries are performed
Arenas H., Harbelot B. and Cruz C..
Implementing a Semantic Catalogue of Geospatial Data.
DOI: 10.5220/0004820101520159
In Proceedings of the 10th International Conference on Web Information Systems and Technologies (WEBIST-2014), pages 152-159
ISBN: 978-989-758-023-9
2014 SCITEPRESS (Science and Technology Publications, Lda.)
by matching strings to selected metadata elements.
In this paper we propose the use of semantic web
technologies to store and query metadata records. By
using these technologies we are able to take advantage
of inference and reasoning mechanisms not available
on relational databases. In Section 2 we review re-
search conducted by other teams in the same field. In
Section 3 we describe how we have implemented our
model. Finally, in Section 4 we present our conclu-
sions and outline future research.
Spatial information is offered by different providers
through web services that implement standards such
as WCS, SOS, WFS or WMS. The deployed services
might have heterogeneous characteristics regarding
software components, languages, or providers. How-
ever, by implementing OGC standards all of them
have common request and response contents, param-
eters and encodings. These common elements allow
a user to access different services using a proven, safe
strategy . In order to allow datasets to be discover-
able, they have to be published in a catalogue service
that implements the CSW standard. The metadata for
the datasets is obtained by the catalogue service with a
harvest operation. The user in order to discover a spe-
cific dataset, submits a query. The server processes
the query using a string matching process, and sends
a response to the user. Once the user has identified
the relevant dataset, she is able to obtain the data and
perform a specific analysis.
The string matching process is a major limitation
in the current SDI. This limitation has been previously
identified by other researchers. In (Kammersell and
Dean, 2007) the authors aim to integrate heteroge-
neous datasources. In this research the authors pro-
pose the creation of a layer that translates the users
query formulated in OWL into WFS XML request
format. Later, they propose do the inverse process
with the results. Another approach is proposed by
(Kolas et al., 2005). Here the authors propose the
implementation of five different ontologies: 1) Base
Geospatial Ontology for basic geospatial concepts re-
sulting from the conversion of GML schemas into
OWL. 2) Domain Ontology, this is the user‘s ontol-
ogy. Its purpose is to link user‘s concepts to the Base
Geospatial Ontology. 3) Geospatial Service Ontol-
ogy, used to describe services and allow discovery. 4)
Geospatial Filter Ontology, which is used to formal-
ize filter description and use. 5) Feature Data Source
Ontology, to represent the characteristics of the fea-
tures returned from the WFS. Another approach is de-
scribed by (Harbelot et al., 2013), here the authors
suggest the integration of data from OGC services
into a triplestore with a focus on the WFS filters. In
(Janowicz et al., 2010) (Janowicz et al., 2012), the
authors propose the addition of semantic annotations
for each level of a geospatial semantic chain process
that involves OGC services. For instance, they pro-
pose specific semantic annotations at the level of the
service OGC Capabilities document that would cor-
respond to all the datasets managed by the service.
Other annotations would correspond to specific data
layers. Spatial Data with semantic annotations could
later be processed and semantically analysed using
custom made reasoning services. To achieve this goal
they propose the deployment of OGC services ca-
pable of interacting with libraries such as Sapience
which would result in richer data and data descrip-
tions. However there is little development in this di-
rection. At the moment there is little use of semantic
annotations on OGC capabilities documents.
In (Gwenzi, 2010) the author describes the CSW
limitations by evaluating GeoNetwork. According to
the author there are three possible ways to add se-
mantic capabilities to the CSW: 1) Linking keywords
to concepts in the getCapabilities response. 2) By
adding an ontology browser to the GeoNetwork client
interface. 3) Using ebRIM extensions to add ontolo-
gies to the CSW. In (Gwenzi, 2010) the author imple-
ments the third option.
Another experience is presented by Yue et al.
(2006). In this work the authors extend the ebRIM
CSW specification by: 1) Adding new classes based
to existing ebRIM classes; and 2) Adding Slots to ex-
isting classes, thus creating new attributes. With these
additions the authors are able to store richer metadata
records in the catalogue. The authors identified two
options to implement an upgraded search function-
ality: 1) Create an external component without fur-
ther modification of the CSW schemas; 2) Modify the
CSW adding semantic functionalities to the existing
CSW schemas. In (Yue et al., 2006) the authors opted
for the first option. Yue et al. (2011) extends this
work, focusing on geoservices (Yue et al., 2011).
A different approach is used by (Lopez-Pellicer
et al., 2010). In this research the goal is to provide ac-
cess to data stored in CSW as Linked Data. In order
to achieve this goal the authors developed CSW2LD,
a middle layer on top of a conventional CSW based
server. It allows the server to mimic other Linked
Data sources and publish metadata records. CSW2LD
wraps the following CSW requests: GetCapabilities,
GetRecords and GetRecordById.
A very interesting work in progress is described in
(Pigot, 2012). This is a website describing a proposal
by a team from the GeoNetwork developer commu-
nity. The authors intend to perform a major change
in GeoNetwork, allowing it to store metadata as RDF
facts stored in a RDF repository. They intent to use
SPARQL/GeoSPARQL to retrieve data. The website
describe technical characteristics of GeoNetwork and
mentions fields that require work in order to imple-
ment the project. Currently queries in GeoNetwork
are formatted as Filter Encoding or as CQL. Any im-
plementation of a RDF metadata repository would
need to consider a translation mechanism between the
current queries format to SPARQL (a W3C recom-
mendation) (DuCharme, 2011). Currently GeoNet-
work handles spatial constraints using GeoTools. In
the semantic web domain, spatial queries are per-
formed using GeoSPARQL (Kolas and Batle, 2012).
According to the authors it is not clear if GeoSPARQL
is mature enough to handle metadata spatial queries.
Even more there is no mechanism to translate spatial
constraints into GeoSPARQL. Regardless of the ad-
vantages that semantic web technologies might bring
into CSWs there is scarce research on this topic. By
the time we wrote this paper, there was no further de-
velopment in (Pigot, 2012) and the website was last
updated by the end of October of 2012.
A regular implementation of OGC CSW, works as a
web service that communicates with a data repository
that stores metadata records. According to the OGC
standard, the catalog should accept requests formatted
as Filter Encoding, which is a XML based language,
designed to express queries. The web service, trans-
lates these queries into a suitable format, such that it
can communicate with its data repository. In most of
the implementations of CSW, the data repository is
relational database.
In this paper, we present a proof of concept imple-
mentation, designed to show the benefits of using on-
tologies in a geospatial data catalog service. We have
developed a minimalistic implementation of the CSW
standard. A major difference between our system and
traditional implementations is that we use a triplestore
as our metadata repository. We opted for a Parliament
triplestore, because of its spatial capabilities thanks
to its support for GeoSPARQL. We developed an on-
tology in the triplestore, and mapped the metadata
records to instances of classes specified in the ontol-
ogy. Thanks to this, it is possible to use superclass -
subclass relationships in the metadata search process.
In traditional CSW implementations, a spatial
search uses the values of the bounding box of the
Figure 1: Architecture implementation.
dataset, as specified in the metadata record. However,
the number of users with an understanding of coor-
dinate systems, good enough to allow them to search
datasets using only the bounding box coordinate val-
ues is quite limited. In our approach, we implement
an ontology class called ToponymUnit. Instances of
this class are geographic features with labels famil-
iar to the users. In our implementation, the user can
search for metadata records whose bounding box has
specific spatial relations with instances of the class
ToponymUnit. Using this approach, users can sub-
mit queries such as: retrieve metadata records that are
within the toponym unit known as France.
Our current implementation is able to re-
spond to standard GetRecords requests submitted
as POST. The response of our system follows the
csw:SummaryRecord format. A crucial part of the im-
plementation is the translation of requests from Filter
Encoding to SPARQL/GeoSPARQL.
Figure 1 depicts the processes in the proposed sys-
tem. In the next subsections we further describe how
we obtain the information necessary to construct the
metadata records, how we map this information to an
ontology, and finally how we perform queries.
3.1 Harvesting Metadata
We focus our research on metadata records for
datasets available through services that implement the
OGC Web Feature Service (WFS) standard. A ser-
vice that implements WFS can contain one or many
datasets. We identify the datasets available on a WFS
and create a metadata record for each one of them.
The metadata record is stored as an instance of the
class abc:MetadataRecord .
In our ontology we implement a class called
geo:Feature that represent features with spatial repre-
sentation. Although, a metadata record is an abstract
description, it does have a spatial component repre-
sented by the bounding box of the dataset it describes.
Due to the spatial nature of the metadata records, we
define the class abc:MetadataRecord as a subclass of
geo:Feature (See Figure 2).
In our ontology we implemented Dublin Core
elements, as properties for instances of the class
Figure 2: Classes, instances and relationships in the pro-
posed model.
Figure 3: Properties of instances of the class
abc:MetadataRecord. We have developed a har-
vest tool that queries the WFS and constructs triples
with the responses. Our tool is a Java application
that makes use of two requests that are part of the
core OGC WFS standard: GetCapabilities and De-
scribeFeatureType. From the GetCapabilities request,
we obtain a general description of the catalog ser-
vice and information regarding the available datasets
on it. With the DescribeFeatureType request, we
can obtain the list of attributes for the actual fea-
tures that compose the dataset. We have tested our
tool with 17 services that implement WFS, having
as a result 2690 metadata records. Figure 3 depicts
the Dublin Core elements as properties of the class
The mapping process between the WFS request
responses and ontology properties is done with our
harvest tool. The WFS respond to requests with a
XML document. Our harvest tool, navigates through
the response document, identifies relevant elements
and maps them to specific properties in our ontology
(See Figures 1 and 3).
For instance, the following code is part of a Get-
Capabilities response containing information regard-
ing the WFS publishing entity.
Provider X
<ows:PositionName>GIS Manager<ows:PositionName/>
This information is mapped into the dc:publisher
Dublin Core element (See Figure 3).
abc:md1 a abc:MetadataRecord .
abc:md1 dc:publisher _:B01 .
_:B01 abc:ProviderName "Provider X" .
_:B01 abc:PositionName "GIS Manager" .
_:B01 abc:IndividualName "Helbert" .
The following XML code, is another part of a Get-
Capabilities response.
<FeatureType xmlns:example=
<Title>Example dataset title</Title>
<Abstract>Example abstract</Abstract>
<ows:Keyword>example keyword1</ows:Keyword>
<ows:LowerCorner>-5.84 37.75</ows:LowerCorner>
<ows:UpperCorner>11.02 54.63</ows:UpperCorner>
From this segment of the response, we can obtain
information for: dc:title, dc:subject, dc:description
and ows:BoundingBox.
abc:md1 a abc:MetadataRecord .
abc:md1 dc:title "Example dataset title" .
abc:md1 dc:subject _:B02 .
_:B02 abc:keyword "example keyword1" .
abc:md1 dc:description _:B03 .
_:B03 abc:abstract "Example abstract" .
_:B03 abc:defaultSRS "EPSG:4326" .
abc:md1 geo:hasGeometry _:B04 .
_:B04 geo:asWKT "POLYGON((-5.84 37.75,
-5.84 54.63, 11.02 54.63, 11.02 37.75,
-5.84 37.75))"ˆˆsf:wktLiteral .
Additionally, our harvesting tool, submits a De-
scribeFeatureType request for each layer of informa-
tion found in the WFS. From the response we are able
to obtain a list of attributes for the spatial features
that compose the dataset. The following XML code
depicts part of a response to a DescribeFeatureType
<xsd:complexType name="country_boundsType">
<xsd:extension base="gml:AbstractFeatureType">
<xsd:element maxOccurs="1" minOccurs="0"
name="THE_GEOM" nillable="true"
<xsd:element maxOccurs="1" minOccurs="0"
name="AREA" nillable="true"
Our harvesting tool, browses through the re-
sponse, identifies relevant elements and create
an attribute list within the dublin core element
dc:description , and creates the following triples:
abc:md1 a abc:MetadataRecord .
abc:md1 dc:description _:B03 .
_:B03 abc:attributeList _:B05 .
_:B05 abc:attribute _:B06 .
_:B06 abc:attributeName "THE_GEOM" .
_:B06 abc:attributeDataType
gml:SurfacePropertyType .
_:B05 abc:attribute B07 .
_:B07 abc:attributeName "AREA" .
_:B07 abc:attributeDataType xsd:double .
3.2 Link to Concept Classification
By using semantic web technologies we are not lim-
ited to string matching queries. We can also use
inference mechanisms based on subsuming and es-
tablished relationships between terminology and con-
cepts. To test these capabilities, we have implemented
a taxonomy with domain ontology classes. The re-
lationships between these concepts are of the type
subclassOf. Individual datasets are represented as in-
stances of the domain ontology classes. Each dataset
is described by a metadata record, which is an in-
stance of the class abc:MetadataRecord . The link
between these two instances is given by the prop-
erty abc:hasDescription (See Figure 2). The follow-
ing triples depict the relation between an instance of
abc:MetadataRecord and an instance of the domain
ontology class abc:Political.
abc:d1 a abc:Political .
abc:md1 a abc:MetadataRecord .
abc:d1 abc:hasDescription abc:md1 .
Figure 2 depicts our proposed domain ontology.
Using the domain ontology we can make inferences
regarding the class membership. From the previous
example, because abc : Political is a subclass of abc :
Boundaries and abc : Spatial, we can infer that abc :
md1 is also a member of these two classes.
The goal of this paper is to show potential uses of
semantic web technologies for metadata record man-
agement. At the moment our focus is not on the auto-
matic classification of datasets within a domain ontol-
ogy. This task can be achieved with a variety of meth-
ods for instance: Naive Bayes, Decision Rules, Neu-
ral Networks, among others. For this experiment, we
decided to create an instance of the class abc : Spatial
for each metadata record, and later add further spec-
ification of the instance randomly, only in order to
test the system query capabilities. Our future devel-
opment plans include the implementation of sophis-
ticated methods for the dataset classification. An in-
teresting work in this field, although not in the spatial
domain is (Werner et al., 2012).
3.3 Toponym Elements
In order to facilitate the identification of suitable spa-
tial datasets we define the class abc:ToponymUnit
as a subclass of geo:Feature . Instances of the
class abc:ToponymUnit are geographic features with
known, accepted names. Our system enables users to
integrate into their metadata queries spatial relations
between the spatial components of metadata records
(bounding box) and toponym instances (See Figure
To populate the class abc:ToponymUnit, we use
a country political boundaries dataset from Esri and
DeLorme Publishing Company, Inc. under a Creative
Commons Attribution-Noncommercial-Share Alike
3.0 United States License (ESRI, 2011). The
dataset is a shapefile with 668 multipolygon fea-
tures. Our goal, is to create instances of the class
abc:ToponymUnit, each instance representing an area
with a known political designation.
However, before translating the political bound-
aries dataset into triples, it was necessary to perform
the following steps: 1) Convert the multipolygon fea-
tures to polygon ones. 2) Delete polygons that we
considered too small for practical purposes. 3) Sim-
plify the remaining polygons by reducing the num-
ber of vertices. 4) Translate the political boundaries
dataset into instances and triples using a customized
Java program, implemented with Jena and GeoTools
libraries. and finally 5) Upload the triples into our
triplestore. The pre-loading processing was done us-
ing Quantum GIS and GeoTools. The final result is
Figure 4: HTML user interface: Defining a constraint
using a spatial relationship with an instance of th class
3037 instances of the class abc:ToponymUnit.
3.4 Metadata Query
In order to test our implementation, we need to en-
able users to submit queries to the catalog service
and browse through the query results. This process
is composed by the following four sub-processes :
1) Definition of the query. 2) Mapping the user re-
quest into a suitable metadata repository query for-
mat. 3) Mapping the triplestore response into an al-
lowed OGC standard. 4) The visualization of the re-
sults in the user interface (See Figure 1).
3.4.1 Definition of the Query
The OGC standard query language for services im-
plementing CSW is called Filter Encoding. This is
a language based on XML . Queries with Filter En-
coding might become long XML documents that re-
quire a strict syntax, therefore it is necessary a soft-
ware application that helps the user to compose them.
In our system this task is done in a user interface de-
ployed as a web page. It uses a combination of HTML
and JavaScript, to enable users to compose complex
In order to help the user to compose queries, the
web site requests from the Server: 1) The list of do-
main ontology classes, 2) The list of labels associated
to instances of abc : ToponymUnit. It uses this infor-
mation to populate combo boxes, allowing the user to
compose constraints (See Figure 4).
The JavaScript application receives the user input
and formats it as a XML query document following
the Filter Encoding specification. The application al-
lows multiple constraints to be linked using the oper-
ators AND and OR. When the query is completed the
user submits it as a POST. The following XML code
represents a query as formatted by the JavaScript run-
ning on the website.
3.4.2 From Filter Encoding to
We are using as our metadata repository a triplestore,
therefore it is necessary to translate request from Fil-
ter Encoding to SPARQL/GeoSPARQL. This task is
accomplished by our Servlet application. Once the
XML query arrives, the application proceeds to de-
compose it, into its constituent constraints.
In a SPARQL query, we distinguish three compo-
nents. 1) The specific elements or nodes we are re-
questing; 2) A set of triples that define a pattern the
SPARQL engine is going to look for; and 3) The fil-
ter component, where we define a set of boolean value
conditions for the triples that match the previously de-
fined pattern.
Our servlet application translates each constraint
separately into the respective set of triples pattern and
filter conditions. Using the interface the user is able
to define three types of constraints:
1. Alphanumeric attributes in the metadata record:
The user can select one attribute in the metadata
record, and perform a string matching, using the
operators PropertyIsEqualTo and PropertyIsLike.
In the later case the SPARQL implementation will
require the definition of a filter component:
would be translated to the triple pattern:
?md a abc:MetadataRecord.
?md dc:title ?xTitle.
with the filter component:
2. Domain ontology class membership: Each meta-
data record describes an entity with a class mem-
bership. This type of constraint allows the user to
identify the class membership of the entity. For
example, the constraint:
Is translated as the SPARQL triples:
?md a abc:MetadataRecord.
?ds abc:hasDescription ?md.
?ds a abc:Harvest.
3. Using toponym elements and spatial relation-
ships: In this case the user identifies a toponym
of interest, then she defines a spatial relationship
between the bounding box of the metadata record
and the geometry of the selected toponym. We im-
plement the spatial operators Disjoint,Intersects,
Contains and Within (See Figure 4). For exam-
ple, the following XML extract, indicates that the
metadata records bounding box should intersect
the geometry of Netherlands:
The constraint is translated as the following triple
?md a abc:MetadataRecord.
?md geo:hasGeometry ?boundingbox.
?boundingbox geo:asWKT ?boundingbox_wkt.
?topoUnit a abc:ToponymUnit.
?topoUnit abc:CountryName "Netherlands".
?topoUnit geo:hasGeometry ?topoGeo.
?topoGeo geo:asWKT ?topoWKT.
plus the additional filter component:
Once all the constraints have been translated, the
triples and filter elements are merged into a syntacti-
cally correct SPARQL/GeoSPARQL query, which is
submitted to the triplestore. The following code, de-
picts the SPARQL query resulting from combining all
the triple patterns and filter components from the pre-
vious examples:
{?md a abc:MetadataRecord.
?md dc:title ?xTitle.
?md geo:hasGeometry ?boundingbox.
?boundingbox geo:asWKT ?boundingbox_wkt.
?ds abc:hasDescription ?md.
?ds a abc:Harvest.
?topoUnit a abc:ToponymUnit.
?topoUnit abc:CountryName "Netherlands".
?topoUnit geo:hasGeometry ?topoGeo.
?topoGeo geo:asWKT ?topoWKT.
The response from the triplestore is then formatted
by the servlet as csw:SummaryRecord and sent to the
client website. The results are visualized in the web-
site allowing the user to examine the metadata records
(See Figure 5).
3.5 Smart Queries
A smart query requires the combination of diverse
datasources as described in (Goodwin, 2005). How-
ever, first the researcher must be able to identify the
Figure 5: HTML user interface showing the results of the
example query.
most suitable dataset for the analysis. Our implemen-
tation aims to help users in this task. By using a do-
main ontology, we improve the user’s query capabili-
ties. Our use of toponyms, allows the user to select ar-
eas of interest by name, and establish specific spatial
relationships with the dataset of interest. The actual
features of the dataset can later be obtained using the
value of abc:GetFeaturesURL, in the metadata record.
In this work we present a simplified CSW imple-
mentation with a triplestore as a metadata reposi-
tory. Our implementation has a working transla-
tor that is able to convert Filter Encoding queries
into SPARQL/GeoSPARQL ones. The system allows
complex queries that can take advantage of inference
mechanisms provided by Semantic Web technologies.
At this point, our system only uses inference based
on class to subclass relationships. However, we plan
to extend these capabilities to include relationships
between concepts and automatic class membership
determination using a domain ontology.
Our approach to capture metadata information is
generic, takes advantage of the OGC standard in-
terfaces. With our harvesting tool we were able
to create 2690 metadata records. However the in-
formation supplied by the WFS publishing enti-
ties has limitations and is in many cases incom-
plete. Our metadata records contain 1384 distinct
keywords including 383 actual URLs. However in
no case the URLs referred to any ontology or for-
malized vocabulary. From the URLs, 217 were
links to html documents, and 52 to XML docu-
ments. In both cases the documents contained ex-
tended metadata descriptions of the datasets. All
the datasets with URL of extended descriptions were
provided by one single WFS deployment (gisweb-, the rest
of the keywords were strings with no formal seman-
tics associated. Our metadata harvesting tool also
obtained information regarding the names of the at-
tributes of the dataset. In total we have obtained 6331
individual attribute names, all of them were strings
with no formal semantics associated.
The use of extended descriptions in XML and
HTML documents is not a standard practice among
the WFS publishing entities. However, in case we
find more documents of this kind, we can upgrade the
harvesting tool in order to allow it to get information
from the associated documents.
The results of our current implementation are
promising, in the near future we will implement an
automatic classification of metadata records based on
harvested metadata using a domain ontology.
This research is supported by: 1) Conseil r
egional de
Bourgogne. 2) Direction G
erale de l’Armement,
DuCharme, B. (2011). Learning SPARQL. O’Reilly Media,
Dunne, D., Leadbetter, A., and Lassoued, Y. (2012). ICAN
Semantic Interoperability Cookbooks. Technical re-
port, International Coastal Atlas Network.
ESRI (2010). GIS Best Practices: Spatial
Data Infrastructure (SDI). http://www.
infrastructure.pdf. Accessed: July 2013.
ESRI, D. (2011). World administrative units.
maps/10.0/world. Accessed on May 2013.
Goodwin, J. (2005). What have ontologies ever done for us
- potential applications at a national mapping agency.
In OWL: Experiences and Directions (OWLED).
Gwenzi, J. (2010). Enhancing spatial web seach with se-
mantic web technology and metadata visualization.
Master of science, University of Twente.
Harbelot, B., Arenas, H., and Cruz, C. (2013). Semantics
for Spatio-Temporal “Smart Queries”. In Poster pre-
sentation in the 9th. International Conference on Web
Information Systems and Technologies, Aachen, Ger-
Janowicz, K., Schade, S., Broring, A., Kebler, C., Maue,
P., and Stasch, C. (2010). Semantic Enablement for
Spatial Data Infrastructures. Transactions in GIS,
Janowicz, K., Scheider, S., Pehel, T., and Hart, G.
(2012). Geospatial Semantics and Linked Spatiotem-
poral Data - Past, Present and Future. Semantic Web
- Interoperability, Usability and Applicability, 3(4):1–
Kammersell, W. and Dean, M. (2007). Conceptual Search:
Incorporating Geospatial Data into Semantic Queries.
In Scharl, A. and Tochtermann, K., editors, The
Geospatial Web, Advanced Information and Knowl-
edge Processing, pages 47–54. Springer London.
Kolas, D. and Batle, R. (2012). GeoSPARQL User Guide.
GeoSPARQL User Guide.docx Accessed on May
Kolas, D., Hebeler, J., and Dean, M. (2005). Geospatial Se-
mantic Web: Architecture of Ontologies. pages 183–
Lopez-Pellicer, F. J., Florczyk, A., Renteria-Aguaviva, W.,
Nogueras-Iso, J., and Muro-Medrano, P. R. (2010).
CSW2LD: a Linked Data frontend for CSW.
OGC (2012). OGC Institutional Web Site. Accessed: Septem-
ber 2013.
OSGeo (2012). CQL.
/cql/cql.html. Accessed on November 2012.
Pigot, S. (2012). Using RDF as Metadata Storage. Ac-
cessed on May 2013.
Vretanos, P. A. (2005). Filter Encoding Implementation
Specification. online. Accessed on May 2013.
Werner, D., Cruz, C., and Nicolle, C. (2012). Ontology-
based Recommender System of Economic Articles. In
WEBIST 2012, pages 725–728.
Yue, P., Di, L., Yang, W., Yu, G., and Zhao, P. (2006).
Path planning for chaining geospatial web services.
In Proceedings of the 6th international conference
on Web and Wireless Geographical Information Sys-
tems, W2GIS’06, pages 214–226, Hong Kong, China.
Yue, P., Gong, J., Di, L., He, L., and Wei, Y. (2011). In-
tegrating Semantic Web Technologies and Geospatial
Catalog Services for Geospatial Information Discov-
ery and Processing in Cyberinfrastructure. GeoInfor-
matica, 15:273–303. 10.1007/s10707-009-0096-1.