=Paper=
{{Paper
|id=None
|storemode=property
|title=OWL Based Formalisation of Geographic Databases Specifications
|pdfUrl=https://ceur-ws.org/Vol-674/Paper115.pdf
|volume=Vol-674
|dblpUrl=https://dblp.org/rec/conf/ekaw/AbadieMM10
}}
==OWL Based Formalisation of Geographic Databases Specifications==
https://ceur-ws.org/Vol-674/Paper115.pdf
OWL-based formalisation of geographic databases
specifications
Nathalie Abadie Ammar Mechouche Sébastien Mustière
Institut Géographique National, Institut Géographique National, Institut Géographique National,
Laboratoire COGIT Laboratoire COGIT Laboratoire COGIT
73 Avenue de Paris 73 Avenue de Paris 73 Avenue de Paris
94160 Saint-Mandé, France 94160 Saint-Mandé, France 94160 Saint-Mandé, France
+33 1 43 98 80 03 +33 1 43 98 80 00 + 71 25 +33 1 43 98 81 49
nathalie-f.abadie@ign.fr ammar.mechouche@ign.fr sebastien.mustiere@ign.fr
ABSTRACT aim at helping a user in retrieving geo-data that represent a
specific geographic concept, such as „buildings’, even if feature
The ability to share and combine geographic data from different class names of available datasets are totally different. In the latter
information sources in a consistent way is a key issue for enabling cases, recent approaches [4][5][6] provide geo-databases experts
successful implementation of Spatial Data Infrastructures (SDIs). with a graphical interface to help them in manually describing
This can only be done through a deep understanding of databases their schemas and specifying mappings between source and target
structure and content. In this poster, we propose to do that schemas.
through the elicitation and formalisation of geographic database However, each geo-data producer has its own rules for data
specifications, relying on OWL ontologies, as recommended in capture, and its own point of view about the geographic real world
the semantic Web community. We thus propose a general [7]. As an example, if a feature class is named „Building‟, it may
ontology for eliciting key concepts manipulated by data actually designate only permanent buildings, or include precarious
specifications, and rules to build local ontologies representing buildings, such as cabins, or huts. Besides, a geographic database
knowledge contained in specific data specifications. is produced at a specific scale of analysis and geographic features
are then captured in the database consistently with this specific
Categories and Subject Descriptors level of detail. For example, only buildings of area greater than 50
H.2.8 [Database Management]: Databases Applications – m2 may be captured. Furthermore, the geometric representation of
Spatial databases and GIS. a given geographic feature may vary: a building may be
represented by a polygon representing its perimeter or by a point
General Terms captured at its centre.
Management, Standardization. All these selection and representation criteria are stored in specific
textual documents, used as guideline for data capture, namely the
database specifications. They are a very rich source of knowledge
Keywords about geo-data semantics and their use in a schema matching
Geographic Database Specification, Ontologies, Geo-data process could help in identifying and solving complex
Semantics, OWL heterogeneities. Let us consider two different databases covering
the same geographical space. The first one has a feature class
1. WHY FORMALISING named „Building‟ which represents only “buildings of area greater
than 20 m2”, while the second one has a feature class named
SPECIFICATIONS? „Built-up area‟ which represents “buildings of area greater than 50
In the last decades, the increase of geographic data acquisition m2”. Comparing these feature classes‟ specifications enables to
campaigns has resulted in a huge amount of diverse, find the following mapping rule: „Building‟ instances of area
heterogeneous and distributed geographic data sources. However, greater than 50 m2 represent the same real world buildings as
even if these data represent the same geographic real world, there „Built-up area‟ instances. Providing a schema matching
is a great heterogeneity between them. Consequently, the ability to application with formal specifications would therefore enable to
share and combine geographic data from different sources in a automatically find such complex mapping rules between
consistent way is a key issue for enabling their efficient usability. heterogeneous geo-databases.
Previous geo-data integration efforts mainly focused on syntactic
heterogeneities through the development of standards. Semantic 2. THE SPECIFICATIONS ONTOLOGY
interoperability, which addresses more complex problems, is still Several formal models for geographic database specifications
investigated. Actually, recent works mainly focused either on geo- have already been proposed [8][9]. As formalisation of data
data discovery and retrieval or on transformation of geo-data specifications in SDIs is a kind of elicitation of data semantics in
schema. In the former case, most of the proposed approaches a Web environment, we propose to rely on semantic Web
[1][2][3] use a global domain ontology to specify the precise standards to do so: our approach is based on ontologies developed
meaning of geo-data, either by renaming feature classes with with the Ontology Web Language (OWL 2 [10]).
ontology labels, or thanks to semantic annotations. They rather
A first step to formalise specifications is to define unambiguously specification is described in a specification application ontology
key concepts commonly used in geo-database specifications. In (LSO) which uses SO‟s concepts. A tool enabling automatic
other words, we define a domain ontology, named “Specifications comparison of formal specifications is being implemented. It aims
Ontology” (SO, see Figure 1). This ontology SO only contains at providing expressive schemas mappings between geographic
concepts specific to geographic data specifications. It relies in heterogeneous databases, for schema translation or schema
turn on more general ontologies, for example for defining basic integration purposes.
geometric types [11]. For example, this domain ontology SO
formalises the concepts of data source and centreline, which are 5. ACKNOWLEDGMENTS
commonly used in many data specifications. This work is partly funded by the French Research Agency
through the GeOnto project ANR-O7-MDCO-005 on “creation,
alignment, comparison and use of geographic ontologies”
(http://geonto.lri.fr/).
6. REFERENCES
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4. CONCLUSION http://www.w3.org/TR/owl2-primer/
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geographic databases semantics by describing the link between
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