=Paper=
{{Paper
|id=None
|storemode=property
|title=Matching geospatial ontologies
|pdfUrl=https://ceur-ws.org/Vol-946/om2012_poster9.pdf
|volume=Vol-946
|dblpUrl=https://dblp.org/rec/conf/semweb/DuAJH12
}}
==Matching geospatial ontologies==
https://ceur-ws.org/Vol-946/om2012_poster9.pdf
Matching Geospatial Ontologies
Heshan Du1 , Natasha Alechina1 , Mike Jackson1 , Glen Hart2
1
University of Nottingham
2
Ordnance Survey of Great Britain
In recent years, multiple geospatial ontologies have been developed for a
wide range of different spatial databases. In addition, the development of vol-
unteered geographic information both challenges and provides opportunities to
the traditional authenticated geospatial information. Though volunteered geo-
graphic information is typically not as reliable and structured as the authen-
ticated geospatial information, it often reflects changes in the real world more
quickly and contains richer information related to human activity [1]. It is there-
fore desirable to link the corresponding information from disparate geospatial in-
formation sources, allowing users to use them synergistically. Aligning disparate
geospatial ontologies is an essential element to realizing this.
We propose a new semi-automatic method to align geospatial ontologies,
based on coherence and consistency checking in description logic, as well as do-
main experts’ knowledge. We evaluate it on real world data and compare it to
two state of the art ontology mapping systems, CODI [2] and LogMap [3]. By
a geospatial ontology we mean an ontology which contains both definitions of
geospatial concepts in its TBox and facts about geospatial individuals in its
ABox. When designing our approach, we assume that the TBox is not very
large, but contains concepts which are more ambiguous, compared to for ex-
ample biomedical ontologies. We also assume that geospatial individuals have
geometry and location information. In common with other approaches, we use
additional disjointness axioms to improve the quality of mapping. Since they
are not part of the original ontology and may be wrong, we treat generated
disjointness axioms as assumptions retractable by users. We treat original ontol-
ogy axioms as correct and not retractable. Given two geospatial ontologies, our
method has two main steps: generating assumptions and calculating a consistent
and coherent assumption set (CAS) which contains the mapping.
Step 1 : Retractable assumptions include disjointness axioms and mapping
axioms. For TBoxes, disjointness axioms are generated for sibling classes. Ini-
tial mapping axioms between TBoxes are generated by stating equivalence of
atomic concepts with identical names. Initial mapping axioms between ABoxes
are generated based on three criteria: location, lexical labelling, and cardinality
of mapping (one-to-one or one-to-many). We ensure that the geospatial instances
from different sources are first represented at the same scale and using the same
coordinate reference system scaling and transforming the input data as neces-
sary. Given two instances, if their geometries are not spatially disjoint, we first
generate a candidate ‘sameAs’ axiom for them. (When dealing with polygon
geometries, the geometry checking is based on spatial disjointness, rather than
shapes or sizes of geometries or their percentages of overlapping, because two
2
corresponding geospatial individuals may be represented differently in different
datasets, and the representations may be of different geometry accuracy lev-
els.) Then, each correspondence will be checked lexically. If the labels of the
instances cannot be matched, we remove the correspondence. After that, the
mapping will go through cardinality checking. In the case that several instances
are mapped to the same instance, we change ‘sameAs’ relation to ‘partOf’ rela-
tion in the corresponding axioms. The geometry, lexical and cardinality checking
are all necessary, since different geospatial individuals may share the same label
or the same location in an ontology, and a same geospatial individual may be
represented as a whole in one ontology, whilst as several parts of it in the other.
Step 2 : Two ontologies are aligned by calculating a CAS with respect to them.
We use Pellet [4] to check consistency and coherence of overall information.
While inconsistency or incoherence exists, minimal inconsistent or incoherent
assumption sets (MIAs) will be calculated and visualized clearly, allowing domain
experts to correct them, until a CAS is obtained. We decide against automatic
fixing of MIAs since none of the methods give entirely reliable results.
The method is implemented as a system called GeoMap. We evaluate it using
the Ordnance Survey of Great Britain (OSGB) Buildings and Places ontology [5]
and the OpenStreetMap (OSM) controlled vocabularies [6], which are represen-
tatives of formal and informal geospatial ontologies respectively. The data used
in evaluation is available at http://www.cs.nott.ac.uk/~hxd/GeoMap.html.
GeoMap, CODI [2] and LogMap [3] are employed to align the OSGB Build-
ings and Places ontology and the OSM ontology, extended with additional dis-
jointness of siblings axioms. Based on manual evaluation, the precision rates
of GeoMap, CODI and LogMap terminology mappings are 89%, 76% and 70%
respectively. CODI generates 5 more correct mapping axioms than GeoMap,
whilst LogMap generates 11 less. In the GeoMap instance mapping, more than
95% correspondences are reasonable. The experimental result shows that, when
aligning geospatial ontologies, using geometry or location information helps, and
domain experts are indispensable.
References
1. Jackson, M.J., Rahemtulla, H., Morley, J.: The Synergistic Use of Authenticated and
Crowd-Sourced Data for Emergency Response. In: 2nd International Workshop on
Validation of Geo-Information Products for Crisis Management (VALgEO). (2010)
2. Niepert, M., Meilicke, C., Stuckenschmidt, H.: A Probabilistic-Logical Framework
for Ontology Matching. In: American Association for Artificial Intelligence. (2010)
3. Jiménez-Ruiz, E., Grau, B.C.: LogMap: Logic-Based and Scalable Ontology Match-
ing. In: International Semantic Web Conference (1). (2011) 273–288
4. Sirin, E., Parsia, B., Grau, B.C., Kalyanpur, A., Katz, Y.: Pellet: a Practical OWL-
DL Reasoner. Web Semantics: Science, Services and Agents on the World Wide
Web 5 (June 2007) 51–53
5. Hart, G., Dolbear, C., Kovacs, K., Guy, A.: Ordnance Survey Ontologies.
http://www.ordnancesurvey.co.uk/oswebsite/ontology (2008)
6. OpenStreetMap: The Free Wiki World Map. http://www.openstreetmap.org (2012)