=Paper=
{{Paper
|id=Vol-422/paper-4
|storemode=property
|title=Ontology-based Disambiguation of Spatiotemporal Locations
|pdfUrl=https://ceur-ws.org/Vol-422/irsw2008-submission-8.pdf
|volume=Vol-422
|dblpUrl=https://dblp.org/rec/conf/esws/KauppinenHSLVH08
}}
==Ontology-based Disambiguation of Spatiotemporal Locations==
https://ceur-ws.org/Vol-422/irsw2008-submission-8.pdf
Ontology-based Disambiguation of
Spatiotemporal Locations
Tomi Kauppinen1 , Riikka Henriksson1 , Reetta Sinkkilä1 , Robin Lindroos1 ,
Jari Väätäinen2 , and Eero Hyvönen1
1
Semantic Computing Research Group (SeCo)
Helsinki University of Technology and University of Helsinki,
P.O. Box 5500, 02015 TKK, Finland
first.last@tkk.fi,
http://www.seco.tkk.fi
2
Geological Survey of Finland,
firstname.lastname@gtk.fi,
http://www.gtk.fi
Abstract. Geographic place names are semantically often highly am-
biguous. For example, there are 491 places in Finland sharing the same
name ”Isosaari” (great island) that are instances of several geographical
classes, such as Island, Forest, Peninsula, Inhabited area, etc. Referenc-
ing unambiguously to a particular ”Isosaari”, either when annotating
content or during information retrieval, can be quite problematic and
requires usage of advanced search methods and maps for semantic dis-
ambiguation. Historical places introduce even more challenges, since his-
torical metadata commonly make spatiotemporal references to historical
regions and places using names whose meanings are non-existing or dif-
ferent in different times. This paper presents how these problems have
been addressed in a large Finnish place ontology SUO and a historical
geo-ontology SAPO. A location ontology server ONKI-Geo has been cre-
ated for publishing the ontologies and utilizing them as mashup services.
To demonstrate the usability of our ontologies, two case applications in
the cultural heritage domain are presented.
1 Introduction
Geospatial ontologies define classes and individuals for representing e.g. geo-
graphic regions, their properties, and mutual relationships [17, 16, 3]. By sharing
ontological resources in different collections and application domains interoper-
ability in terms of geographical locations can be obtained, and intelligent end-
user services such as semantic search, browsing and visualization be facilitated.
For example, in the semantic portal MuseumFinland3 [7] a location parton-
omy4 was used for annotating museum artifacts with metadata about the place
of manufacture and place of usage.
3
http://www.museosuomi.fi
4
This partonomy is a part-of hierarchy of individuals of the classes Continent, Coun-
try, County, City, Village, Farm etc.
A problem when creating a semantic cultural heritage portal is that places,
both modern and historical ones are needed but place finding and place name dis-
ambiguation is hard as there are millions of places. Moreover, geography changes
rapidly, which makes it hard 1) for the content annotator to make correct ref-
erences to spatiotemporal regions and 2) for the end-user to understand the
changes in historical geography and, as a result, to formulate the queries. For
example, many artifacts in MuseumFinland originate from regions that no
longer exist and/or are not part of Finland but of Russia with new names after
the Second World War. Finding the right names for querying, understanding to
what regions on the map the names refer to at different times, and understand-
ing how old historical names relate to modern Finnish and Russian geography
creates, at the same time, both a semantic challenge for the technology and an
important part of useful content to learn when using the portal.
We propose that (at least) three requirements should be taken into account
when utilizing location information in a system for end-users to view cultural
heritage collections:
– Precise annotation with locations Finding relevant location or place
concepts and their properties (like place type, WGS84 coordinates, hierar-
chical position) easily from geo-ontologies for annotating resources is a key
need.
– Ontology-based spatiotemporal search It is necessary to be able to use
both historical and modern locations and regions as search concepts e.g. in
a view-based, or multi-facet search.
– Context visualization of the locations An end-user needs to see the
locations in a right context, for example their neighbors, covered (i.e. over-
lapped) historical regions, and their position in a partonomical hierarchy or
on a map.
To address these requirements, we present in this paper work done for 1) mod-
eling places, their properties and changes they have overcome as two ontologies
SUO and SAPO and 2) publishing the ontologies in an location ontology server
ONKI-Geo [6]. We also 3) present two case applications implemented as a part
of the semantic CultureSampo portal [8] that use place ontologies in a search
for items related to places and show how ONKI-Geo is used in CultureSampo
as a subservice using mashup techniques.
2 Geo-Ontology SUO
Spatial structures and their mutual relations are a most essential form of geo-
graphic knowledge. The principles by which the geographic domain—and geo-
ontologies—are primarily structured from the theoretical viewpoint are topology
(the theory of boundaries, contact, and separation), mereology (theory of parts
and wholes) and geometry [14]. To facilitate spatial reasoning based on parton-
omy and subsumption hierarchies in semantic portal applications [?,8], we have
created a Finnish geo-ontology SUO 5 [5] in RDF [2] and OWL 6 .
The SUO has been populated with 1) place information from the Geographic
Names Register (PNR) provided by the National Land Survey of Finland7 and
with 2) place information from the GEOnet Names Server (GNS)8 maintained
by the National Geospatial-Intelligence Agency (NGA) and the U.S. Board on
Geographic Names (US BGN). PNR contains about 800,000 names of natural
and man-made features in Finland, including data such as place type or feature
type and the coordinates of a place. The GNS register contains similar informa-
tion of about 4,100,000 places around the world excluding places in the United
States. An analysis of PNR and GNS produced the majority of classes of the
SUO-ontology: together it contains over 800 different classes of man-made or
natural geographical places, organized into a class hierarchy, and their topo-
logical and mereological relations, as well as relations defining coordinate-based
geometry for points, curves and polygons. SUO includes classes such as city,
province, graveyard, lake, or river.
3 Historical Geo-Ontology SAPO
Names and borders for political geographic regions change over time: they are
e.g. split (e.g. former Czechoslovakia) or merged together (e.g. former East and
West Germany). Furthermore, also place names change (e.g. Zaire changed its
name to Congo). As a result, content in databases may be indexed with names
that do not exist anymore or refer to different regions than names in use today.
This creates severe problems for information indexing and retrieval.
We have addressed the problem by developing a method and a historical place
ontology SAPO for representing geographical changes [11, 12]. SAPO consists of
a small set of classes and relationships (merged, split, rename, . . . ) for repre-
senting changes, and over 1100 change instances concerning around 930 different
historical Finnish counties, the complete change history of Finnish counties in
1860–2007. SAPO uses the place types from SUO but extends it into the tem-
poral dimension by telling the begin and end times and changes concerning each
place.
Figure 1 shows a part of SAPO where two regions, namely Nuija-
maa (1944–1988) and Lappeenranta (1967–1988) have merged into one re-
gion called Lappeenranta (1989–). The merge is defined as two RDF triples
< A, mergedT o, C > and < B, mergedT o, C >, where A and B are regions that
are merged to form C.
Based on the change types, logic rules can be applied to enrich the ontology
with new useful relations. For example, in figure 1 the RDF statements covers
5
http://www.seco.tkk.fi/ontologies/suo/
6
http://www.w3.org/2001/sw/
7
http://www.maanmittauslaitos.fi/en/
8
http://earth-info.nga.mil/gns/html/
and neighborOf can be derived based on the mergedTo change. A rule for deriving
covers statements is given below:
A mergedT o C ∧ B mergedT o C −→
A covers C ∧ C covers A ∧ B covers C ∧ C covers B
Another set of rules adds the triples < A, neighborOf, B > and
< B, neighborOf, A > by checking if there are exactly two regions to be merged
and if the area of the resulting region is made of a simple polygon.
Mutual covers relations of regions can be used to compute coverages between
regions of different times, without knowing the actual polygons of the regions,
and a geo-ontology time-series of regions [11]. Our method produces the cover-
ages9 between all the places in a historical place ontology.
Fig. 1. The base model and infered model of the SAPO historical ontology. The pred-
icates of the infered triples are marked with dashed lines.
The result can be used to show the temporal contextual information about
places or in a query expansion. For example, to visualize the result of the infer-
ence and to see regions in relation with other regions, we have used the IRMA-
Sapo browser10 , depicted in figure 2. With IRMA-Sapo browser, a user can find
historical regions beginning with certain letters and also their neighbouring re-
gions, covering regions, and see them in their partonomy.
IRMA-Sapo is based on the intelligent metadata assistant IRMA [13, 15].
IRMA is generic in a sense that it can be used to visualize the context of any
concept, i.e. the properties and relationships of that concept.
9
There are a few exceptions where manual work is needed as discussed in [11].
10
http://www.seco.tkk.fi/services/irma/
Fig. 2. SAPO historical ontology in an IRMA browser. Search is supported by an
autocompletion box which retrieves all the matching historical regions. By clicking
a region, the user can further select either from the neighbouring historical regions,
covering regions, or from the partonomy hierarchy.
4 Ontology Service ONKI-Geo
ONKI-Geo [6] publishes place ontology SUO and historical place ontology SAPO
with online ontology services for humans and machines to use. The user inter-
face utilizes Ajax-techniques11 for communicating with the ONKI-Geo database
server containing the place instances. The place finder contains a simple faceted
search engine for narrowing the search along the following dimensions: 1) Place
name facet filters matching place instances using string autocompletion for their
labels. 2) Place type facet of place types (City, Island, Cemetery, etc.) is used
to focus search to places of desired types. 3) Language facet limits search to
place names in given languages (Finnish, Swedish, English, and three dialects of
Sami). 4) Time facet is used to focus search on historical place names. 5) Map
facet allows the user to specify a polygon area in which to search for the place
on the map [2]. The polygon functions as a narrowing criteria for the search in
the same way as category selections in the other facets. 6) Area facet makes it
possible to focus search on continents, countries, and their smaller regions.
The ONKI-Geo Browser is depicted in Figure 3. By using ONKI-Geo one
can e.g. find easily on the map all places of 1) some type with 2) names beginning
with some letters, and that are 3) inside a polygon, out of millions of places12 .
ONKI-Geo uses Google Maps API for visualization, and can be connected to
11
http://ajax.org
12
A related service, Geonames.org also includes a web service to return place name
information and supports search e.g. by type and name but not using e.g. a polygon.
and utilized in legacy systems using Ajax and mash-up techniques. In a museum
cataloging system, for example, places can be found by using the ONKI-Geo
user interface and by pushing the “Select” button, the corresponding URI or
coordinates are transferred from the centralized national service into the lecagy
application. A technical challenge with the ONKI-Geo service is the large size
of the SUO. The response times of the faceted search were several minutes when
using a straight-forward Jena implementation 13 . To solve the efficiency problem,
only the SUO ontology classes, without the instances, are stored in a Jena model,
and a separate indexing layer was created for the millions of instances. The
indexing of the instances is created in a relational database (MySQL) making
complex combinational searches possible and relatively fast.
Fig. 3. ONKI-Geo Browser. Search can be constrained by using the facets on the left or
by drawing a polygon on the map. By pushing the “Select” button in the left bottom
corner, the found concept or selected coordinate information is transferred into the
mash-up widget in a legacy application.
13
http://jena.sourceforge.net
5 Two Case Applications
We have implemented two case applications as a part of the semantic Culture-
Sampo portal to demonstrate the use of place ontologies in the cultural heritage
domain.
Map-search allows for searching with places on a map as illustrated in Figure
4. By clicking a place on a map, the items annotated with that place are retrieved
and shown on the right side of the map. Map-search uses simple inference by
creating a transitive closure over the part-of hierarchy.
Figure 5 illustrates the second case application. Historical regions of the
SAPO ontology can be selected in a drop-down menu on the left. Here a tem-
poral, historical place SAPO:Viipuri(1403-1943) is selected. As a result, the
polygonal boundaries of Viipuri of that time are visualized on a contemporary
Google Maps satellite image14 , a map, or on a historical map. In addition, those
modern places of ONKI-Geo that are inside the polygonal boundaries of the his-
torical region are retrieved in a mashup fashion and can be used to browse the
map. The content related to SAPO:Viipuri(1403-1943) is retrieved from an RDF
repository and is shown in this view on the right. Furthermore, content from his-
torical regions that cover (i.e. overlap) SAPO:Viipuri(1403-1943) are listed as
recommendations. The coverages are looked up from the global coverage table
[11] containing all the coverages between historical places.
6 Discussion and Related Work
One of the major problems in semantic web ontologies is that organizations
commonly mint different URIs for same resources [9]. One possible solution is to
build coreference systems that offer mappings between different URIs [9]. On the
other hand, if organizations would retrieve URIs directly from commonly used
ontology library systems and ontology servers offering a rich set of relationships
between the URIs, the integration of data sets would be more straighforward.
The idea about these ontology servers and libraries have been presented before
[4] and they have also been extensively compared [1]. However, there is little
work done for offering ontology services for semantic geospatial ontologies where
there are specific needs for disambiguation among large amount of spatiotem-
poral instances. The SPIRIT spatial search engine [10] provides facilities to find
web resources relating to places referred to in a query. The geo-ontology within
this search engine is used for the purposes of information retrieval by modeling
the geographical terminology and the spatial structure of places. It supports
for example query disambiguation by recognizing the variant place names and
historical alternatives.
Our approach is to provide a rich set of spatial and temporal properties for the
place instances in order to facilitate the process of disambiguating between them
and for finding the needed instance (i.e. the URI) to be used e.g. in annotation.
14
http://maps.google.com/
Fig. 4. A search with regions without temporal extensions.
We believe that if there is not enough evidence—i.e. properties and relationships
between URIs—that a URI served by a service is the needed one both temporally
and spatially then organizations tend to either mint their own URIs for the
purpose or to select URIs somewhat randomly from different ad hoc -services.
This paper presented how two ontologies, SUO and SAPO, can be used to
represent and visualize different kinds of locations and their changes in time. We
also presented the ONKI-Geo ontology server for publishing SUO and SAPO,
and for finding the correct places for annotations. Finally, we presented two case
applications that demonstrate that the presented ideas work aĺso in practise in
the cultural heritage domain.
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