=Paper=
{{Paper
|id=Vol-518/paper-2
|storemode=property
|title=A Qualitative Approach to Vague Spatio-Thematic Query Processing
|pdfUrl=https://ceur-ws.org/Vol-518/terra09_submission_2.pdf
|volume=Vol-518
}}
==A Qualitative Approach to Vague Spatio-Thematic Query Processing==
https://ceur-ws.org/Vol-518/terra09_submission_2.pdf
A Qualitative Approach to Vague Spatio-Thematic
Query Processing
Rolf Grütter and Thomas Scharrenbach
Swiss Federal Research Institute WSL, an Institute of the ETH Domain, Zürcherstrasse 111,
8903 Birmensdorf, Switzerland
{Rolf.Gruetter, Thomas.Scharrenbach}@wsl.ch
Abstract. In order to support the processing of spatial queries, spatial knowl-
edge must be represented in a way that machines can make use of it. In ontol-
ogy-based geographic information systems, a challenge thus is to enhance the-
matic knowledge with spatial knowledge. A way to achieve this is to combine
existing approaches to spatial knowledge representation with ontologies. In this
paper an implementation of the Region Connection Calculus (RCC) in the Web
Ontology Language (OWL) augmented by DL-safe rules is used in order to rep-
resent spatio-thematic knowledge. It is shown how the represented knowledge
supports the processing of queries using (possibly vague) spatial concepts.
Thereby, the division of land into administrative regions, rather than, for in-
stance, a metric system, is taken as a frame of reference for evaluating close-
ness. Hence, closeness is evaluated based on purely qualitative criteria. Since
colloquial descriptions typically involve qualitative concepts, the presented ap-
proach is expected to align better with the way human beings deal with close-
ness than a quantitative approach. The paper is discussed w.r.t. related work and
an overview of possible future extensions is provided.
1 Introduction
Fueled by a joint initiative of research institutes and industrial organizations towards
the Semantic Web, knowledge representation has regained considerable attention
through the last decade [1]. Technically, the initiative was committed to advance De-
scription Logics (DLs), f.k.a. terminological systems, as a means for capturing the
terminological and assertional knowledge of a domain and for inferring new knowl-
edge from existing. This kind of knowledge has been (and continues to be) made
available by so-called ontologies [2].
Ontologies are increasingly integrated into applications in order to support seman-
tic interoperability and to provide a homogenous view of heterogeneous data [3, 4]. In
geographic information systems, where mereological considerations are all-important,
for instance, in order to process spatial queries, a challenge is to enhance ontologies,
usually representing purely thematic knowledge, with spatial knowledge [5, 6]. A way
to achieve this is to combine existing logic-based approaches to spatial knowledge
representation with description logics [7, 8, 9, 10].
In this paper, an implementation of the Region Connection Calculus (RCC) in the
Web Ontology Language (OWL) augmented by DL-safe rules is used in order to rep-
2 Rolf Grütter and Thomas Scharrenbach
resent spatio-thematic knowledge. We show how such a representation can be applied
for answering queries involving (possibly vague) spatial concepts. The primary goal
is to demonstrate how state-of-the-art technology can be used for this purpose. While
pursuing this goal, some additions to the theory of spatial knowledge representation
are made. The basic idea underlying our approach is to take the division of land into
administrative regions, rather than, for instance, a metric system, as a frame of refer-
ence for evaluating closeness. Accordingly, closeness is evaluated based on purely
qualitative criteria. Since colloquial descriptions typically involve qualitative con-
cepts, our approach is expected to align better with the way human beings deal with
closeness than a quantitative approach.
The paper is organized as follows: Section 2 provides an overview of recent work
on vague spatial concepts. Insights gained from this overview are used later in the pa-
per when describing and implementing the notion of spatial closeness. Section 3 pro-
vides a short introduction into DL-safe rules which are used – together with OWL DL
and RCC – for the representation of qualitative spatio-thematic knowledge. In Section
4, the vague notion of spatial closeness is introduced into RCC and its implementation
in DL augmented by DL-safe rules is outlined. In Section 5, the approach is applied to
spatio-thematic query answering in the Web. Section 6 discusses the approach and
Section 7 concludes with an overview of future work.
2 Related Work
There is a bulk of work about vague spatial concepts in both philosophy and geo-
graphic information science. In this paper, we limit our discussion to the most recent
works. For a comprehensive survey, particularly of approaches using fuzzy logic or
contextual information, the interested reader is referred to [11].
In [12] an experiment with human subjects concerning the vague spatial relation
“near” between places in environmental space is presented. 1 An environmental space
is referred to as the space of buildings, neighbourhoods, and cities, without considera-
tion of symbolic representations such as maps. In order to better understand how hu-
mans conceptualize nearness and to test the fit of formal theories to human concepts,
the author seeks to apply appropriate theories to the data resulting from the experi-
ment. Amongst other insights into the conceptualization of “nearness”, the experiment
shows the importance of scale factors introduced by the context of the reference
place. This supports our claim made in Section 4 that scales, more precisely, the cate-
gories in which terms human subjects think, are an important contingency of the no-
tion of closeness. 2
In [13] a qualitative representation for spatial proximity that accounts for absolute
binary nearness relations is introduced. The formalism is based on the notion of per-
ceived points, called sites, in a point based universe. Proximity concepts are deter-
mined by the parameters of distance between two sites and weight of each of those
1 In the literature the term “near” is often used to denote the same concept as “close to”.
2 cf. “In the discussion of proximity perception and cognition, the term ‘scale’ refers to the size
of the frame of reference in a perceiver’s mental map, i.e. the spatial extent of the area that is
considered when a distance is assessed.” [11, p. 162]
A Qualitative Approach to Vague Spatio-Thematic Query Processing 3
sites. These parameters are drawn from the concept of Generalized Voronoi Dia-
grams, i.e., Power Diagrams.
The approach introduced in [13] and that presented here have in common that the
qualitative description of nearness is based on a qualitative representation of distance:
in their case Voronoi diagrams transform (quantitative) distances into a network of
(qualitative) topological relations. This is different from all other approaches dis-
cussed in this paper, where a mapping mechanism between qualitative and metric dis-
tance measures is established (or implied). While the authors link their concept of
nearness to the topological relations equality, external connectedness and inclusion –
which can also be expressed by RCC – this link is established by the areas of influ-
ence of perceived points in a point based universe. Polygons and spatial relations be-
tween polygons are not considered. While the approach is appealing in its formal
strictness and the cognitively useful models and interpretations provided, it does not
address the issue of grounding. In particular, it is not clear how the weights w(p) asso-
ciated with every site p are retained from the abstracted “real world” entity.
In order to enable metric systems (such as GIS) to translate between linguistic
proximity measures and metric distance measures, a statistical approach to context-
contingent proximity modelling is presented in [11]. Relevant context factors are cho-
sen that influence, according to empirical studies, the way human beings reason about
proximity. The presented translation mechanism works in one direction only: Given
the corresponding metric distance measures and context information, linguistic prox-
imity measures are “predicted”. This direction does not support the obvious transla-
tion of local prepositions, such as “near” or “far”, used in natural language queries
into distance measures processed in metric systems. In [14] the authors note that the
information required to bind the numerous context variables may not be available in a
practical application, and hence it is difficult to see how they would be implemented
on a large scale.
According to [15], the answer to whether something is near or not depends on the
context in which the question is asked and the nature of the objects being compared.
In [14] ontologies are used to make explicit the vague spatial relation “near” for data-
base querying. In order to keep it implementable in a practical system, the algorithm
used to calculate the relation “near” is relatively simple. It only uses two contextual
parameters, namely Euclidean distance from a reference point and density of the fea-
ture class. Despite its simplicity it achieves perfect precision and recall when applied
to a (small) number of test sets obtained by asking people which objects were near to
each of the reference points. Different from the approach presented here, the authors
base their algorithm on quantitative parameters, namely Euclidean distance and grav-
ity (which is a measure of how objects are distributed), and not on qualitative rela-
tions. Furthermore, they reduce the objects considered to centroids and the calcula-
tions to point calculations whereas we consider spatial regions (i.e., polygons).
In [16] an approach to geographic information retrieval integrating topological,
geographical and conceptual matching is presented. For topological matching topo-
logical relations are extracted from overlaying data layers; for geographical matching
constraints are obtained from dictionaries; for conceptual matching a geographic on-
tology is used. A constraint, provided as an example, defines two geographic objects
(points or polygons) as near provided they are connected by a third object (an arc,
e.g., a road), the length of which is less than 1 kilometre. Different from the approach
4 Rolf Grütter and Thomas Scharrenbach
presented here, a metric distance measure thus is a necessary condition for nearness,
although not a sufficient. However, the framework seems general enough to be
aligned with that presented here.
3 Preliminaries
The presented framework uses a number of spatial relations from different RCC sub-
languages, particularly RCC-8, and a composition rule. It also uses the subsumption
hierarchy of RCC relations and a sum function as introduced in [17]. These spatial
notions are implemented in OWL DL augmented by DL-safe rules. We thus assume
that the reader is familiar with RCC [17, 18] and description logics [19], particularly
OWL DL [20]. 3
As mentioned, the composition rule of the framework is implemented as a DL-safe
rule. DL-safe rules are function-free Horn rules with the restriction that each variable
in the rule occurs in a non-DL-atom in the rule body [22]. This is achieved by adding
special non-DL-literals such as (x) to the rule body, and by adding a fact (a) for
each individual a to the knowledge base. For instance, the RCC-5 composition rule
"x"y"z [PP(x, y) EQ(z, y) PPi(z, x)] is implemented as the DL-safe rule properPar-
tOf(x, y) equalTo(z, y) (x) (y) (z) inverseProperPartOf(z, x) where (x),
(y) and (z) are non-DL-literals. 4 While in theory DL-safe rules support complex,
i.e., disjunctive, heads (respectively negation in the rule body) [23, 24], there is cur-
rently no implementation that supports this feature. However, since RCC relations de-
scribe a closed world [17], it is always possible to replace a negative atom, for in-
stance disconnectedFrom(z, y), by a, possibly auxiliary (cf. Section 4.1), positive
atom, for instance connectsWith(z, y).
4 Representing Spatio-Thematic Knowledge
4.1 Defining Closeness in RCC
A basic assumption underlying our approach is that administrative regions are social
artifacts and their organization is largely, if not entirely, motivated by the property of
spatial closeness. To be more precise, administrative regions are assumed to mirror
how a collective perceives spatial closeness on increasing scales of social organiza-
tion. We will come back to this when applying the approach to an example query.
Since administrative regions are typically organized in partitions it is necessary to
introduce the notion of a partition and to reformulate it in a way that is compliant with
a model-theoretic interpretation of RCC, the formalism used for expressing closeness.
3 An approach similar to RCC which is based on the description of topological relations be-
tween two spatial regions was introduced as the 9-intersection model in [21].
4 In DL-safe rules, all variables are universally quantified.
A Qualitative Approach to Vague Spatio-Thematic Query Processing 5
Compliance with this kind of interpretation is a requirement for the implementation of
RCC in a DL knowledge base and rule base in Section 4.2. Further, for asserting
closeness between individual regions, we must slightly extend RCC and introduce
closeness by an additional relation. The idea is that, given the conceptualization of a
user in terms of a query and a partially ordered and typed system of partitions, close-
ness can be evaluated by a composition rule.
Definition 1a (Partition). A partition is defined as a (possibly improper) subset of the
power set of a set Y, denoted by (Y i ) i Î I Í (Y), for which holds
Y = i Î I Y i where I is a finite index set;
Y i Ç Y j = Ø for i ≠ j;
Y i ≠ Ø for all i Î I.
In this definition, Yi and Y refer to sets of points in a point-based universe. As men-
tioned, for reasons of compatibility with the model-theoretic semantics of DL, we use
a non-standard interpretation where regions are interpreted as individuals, and not as
sets, in an abstract domain. We thus reformulate the definition using the Boolean
RCC function SUM and the RCC relation DR (i.e., “discrete from”). As is customary,
we use lower case letters for variables denoting individuals.
Definition 1b (Partition in RCC). A family of regions (xi)i Î I is a partition of a re-
gion y if the following holds:
y = SUMi Î I xi where I is a finite index set; 5 6
"xi"xj DR(xi, xj) for i ≠ j;
regions (xi)i Î I are named for all i Î I.
We only consider partitions where the elements are typed by kinds of administra-
tive regions, for instance, Commune(xi) says that xi is of type Commune. For regions we
do not allow multiple typing, that is, the concepts used for typing are mutually dis-
joint. Similarly, a given type is used for a single partition only. This allows distin-
guishing the partitions by their types.
In order to account for the different scales of social organization we define a par-
tial order on the system of partitions in RCC by comparing partitions w.r.t. their
granularity.
Definition 2 (Partial Order on Typed Partitions in RCC). Let C(xi)i Î I and D(yj)j Î J
be partitions of the same region of types C and D, respectively. We say that C(xi)i Î I is
more fine-grained than D(yj)j Î J, denoted by C(xi)i Î I D(yj)j Î J, if each element of
C(xi)i Î I is a (possibly improper) subset of an element of D(yj)j Î J. A partial order on
typed partitions is reflexive, transitive and antisymmetric.
This means that each element of D(yj)j Î J is partitioned by elements of C(xi)i Î I. For
instance, Commune(xi)i Î I and District(yj)j Î J are typed partitions of a canton and each
element of District(yj)j Î J is partitioned by elements of Commune(xi)i Î I.
i Î I xi is defined as "z [C(z, y) « ڀi Î I C(z, xi)] [17]. C stands for “connects with”.
5 SUM
6 This implies "x P(x , y). P stands for “part of”.
i i
6 Rolf Grütter and Thomas Scharrenbach
Definition 3 (Minimal Partial Order on Typed Partitions in RCC). We say that a
partial order on typed partitions is minimal w.r.t. a given conceptualization, denoted
by C(xi)i Î I min D(yj)j Î J, if the conceptualization does not provide a type for any
(wk)k Î K such that C(xi)i Î I (wk)k Î K D(yj)j Î J. A minimal partial order on typed
partitions is intransitive.
For instance, if a given conceptualization provides the administrative types District
and Commune, any partial order comprising a non-typed partition of intermediate
granularity is not minimal.
For asserting closeness between individual regions we slightly extend RCC and in-
troduce the relation CL(x, y) which is read as “x is close to y”. In accordance with em-
pirical evidence [12], closeness is introduced as a weakly asymmetrical relation. This
means that the relation is symmetrical, if x and y are members of the same partition,
but asymmetrical, if y is a member of a more fine-grained partition than x or else, if x
is a non-administrative region. 7
Definition 4 (Closeness in RCC). Given a region xi of a partition used as a referent in
a query, a type C of a conceptualization for xi and a minimal partial order on typed
partitions C(xi)i Î I min D(yj)j Î J, closeness in RCC can be inferred by the composition
rule "xi"yj"z [P(xi, yj) XC(z, yj) CL(z, xi)].
In this definition, XC(z, yj), read as “exclusively connects with”, is an auxiliary re-
lation. Its main purpose is to prevent the transitive property of P(xi, yj), which has
been overridden by the definition of a minimal partial order on typed partitions, from
being reintroduced through the backdoor of the composition rule. Note that since the
relation is directed from z to yj, transitivity is excluded by removing Pi(z, yj) (i.e., “in-
verse part of”) and its subrelations from C(z, yj).
Definition 4 shows that closeness depends on the type of region used as a referent
in a query, hence on the way a user conceptualizes a domain. This includes the scale
on which spatial relations are to be evaluated. It also depends on partitions into ad-
ministrative regions reflecting how a collective perceives spatial closeness on increas-
ing scales of social organization. As a result of this dependency, CL(z, xi) is undefined
unless it is related to a minimal partial order on typed partitions. A comprehensive ex-
ample is provided in Section 5.
4.2 A DL Knowledge Base and Rule Base for RCC
The knowledge required for answering (possibly vague) spatio-thematic queries can
be represented by a DL knowledge base consisting of a TBox and ABox ,
= {, }, and a rule base for DL-safe rules.
Among other things contains a number of concept inclusion axioms that intro-
duce kinds of regions. It is worth recalling the definition of an ontology as an explicit,
formal specification of a shared conceptualization [2]. Accordingly, the introduced
categories are not arbitrary. They are social artifacts and reflect how a collective
7 In [12] it is argued that for nearness the subject-referent dichotomy plays a dominant role in
that the referent creates the scale in which the relation has context.
A Qualitative Approach to Vague Spatio-Thematic Query Processing 7
thinks that the world (or a piece thereof) is structured. In the long run, a collectively
shared conceptualization is furthermore not invariant but evolves together with the
development of a society and a country.
In order to implement RCC in DL, the subsumption hierarchy of RCC relations
[17] is represented as a hierarchy of binary role inclusion axioms in the TBox. The
RCC relation P(xi, yj) and its subrelations are implemented as functional roles, thereby
ensuring that an individual xi is only part of a single region yj. This overrides the tran-
sitivity of the RCC relation P(x, y), which prevents, for instance, communes to be re-
lated to cantons (or to countries or continents if these were represented). Partitions are
represented in by (anonymous) concepts that are made up of individual names, also
called nominals, {x1, …, xn}. Nominals are linked to types by concept inclusion axi-
oms of the form C {x1, …, xn} stating that the set of individuals in the interpretation
of C is a (possibly improper) subset of the individuals in the interpretation of {x1, …,
xn}. In order to disallow multiple typing the concepts used for typing are defined as
mutually disjoint, C D.
In order to populate the ABox, known RCC relations between individual regions
are asserted as role assertions. Particularly, partitions are asserted as of partOf(xi, yj),
or any of its subrelations, for all applicable xi Î {x1, …, xn} and yj Î {y1, …, ym}. In
so doing, is closed w.r.t. nominals denoting administrative regions. 8 A minimal
partial order on typed partitions is implemented by asserting partOf(xi, yj), or any of its
subrelations, exclusively for those pairs of individuals (xi, yj) for which hold C(xi)i Î I
min D(yj)j Î J. also contains facts about individual regions in terms of concept as-
sertions.
The composition rule "x"y"z [P(x, y) XC(z, y) CL(z, x)] is implemented in
by the DL-safe rule partOf(x, y) exclusivelyConnectsWith(z, y) (x) (y) (z)
closeTo(z, x) where (x), (y) and (z) are non-DL-literals. In order to make the
rule DL-safe, a fact (a) is asserted for each individual a in the ABox. The rule is
read as “A region z is close to a region x if x is part of a region y and z exclusively
connects with y where the identity of all regions is known.”
4.3 Processing Vague Spatio-Thematic Queries
The concepts implicitly and explicitly used in a query reveal how a user conceptual-
izes a domain. Thereby the user is assumed to be a member of the social collective in
question. Query concepts can be used to determine the scale on which closeness is to
be evaluated. They translate what is often referred to as the context of a vague con-
cept, such as closeness, from contingencies in the real world into linguistic con-
straints. We assume a query of the form "z [Q(z) CL(z, a)] which is expected to re-
turn the set of those individuals of type Q that are close to a given individual a of a
partition. In this query, the type of individual a, for instance C(a), sets the scale for
the evaluation of closeness.
8 Note that we use partOf(x , y ) and its subrelations only for asserting partitions into administra-
i j
tive regions.
8 Rolf Grütter and Thomas Scharrenbach
Algorithm 1. Function CLOSETO computes (Q $closeTo.{a})(z) from and .
FUNCTION CLOSETO
INPUT: Knowledge Base = {, }, Rule Base ,
Concept Q, Individual a
OUTPUT: Set
1. {b} ← {b | partOf(a, b)}
2. V ← {vi Î I | exclusivelyConnectsWith(vi, b)}
3. W ← {wj Î J | Q(wj)}
4. Z ← V Ç W
5. OUTPUT Z
The query "z [Q(z) CL(z, a)] is implemented in DL by the concept description Q
$closeTo.{a}. Given an ABox and a concept description Q $closeTo.{a}, the
retrieval problem is thus to find all individuals z in such that (Q $close-
To.{a})(z). Algorithm 1 shows the steps (1–5) to take when processing a query. Note
that in order to process steps 1 and 2 the composition rule is required.
5 Applying the Approach to an Example Query
As stated in Section 4, we assume that administrative regions mirror how a collective
perceives spatial closeness on increasing scales of social organization. In order to ver-
ify our assumption, it is necessary to recall the design principles underlying the or-
ganization into administrative units. For example, the following official statement
clarifies the purpose of districts as administrative units:
“The administrative districts ... perform decentralized administrative tasks of the
cantons, particularly in the areas of health (district hospitals, public health), to some
extent education (district schools), judiciary (district courts) and general administra-
tion (taxation, business failures, etc.). In several cantons the administrative districts,
furthermore, correspond to the electoral wards.” [25]
The statement implies that the organization of districts is largely, if not entirely,
motivated by the spatial closeness of the communes. District hospitals, for instance,
are decentralized entities of the health care delivery system. They might have been es-
tablished with the intention to keep the distance between the patients (living in the
communes) and the care providers small. This obviously reflects the experience of
human subjects who perceive this distance as small. Similar arguments apply to dis-
trict schools, district courts and electoral wards. It is, therefore, reasonable to claim
that the organization of districts is motivated by the property of spatial closeness. The
organization of other administrative regions can be motivated in a similar way.
Search engines on the Web are weak in supporting spatial queries. This can be
demonstrated with the following example. Suppose we are looking for an answer to
the question “Which landscapes and natural monuments of national importance are
close to Aesch ZH?” where Aesch ZH is a commune in the canton of Zurich, Switzer-
land. Searching with the strings (i.e., landscapes close to Aesch ZH) returns 1,350 matches. 9 None of
the top 30 deal with a landscape.
This poor answer could be a result of there being no landscapes close to Aesch ZH
or there being no resources related to landscapes close to Aesch ZH on the Web. In an
attempt to exclude the second, we search for
which is the name of a landscape of national importance potentially close to Aesch
ZH. Searching for Albiskette-Reppischtal returns 230 matches. None of these matches
appears among the top 30 of the initial search. Worse, none of the scanned results
from the initial search indicates that the search engine really understood the query.
The knowledge required for answering the example query is represented in a con-
sistent DL knowledge base = {, } and DL-safe rule base as published on
http://www.wsl.ch/personal_homepages/scharren/download/terra_cognita_2009.owl.
10 The description language used for the is OWL DL [20]; the DL expressivity is
. It is clear that the representation of relevant knowledge is not sufficient.
In order to answer the example query a search engine must be able to make use of it.
The search engine used above does not have this ability. We used Pellet 2.0 in order
to test the presented approach. Pellet 2.0 is a DL reasoner that integrates a SPARQL
query-engine and a rule engine for the processing of DL-safe rules. 11 It is able to han-
dle all aspects of the introduced framework. Note that a detailed discussion of suc-
cessful search algorithms is outside the scope of this paper.
The example query can be formalized in DL as (Landscape $closeTo.{Aesch})(z)
where Aesch is an element of a partition. The query is processed as indicated in Sec-
tion 4.3 and the result set {Albiskette-Reppischtal} contains a landscape (of national im-
portance) which is close to Aesch. Accordingly, a logically enabled search engine op-
erating on the and is expected to return the 230 matches for Albiskette-
Reppischtal when queried with the strings , which it does not at present. 12
6 Discussion
Valid evaluations of the example query include cases where Aesch is a (possibly in-
verse) part of, partially overlaps or is externally connected to a landscape. The current
release of the reasoner used for the example does not allow excluding these cases in a
single query (cf. Section 3). They could still be excluded by querying all landscapes
that are not in any of the above relations to Aesch and by intersecting the result set
with the result set from the example query.
Closeness is a vague concept, in the sense that there exist borderline cases for
which it is difficult to decide whether they are covered by the concept or not [12].
9 http://www.google.ch
10 Please contact the authors if you wish to access the Web page and it is no longer maintained.
11 http://clarkparsia.com/pellet
12 Further queries that are successfully processed include communes, districts or landscapes ad-
jacent to a given commune or district; communes close to a given commune; districts close to
a given commune or district; landscapes close to a given district.
10 Rolf Grütter and Thomas Scharrenbach
While our approach takes a Boolean decision on closeness it still accounts for border-
line cases by using a qualitative formalism. Whether a region is close to another re-
gion or not depends on the size and shape of the administrative units serving as a
frame of reference. When comparing the evaluation of closeness, even on the same
scale of social organization, a given metric distance may in one case be interpreted as
close and in another case as not close. Our claim is that the size and shape of adminis-
trative regions are not arbitrary but reflect how a collective perceives spatial closeness
on increasing scales of social organization. Whether our claim is empirically well
founded or not remains to be shown.
The concept of closeness evolves over time. What is perceived as close by the
members of a social collective (at least in the industrialized countries) has been sub-
ject to change for decades. Similarly, at the institutional level, the concept of close-
ness evolves. In recent years, several cantons in Switzerland, for instance, have re-
vised their administrative structures or established a legal basis for future revisions. A
result of these revisions is a reduction in the number of districts. It is important to note
that the societal change precedes the institutional. If this was not the case, proposals
for structural revisions would not obtain a majority of popular votes. 13 Since our ap-
proach evaluates closeness within the frame of administrative structures and the insti-
tutional change lags behind the societal change, it tends to underestimate closeness. 14
According to our approach, if one region is identified as being close to another, it has
been perceived as such possibly for a long time. While it takes into account evolution
of closeness, it does so at the slow pace of the institutions and not at the fast pace of
the social collective.
7 Conclusion and Outlook
In this paper an implementation of RCC in OWL DL augmented by DL-safe rules is
used in order to represent spatio-thematic knowledge. It is demonstrated how such a
representation can be used for answering queries involving (possibly vague) spatial
concepts. Thereby, the division of land into administrative regions rather than, for in-
stance, a metric system is taken as a frame of reference for evaluating closeness. Ac-
cordingly, closeness is evaluated based on purely qualitative criteria. Since colloquial
descriptions typically involve qualitative concepts, the presented approach is expected
to align better with the way human beings deal with closeness than a quantitative ap-
proach.
So far the presented approach supports the evaluation of closeness of regions w.r.t.
an administrative region. Evaluation of closeness between arbitrary regions would be
desirable. Exploring whether and how the frame of reference can be leveraged to sup-
port evaluation of closeness between arbitrary regions is left to future work. Likewise,
the scalability of the implementation and, possibly, alternative implementation strate-
gies remain to be explored.
13 This might be a peculiarity of the Swiss political system.
14 In a practical application, if the data base is not updated at the time when the revisions take
effect the lag is even longer.
A Qualitative Approach to Vague Spatio-Thematic Query Processing 11
The paper only considers the concept of closeness. There are a number of addi-
tional vague spatial concepts such as “near”, “next to”, “a little distance outside”, “a
long way off”, and “far away from”. It would be interesting to formalize these con-
cepts in a similar way as demonstrated for “close to”. Such a formalization might re-
sult in a theory of vague spatial concepts in RCC which could be implemented, for in-
stance, in OWL DL augmented by DL-safe rules.
Acknowledgments. The authors sincerely thank Jürg Schenker (FOEN), Martin
Hägeli (WSL) and Bettina Waldvogel (WSL) for the fruitful discussions and
leadership that made this research possible. This research has been funded and
conducted in cooperation with the Swiss Federal Office for the Environment (FOEN).
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