=Paper=
{{Paper
|id=None
|storemode=property
|title=Aligning Geospatial Ontologies Logically
|pdfUrl=https://ceur-ws.org/Vol-954/paper1.pdf
|volume=Vol-954
}}
==Aligning Geospatial Ontologies Logically==
https://ceur-ws.org/Vol-954/paper1.pdf
Aligning Geospatial Ontologies Logically
Heshan Du
The University of Nottingham, UK
Abstract. Information sharing and updates have become increasingly
important in the rapidly changing world. However, owing to the dis-
tributed and decentralized nature of information collection and storage,
it is not easy to use information from different sources synergistically.
Ontology plays as an important role in establishing formal descriptions
of a domain of discourse. In geographic information science, the rapid
developments of crowd-sourced geospatial databases challenge and also
bring opportunities to the current geospatial information development
framework. In this paper, a new semi-automatic method is proposed to
align disparate geospatial ontologies, based on description logic and do-
main experts’ knowledge.
1 Introduction
Information sharing and updates have become increasingly important in the rapidly
changing world, with a large amount of disparate information available. However, owing
to the distributed and decentralized nature of information development, it is not easy
to fully capture the information content in different sources. A same expression can
have different meanings in different context, and different expressions may refer to the
same meaning. Such issues are quite common when disparate and related information
sources exchange their data.
Ontology plays an important role in information sharing. Ontology, originated in
the work of Aristotle, is a branch of philosophy which studies the existence of enti-
ties [41]. In computer science, ontology is an explicit formal specification of a shared
conceptualization [14]. Compared to its origin, computer science ontology is not only
about existence, but also about meaning, and making meanings as clear as possible [41].
Ontologies are often employed as important means for establishing explicit formal vo-
cabulary shared among applications.
Description logics are a family of formalisms for representing the knowledge of an
application domain [2]. They firstly define the relevant concepts (terminology), and
then use these concepts to specify properties of objects in the domain [2]. Compared
to traditional first-order logic, description logics provide tools for the manipulations
of compound and complex concepts [7]. Description logics support inference patterns,
including classification of concepts and individuals, which are often used by humans to
structure and understand the world [2]. When dealing with ontology issues, description
logics can be used as the logical underpinning.
The rapid developments in geographic information science emphasize the impor-
tance of geospatial information sharing. Spatial Data Infrastructures (SDI) refers to an
institutional and technical foundation of policies, standards and procedures that en-
able organizations at multiple levels and scales to discover, evaluate, use and maintain
R.K. Rendsvig and S. Katrenko (Eds.): ESSLLI 2012 Student Session Proceedings, CEUR Work-
shop Proceedings vol.: see http://ceur-ws.org/, 2012, pp. 1–12.
2 Heshan Du
geospatial data [28]. Over the last few years, the current top-down approach to SDI has
been challenged by the rapid pace of technological development [17]. There is a need to
address the separation of national and international SDI from crowd-sourced geospa-
tial databases [1]. Relying on volunteers for data collection, crowd-sourced data is less
expensive than authenticated data. In addition, although typically not as complete in
its coverage or as consistent in its geometric or metadata quality as authenticated data,
crowd-sourced data may provide a rich source of complementary information with the
benefit of often more recent and frequent updates than that of authenticated data [18].
It is desirable to use authenticated and crowd-sourced geospatial data synergistically.
Compared to other ontologies, geospatial ontologies have several special properties.
Firstly, many words within geospatial ontologies are often more widely used in daily
life, and there is less consensus about their definitions. For example, the word ‘creek’
can refer to a river in Australia, while cannot in the US. The word ‘field’ has different
meanings (e.g. a branch of knowledge, a piece of land, etc.) for different people in
different contexts. There are no precise formal definitions which can tell ‘river’ and
‘stream’, ‘lake’ and ‘pond’ apart. In addition, geospatial ontologies often do not have
a huge number of classes as biomedical or bioinformatics ontologies do, but may have
many instances, referring to real world objects, whose locations, at least in theory, are
verifiable. With respect to these properties and the underspecification of geospatial
ontologies, no fully automated system can ensure the correctness and completeness
of generated mappings. Therefore, experts are inevitably needed to make decisions,
for example on the correctness of correspondences, based on their domain knowledge,
which is often implicit in the individual ontologies. This research will finally lead to
the minimisation of the human intervention.
This project aims to explore logic-based approaches to aligning disparate and re-
lated geospatial ontologies to obtain harmonized and maximized information content.
Ontologies are disparate if they are created independently. Ontologies are related if
they contain more than one common concept. Aligning means establishing relations
between the vocabularies of two ontologies [9]. The desired output will be a collection
of verified such relations, for query answering over multiple ontologies. If ontologies
cannot be aligned logically, a deficiency report will be produced explaining reasons
and providing suggestions about further actions to take in order to align them. In this
paper, we propose a new semi-automatic method to align disparate geospatial ontolo-
gies, based on description logic and domain experts’ knowledge. It is assumed that
original information within ontologies is believed all the time as premises, whilst gen-
erated information, including disjointness axioms and mappings, is believed by default
as assumptions, which may be retracted later. Differing from other existing methods,
generated disjointness axioms are seen as assumptions, which are retractable during
the overall aligning process. Disparate ontologies are aligned by finding a coherent and
consistent assumption set with respect to them. Based on this main idea, algorithms
are designed to align ontologies at the terminology level and the instance level. With
respect to the special properties of geospatial ontologies, an algorithm is designed for
refining correspondences between geospatial individuals taking their geometries and
semantics into account.
The rest of the paper is organized as follows. The related work is summarized in Sec-
tion 2. Section 3 introduces the geospatial ontologies we use, and explains some results
generated by a state-of-the-art system called S-Match [12]. Our method is discussed in
Section 4. Finally, it provides conclusions in Section 5.
Aligning Geospatial Ontologies Logically 3
2 Related Work
Ontology matching is the task of finding correspondences between entities from different
ontologies [11]. A correspondence represents a semantic relation, such as inclusion or
equivalence. A mapping is defined as a set of these correspondences [25]. Many ontology
matching methods and systems have been proposed and developed in recent years [11]
[35], based on shared upper ontologies, if available, or using other kinds of information,
such as lexical and structural information, user input, external resources and prior
matches [30]. The existing methods can be classified into three broad categories [4].
Syntactic methods rely on a syntactic analysis of the linguistic expressions to generate
mappings. Though these methods are direct and effective, semantic relations between
entities cannot be captured. Pragmatic methods infer the semantic relations between
concepts from associated instance data. Though they work well when the instance
data is representative and overlapping, this kind of methods use a strong form of
induction, thus lack correctness and completeness. Conceptual methods compare the
lexical representation of concepts to compute mappings. Socially negotiated meanings
(e.g. dictionaries) are often used when generating relations, making the problem very
complicated.
Many methods are hybrid, combining different approaches and making use of struc-
tural, lexical, domain or instance-based knowledge. Most of them apply heuristics-based
or machine learning techniques to various characteristics of ontology. However, map-
pings generated by these methods often contain logical contradictions. Some systems,
such as CtxMatch [5] and its extension S-Match [12], and more recently, Automated
Semantic Mapping of Ontologies with Validation (ASMOV) [20], Knowledge Organi-
zation System Implicit Mapping (KOSIMap) [33], logiC-based ONtology inTEgratioN
Tool using MAPpings (ContentMap) [22], LogMap [21], and Combinational Optimiza-
tion for Data Integration (CODI) [29], seem to be exceptions, since they employ logical
reasoning for either mapping generation or verification. Due to limited space, not all
of them are discussed in detail.
CtxMatch and its extension S-Match are early logic-based attempts for ontology
matching. While most of the other methods compute linguistic or structural similar-
ities, CtxMatch shifts the semantic alignment problem to deducing relations between
logical formulae [5, 6]. Relevant lexical (concepts denoted by words), world (relations
between concepts) and structural knowledge is represented as logical formulae, and
logical reasoning is employed to infer different kinds of semantic relations [34]. Word-
Net [27], an external resource, is employed to provide both lexical and world knowledge.
The S-Match system re-implements and extends CtxMatch. Taking two tree-like struc-
tures (e.g. hierarchies) as input, it computes the strongest semantic relations between
every pair of concepts [12]. The semantic matching has two main steps. Firstly, at
element level, relations between labels are calculated, and then, at structure level, it
generates relations between concepts, whose semantics are constructed based on the
semantics of labels [36]. The structure level matching task is converted into proposi-
tional validity problems, and the standard DPLL-based SAT solver [3] is employed to
check the unsatisfiability of propositional formulae [13]. However, S-Match only uses
information in the tree-like structures extracted from ontologies, which is insufficient
to guarantee the overall coherence of ontologies after applying the mapping relations.
More recently, some matching tools have been developed, involving semantic verifica-
tion into the alignment process.
LogMap [21] is a logic-based and scalable ontology matching tool. It addresses the
challenges when dealing with large-scale bio-medical ontologies with tens (even hun-
4 Heshan Du
dreds) of thousands of classes. It employs lexical and structural methods to compute an
initial set of mapping relations as the starting point for further discovery of mapping
relations. The core of LogMap is an iterative process which alternates repair and discov-
ery steps. In the repair step, unsatisfiable classes will be detected using propositional
Horn representation and satisfiability checking, and be repaired using a greedy diag-
nosis algorithm. However, the propositional Horn satisfiability checking is sound but
incomplete, and the underlying semantics is restricted to propositional logic, and thus
cannot guarantee the coherence of the mapping between more expressive ontologies. In
the discovery step, new mapping relations will be generated based on the similarity be-
tween classes which are semantically related to matched classes. ISUB [37] is employed
to compute the similarity scores. Mapping relations which are newly discovered are
active, and only active mapping relations can be eliminated in the repair step, whilst
mapping relations found in earlier iterations are seen as established or valid. In other
words, each mapping relation will be checked once, against the available information
at that time, which, however, cannot guarantee its correctness when new information
is discovered later.
Combinational Optimization for Data Integration (CODI) [29] is a probabilistic
logical alignment system. It is based on Markov logic [10], which combines first-order
logic with undirected probabilistic graphical models. As the main advantages over other
existing matching approaches, Markov logic can combine hard logical axioms and soft
uncertain formulae for potential correspondences. Cardinality constraints, coherence
constraints and stability constraints are formalized using logical axioms and similarity
measures. The matching problem is transformed to a maximum-a-posteriori optimiza-
tion problem subject to these constraints. The GUROBI optimizer [15] is employed
to solve the optimization problems. According to Noessner and Niepert [29], CODI
reduces incoherence during the alignment process for the first time, compared to all
other existing methods repairing alignments afterwards. CODI is based on the ratio-
nale of finding the most likely mapping by maximizing the sum of similarity-weighted
probabilities for potential correspondences. It can be argued that during the optimiza-
tion process, some valid correspondences can be thrown away. In addition, the input
coherence constraints will influence the resulting mapping, however, in practice, many
ontologies are underspecified, within which valid disjointness axioms are not always
available.
In addition, there is some recent work on debugging and repairing ontologies and
mappings in ontology networks [24] [32] [40] [23], which is still at an early stage. How-
ever, all of them use disjointness axioms as premises, rather than assumptions, and
none of the matching systems discussed above have addressed the special properties of
geospatial information fully. Several ontology-driven methods have been developed for
integrating geospatial terminologies. Most of them are based on similarity measures
or a predefined top-level ontology, and logical reasoning is only employed when formal
ontologies commit to the same top-level ontology [8]. However, when ontologies are
developed independently, the common top-level ontology is not always available. Addi-
tionally, there exist some other methods, such as [38] and [19], following the pragmatic
approach to link geospatial schemas or ontologies, inferring the terminology correspon-
dences from the instances correspondences. As discussed above, relying on a very strong
form of induction from particular to the universal, this approach will lead to the lack
of correctness and completeness [4]. Therefore, more research is required to fill in the
gap, exploring logic-based approaches to aligning disparate geospatial ontologies.
Aligning Geospatial Ontologies Logically 5
3 Disparate Geospatial Ontologies
The Ordnance Survey of Great Britain (OSGB) ontology [16] and the OpenStreetMap
(OSM) controlled vocabularies [31] are selected to undertake initial research. OSGB
and OSM are representatives of authenticated and crowd-sourced geospatial informa-
tion sources respectively. OSGB is the national topographic mapping agency of Great
Britain. It has built ontologies for Hydrology and for Buildings and Places [16]. OSM
is a collaborative project aimed to create a free editable map of the world [31]. It
employs the bottom-up approach, relying on volunteers to collect the data. Currently,
OSM does not have a standard ontology, but maintains a collection of commonly used
tags for main map features [31]. An OSM feature ontology is generated automatically
from the existing classification of main features. Both ontologies are written in the
OWL 2 Web Ontology Language [39]. The OSGB Buildings and Places ontology has
692 classes and 1230 logical axioms, and its DL expressivity is ALCHOIQ. There are
663 classes and 677 logical axioms in the OSM ontology, whose DL expressivity is AL.
Both ontologies, containing no disjointness axioms, are coherent.
To understand the ontologies more deeply, S-Match is employed to generate rela-
tions between concepts from them. To distinguish concepts from different ontologies,
let us label each concept with the abbreviated name of the ontology it belongs to,
such as OSGB : School and OSM : School. All the labelled concepts will be treated
as belonging to one super ontology. A relation then can be represented as an axiom
within which all the concepts are labelled, like OSGB : School ⊒ OSM : School.
Some of the mapping axioms generated by S-Match seem reasonable, such as
OSGB : Roof ≡ OSM : Roof , OSGB : Service ⊒ OSM : Service, and OSGB :
Accommodation ⊒ OSM : Accommodation. However, there are also some problem-
atic relations. For example, OSGB : T hing ≡ OSM : N othing is derived, because the
string ‘Thing’ is considered to be close enough by the string-based matcher 1 for stating
that it is equivalent to the string ‘Nothing’. The relation OSGB : P erson ⊒ OSM :
GuestHouse is generated because, ‘GuestHouse’ is split to ‘Guest’ and ‘House’, and
‘House’ is treated as a person’s name, referring to a particular individual of ‘Person’.
The relation OSGB : P erson ⊒ OSM : Dentist seems reasonable, just looking at it
alone. However, OSM : Dentist ⊑ OSM : Healthcare, and OSM : Healthcare ⊑
OSM : Amenity. The OSM : Dentist is used for tagging a place where a dentist
practice or surgery is located, rather than referring to a person who is a dentist. In this
case, S-Match seems too restrictive to deal with the informal use of terms and their
variable meanings in crowd-sourced databases, such as OSM.
4 Method
Ontology alignment has attracted the attention of people working in several research
fields, such as linguistics, philosophy, psychology and computer science. To fully un-
derstand and solve the problem, relying on only one approach is inadequate. Different
approaches may play different important roles and solve different aspects of the prob-
lem effectively. This project focuses on the logic-based approach, and explores what
logic can do and how far logic can go when aligning disparate geospatial ontologies.
1
When matching labels of concepts, S-Match employs string-based, sense-based and
gloss-based matchers [13].
6 Heshan Du
To represent and reason with two ontologies Oi and Oj , where i, j are their names,
as well as the matching relations between them, as if they all belong to one super
ontology Oi ∪ Oj , we label all atomic concepts and roles in each ontology by the name
of the ontology. The ontology Oi is the set {ϕi : ϕ ∈ Oi }, where ϕ denotes a logical
formula. A logical formula ϕi is labelled inductively as follows.
– Ai = i : A, for atomic concept A;
– Ri = i : R, for atomic role R;
– (¬B)i = ¬B i ;
– (B ⊓ C)i = B i ⊓ C i ;
– (B ⊔ C)i = B i ⊔ C i ;
– {o}i = {oi }, for nominal {o}, individual name o;
– (∀R.B)i = ∀Ri .B i ;
– (∃R.B)i = ∃Ri .B i ;
– (≥ n R.C)i =≥ n Ri .C i
– (≤ n R.C)i =≤ n Ri .C i
– (= n R.C)i == n Ri .C i
– (R− )i = (Ri )− ;
– (R+ )i = (Ri )+ ;
– (B ⊑ C)i = B i ⊑ C i
– (S ⊑ T )i = S i ⊑ T i
where B, C denote concept descriptions, S, T denote roles. Similarly, we label all indi-
vidual names in each ontology by the ontology name:
– ai = i : a;
– (C(a))i = C i (ai )
– (R(a, b))i = Ri (ai , bi )
where a, b denote individual names.
A terminology mapping is a set of correspondences between classes from different
ontologies. A terminology correspondence is represented as one of the two basic forms:
Bi ⊑ C j (1)
i j
B ⊒C (2)
where B, C denote class descriptions. The relation (1) states that the class B from
the ontology i is more specific than or equivalent to the class C from the ontology j.
The relation (2) states that the class B from the ontology i is more general than or
equivalent to the class C from the ontology j. The equivalence relation (3) holds if and
only if (1) and (2) both hold.
Bi ≡ C j (3)
It states that the concept B from the ontology i and the concept C from the ontology
j are equivalent.
A disjointness axiom states that two or more classes are pairwise disjoint, having
no common element. For example, P erson and P lace are disjoint. The disjointness
axioms in ontologies play an important role in debugging ontology mappings. However,
within the original geospatial ontologies, disjointness axioms are not always available or
sufficient. Adding disjointness axioms manually, especially for large ontologies, is time-
consuming and error-prone. Many existing systems employ more automatic approaches,
either assuming the disjointness of siblings (e.g. KOSIMap [33]), or employing machine
Aligning Geospatial Ontologies Logically 7
learning techniques to detect disjointness (e.g. [26]). After the disjointness axioms are
generated by whatever means, all existing ontology matching or debugging methods,
to the best of our knowledge, use them as premises, believing their validity through
the overall following process, though the input disjointness axioms can be insufficient
or too restrictive. Differing from these methods, we use generated disjointness axioms
as assumptions, rather than premises, and ensure the assumption set is coherent. A
disjointness assumption is represented as:
B ⊑ ¬C (4)
where B, C denote class descriptions either from ontology i or ontology j. We follow
the terminology from [26], and adapt them to this context.
Definition 1 (Coherence). An ontology O is coherent if there is no class C such
that O |= C ⊑ ⊥. Otherwise, it is incoherent.
Definition 2 (Coherence of an Assumption Set). An assumption set As is in-
coherent with respect to an ontology O, if O ∪ As is incoherent, but O is coherent.
Otherwise, it is coherent with respect to an ontology O.
Definition 3 (Minimal Incoherent Assumption Set). Given a set of assumptions
As , a set C ⊆ As is a minimal incoherent assumption set (MIA) iff C is incoherent
′
and each C ⊂ C is coherent.
A minimal incoherent assumption set can be fixed by removing any axiom from
it. When a MIA contains more than one element, one needs to decide which axiom to
remove. Most of the existing methods remove the one either with the lowest confidence
value or which is the least relevant. However, it can be argued that there is no consensus
with respect to the measure of the degree of confidence or relevance. In addition, the
confidence values or the relevance degrees might be unavailable, difficult to compute
or compare. In such cases, it seems sensible to allow domain experts to make such
decisions.
When aligning ontologies using a terminology mapping, Definition 2 is extended
from one ontology O to two ontologies O1 and O2 , given that the union of two on-
tologies O1 ∪ O2 is an ontology. Based on these definitions, Algorithm 1 is designed as
follows 2 . An assumption in a minimal incoherent assumption set can be a disjointness
axiom or a terminology correspondence axiom. The set of minimal incoherent assump-
tion sets will be visualized clearly (Line 7). Domain experts are consulted to decide
which assumption(s) to retract (Line 8). A repair action can be retracting or adding
an assumption axiom. Users are allowed to take several repair actions at one time.
ALGORITHM 1: Terminology Level Alignment
Input: O1 , O2 : coherent ontologies
Ds : a disjointness assumption set
Mst : a terminology mapping between O1 and O2
Output: As : a coherent assumption set with respect to O1 ∪ O2
1. O := O1 ∪ O2
2. As := Ds ∪ Mst
3. assert O is coherent
2
In an algorithm, lines marked with ∗ may require manual intervention.
8 Heshan Du
4. O := O ∪ As
5. while O is incoherent do
6. Smia := M IA(O)
7. visualization(Smia )
8*. repair(O, Smia )
9. update(As )
10. end while
11. return As
Following the algorithm above, even if the problematic relations generated by S-Match
are introduced, they can be retracted, given the disjointness assumptions such as
OSGB : P erson ⊑ ¬OSM : Accommodation and OSGB : P erson ⊑ ¬OSM :
Amenity.
An instance level mapping is a set of individual correspondences. An individual
correspondence is represented in one of the following forms:
(ai , bj ) ∈ sameAs (5)
i j
(a , b ) ∈ partOf (6)
where a, b denote individual names. The relation (4) states that the individual name a
from the ontology i and the individual name b from the ontology j refer to the same
object. The relation (5) states that the individual name a from the ontology i refers
to an object which is a part of the object the individual name b from the ontology j
refers to.
When working with geospatial instances, Algorithm 2 is designed to refine the ini-
tial instance level mapping using geometry, lexical and cardinality properties.
ALGORITHM 2: Refining GeoInstance Mapping
Input: Msa : an initial instance level mapping for geospatial individuals
Output: Msa : the refined input Msa
1. for each individual correspondence m in Msa do
2. a1 := m.individual1 , a2 := m.individual2
3. if a1 .geometry, a2 .geometry are not matched then
4. remove(Msa , m)
5. else if a1 .lexicons, a2 .lexicons are not matched then
6. remove(Msa , m)
7. end if
8. end for
9. for each individual b appearing more than once in Msa do
10. Msb := allCorrespondencesInvolving(b)
11. repair(Msa , Msb )
12. end for
13. return Msa
Given a set of correspondences linking geospatial individuals from different ontologies,
Algorithm 2 applies three main constraints, these are, geometry, lexical and cardinal-
ity. Firstly, a correspondence is invalid if the geometries of the linked individuals are
not matched (Line 3-4). For example, when the geometries are both polygons, if they
are spatially disjoint, they cannot be matched. Secondly, a correspondence is invalid if
the lexicons, i.e. meaningful labels indicating identity, cannot be matched (Line 5-6).
Aligning Geospatial Ontologies Logically 9
The lexical matching required should be robust enough to tolerate partial differences
in labelling. For example, a full name and its abbreviation should be matched. Thirdly,
if an individual is involved in several different ‘sameAs’ correspondences, then these
correspondences need to be repaired (Line 9-12), for example, by changing the relation
from ‘sameAs’ to ‘partOf’. The three constraints complement each other to cope with
the following possibilities. Different geospatial individuals may share the same label or
the same location in an ontology. In addition, the same geospatial individual may be
represented as a whole in one ontology, whilst as several parts of it in the other.
The algorithm for aligning instances is generated by extending the assumption set
to include instance correspondences (output of Algorithm 2 ) and changing coherence
checking to consistency checking in Algorithm 1. Similarly, domain experts are con-
sulted to make decisions to repair inconsistencies. Consider the example below.
Example 1. OSGB : 1000002308476718 refers to a OSGB : HealthCentre labelled as
‘SNEINTON HEALTH CENTRE’. OSM : 62134030 refers to a OSM : Clinic labelled
also as ‘SNEINTON HEALTH CENTRE’. Their geometries are very similar. However,
the existence of the following assumptions can lead to inconsistency.
(OSGB : 1000002308476718, OSM : 62134030) ∈ sameAs (7)
OSGB : Clinic ≡ OSM : Clinic (8)
OSGB : Clinic ⊑ ¬OSGB : HealthCentre (9)
Domain experts are consulted to decide which assumption(s) to retract. To keep the
individual correspondence (7), it is reasonable to retract (9). This differs from all other
methods, which use (9) as a premise, and therefore will either remove (7) or (8), though
both are reasonable.
This method has been implemented as a system. Its performance is being evaluated,
compared to other existing systems, such as S-Match, CODI and LogMap.
5 Conclusion
To facilitate the geospatial information sharing and updates, it is important to har-
monize disparate and related geospatial ontologies. This paper discusses problems in-
volved, and presents a new logic-based method to deal with them. Future work includes
employing a truth maintenance system to track logical dependencies and qualitative
spatial reasoning to check topological consistency of geospatial data.
References
1. Anand, S., Morley, J., Jiang, W., Du, H., Jackson, M.J., Hart, G.: When worlds
collide: Combining Ordnance Survey and OpenStreetMap data. In: Association for
Geographic Information (AGI) GeoCommunity ’10 Conference (2010)
2. Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F.
(eds.): The Description Logic Handbook. Cambridge University Press (2007)
3. Berre, D.L., Parrain, A.: The Sat4j Library, release 2.2. Journal on Satisfiability,
Boolean Modeling and Computation 7(2-3), 59–64 (2010)
4. Bouquet, P.: Contexts and ontologies in schema matching. In: Context and
Ontology Representation and Reasoning (2007), http://ceur-ws.org/Vol-298/
paper2.pdf
10 Heshan Du
5. Bouquet, P., Serafini, L., Zanobini, S.: Semantic Coordination: A New Approach
and an Application. In: International Semantic Web Conference. pp. 130–145
(2003), http://disi.unitn.it/~bouquet/papers/ISWC2003-CtxMatch.pdf
6. Bouquet, P., Serafini, L., Zanobini, S.: Peer-to-Peer Semantic Coordination. Web
Semantics: Science, Services and Agents on the World Wide Web 2(1) (2004),
http://www.websemanticsjournal.org/index.php/ps/article/view/54
7. Brachman, R.J., Levesque, H.J.: Knowledge Representation and Reasoning. The
Morgan Kaufmann Series in Artificial Intelligence, Morgan Kaufmann (2004),
http://books.google.co.uk/books?id=OuPtLaA5QjoC
8. Buccella, A., Cechich, A., Fillottrani, P.: Ontology-Driven Geographic Informa-
tion Integration: A Survey of Current Approaches. Computers and Geosciences
35(4), 710 – 723 (2009), http://www.sciencedirect.com/science/article/pii/
S0098300408002021
9. Choi, N., Song, I.Y., Han, H.: A survey on ontology mapping. SIGMOD Record
35, 34–41 (September 2006), http://doi.acm.org/10.1145/1168092.1168097
10. Domingos, P., Lowd, D., Kok, S., Poon, H., Richardson, M., Singla, P.: Just Add
Weights: Markov Logic for the Semantic Web. In: Uncertainty Reasoning for the
Semantic Web I, ISWC International Workshops, URSW 2005-2007, Revised Se-
lected and Invited Papers. pp. 1–25 (2008)
11. Euzenat, J., Shvaiko, P.: Ontology Matching. Springer-Verlag, Heidelberg (DE)
(2007), http://www.springerlink.com/content/978-3-540-49611-3#section=
273178&page=1
12. Giunchiglia, F., Shvaiko, P., Yatskevich, M.: S-Match: an Algorithm and an Imple-
mentation of Semantic Matching. In: European Semantic Web Conference (ESWS).
pp. 61–75 (2004)
13. Giunchiglia, F., Yatskevich, M., Shvaiko, P.: Semantic Matching: Algorithms and
Implementation. Journal on Data Semantics IX 9, 1–38 (2007)
14. Gruber, T.R.: A translation approach to portable ontology specifications. Knowl-
edge Acquisition 5, 199–220 (1993), http://citeseer.ist.psu.edu/viewdoc/
summary?doi=10.1.1.101.7493
15. Gurobi Optimization Inc.: Gurobi Optimizer Reference Manual.
http://www.gurobi.com (2012), http://www.gurobi.com
16. Hart, G., Dolbear, C., Kovacs, K., Guy, A.: Ordnance Survey Ontologies.
http://www.ordnancesurvey.co.uk/oswebsite/ontology (2008)
17. Jackson, M.J.: The Impact of Open Data, Open Source Software and Open Stan-
dards on the Evolution of National SDI’s. In: Third Open Source GIS Conference
(21-22 June 2011), http://uiwapmds01.nottingham.ac.uk/qcsplace/ondemand/
events11/a5a1e446858f4867b77716111a/player.HTM
18. Jackson, M.J., Rahemtulla, H., Morley, J.: The Synergistic Use of Authenticated
and Crowd-Sourced Data for Emergency Response. In: 2nd International Work-
shop on Validation of Geo-Information Products for Crisis Management (VAL-
gEO). Ispra, Italy (11-13 October 2010), http://globesec.jrc.ec.europa.eu/
workshops/valgeo-2010/proceedings
19. Jain, P., Hitzler, P., Sheth, A.P., Verma, K., Yeh, P.Z.: Ontology Alignment for
Linked Open Data. In: International Semantic Web Conference (1). pp. 402–417
(2010)
20. Jean-Mary, Y.R., Shironoshita, E.P., Kabuka, M.R.: ASMOV: results for OAEI
2010. In: the 5th International Workshop on Ontology Matching (OM-2010) (2010)
21. Jiménez-Ruiz, E., Grau, B.C.: LogMap: Logic-Based and Scalable Ontology Match-
ing. In: International Semantic Web Conference (1). pp. 273–288 (2011)
Aligning Geospatial Ontologies Logically 11
22. Jiménez-Ruiz, E., Grau, B.C., Horrocks, I., Llavori, R.B.: Ontology Integration
Using Mappings: Towards Getting the Right Logical Consequences. In: The 6th
European Semantic Web Conference (ESWC). pp. 173–187 (2009)
23. Lambrix, P., Liu, Q.: Debugging is-a Structure in Networked Taxonomies.
In: the 4th International Workshop on Semantic Web Applications and
Tools for the Life Sciences. pp. 58–65. SWAT4LS ’11, ACM, New York,
NY, USA (2011), http://doi.acm.org/10.1145/2166896.2166914,http://www.
informatik.uni-trier.de/~ley/db/indices/a-tree/l/Lambrix:Patrick.html
24. Meilicke, C., Stuckenschmidt, H.: An Efficient Method for Computing Alignment
Diagnoses. In: Third International Conference on Web Reasoning and Rule Sys-
tems. pp. 182–196 (2009)
25. Meilicke, C., Stuckenschmidt, H., Tamilin, A.: Reasoning Support for Mapping
Revision. Journal of Logic and Computation 19(5), 807–829 (2008), http://disi.
unitn.it/~p2p/RelatedWork/Matching/Meilicke08reasoning.pdf
26. Meilicke, C., Völker, J., Stuckenschmidt, H.: Learning Disjointness for Debugging
Mappings between Lightweight Ontologies. In: Proceedings of the 16th interna-
tional conference on Knowledge Engineering: Practice and Patterns. pp. 93–108.
EKAW ’08, Springer-Verlag, Berlin, Heidelberg (2008), http://dx.doi.org/10.
1007/978-3-540-87696-0_11
27. Miller, G.A.: WordNet: a lexical database for English. Communications of the ACM
38, 39–41 (November 1995), http://dl.acm.org/citation.cfm?doid=219717.
219748
28. Nebert, D.D.: Developing Spatial Data Infrastructures: The SDI Cookbook. Global
Spatial Data Infrastructure Association (GSDI) (2004)
29. Niepert, M., Meilicke, C., Stuckenschmidt, H.: A Probabilistic-Logical Framework
for Ontology Matching. In: American Association for Artificial Intelligence (2010)
30. Noy, N., Stuckenschmidt, H.: Ontology alignment: An annotated bibliography.
In: Semantic Interoperability and Integration. Schloss Dagstuhl (2005), http:
//citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.93.2574
31. OpenStreetMap: The Free Wiki World Map. http://www.openstreetmap.org
(2012)
32. Qi, G., Ji, Q., Haase, P.: A Conflict-Based Operator for Mapping Revision:
Theory and Implementation. In: Proceedings of the 8th International Seman-
tic Web Conference. pp. 521–536. ISWC ’09, Springer-Verlag, Berlin, Heidel-
berg (2009), http://dx.doi.org/10.1007/978-3-642-04930-9_33,http://www.
springerlink.com/content/0v25w11500867103/fulltext.pdf
33. Reul, Q., Pan, J.Z.: KOSIMap: Use of Description Logic Reasoning to Align Het-
erogeneous Ontologies. In: the 23rd International Workshop on Description Logics
(DL 2010) (2010)
34. Serafini, L., Zanobini, S., Sceffer, S., Bouquet, P.: Matching Hierarchical Classi-
fications with Attributes. In: European Semantic Web Conference (ESWC). pp.
4–18 (2006)
35. Shvaiko, P., Euzenat, J.: Ontology Matching: State of the Art and Future Chal-
lenges. IEEE Transactions on Knowledge and Data Engineering (2012)
36. Shvaiko, P., Giunchiglia, F., Yatskevich, M.: Semantic Matching with S-Match,
vol. Part 2, pp. 183–202 (2010)
37. Stoilos, G., Stamou, G.B., Kollias, S.D.: A String Metric for Ontology Alignment.
In: International Semantic Web Conference. pp. 624–637 (2005)
38. Volz, S., Walter, V.: Linking Different Geospatial Databases by Explicit Re-
lations. In: Proceedings of the XX th International Society for Photogramme-
12 Heshan Du
try and Remote Sensing (ISPRS) Congress, Comm. IV. pp. 152–157 (2004),
http://www.isprs.org/proceedings/XXXV/congress/comm4/papers/332.pdf
39. W3C: OWL 2 Web Ontology Language. http://www.w3.org/TR/owl2-overview
(2009)
40. Wang, P., Xu, B.: Debugging Ontology Mappings: A Static Approach. Comput-
ers and Artificial Intelligence 27(1), 21–36 (2008), http://disi.unitn.it/~p2p/
RelatedWork/Matching/MappingDebug_CAI.pdf
41. Welty, C.A.: Ontology research. AI Magazine 24(3), 11–12 (2003), http://www.
aaai.org/ojs/index.php/aimagazine/article/view/1714