Ontologies Alignment using Composition and Aggregation of Similarities
Sami Zghal
CRIL CNRS FRE 2499, Artois University, IUT of Lens, Rue de l’Universit
e - S.P. 16, 62307 Lens Cedex, France
Marouen Kachroudi, Sadok Ben Yahia
Computer Science Department, Faculty of Sciences of Tunis, Campus Universitaire, 1060 Tunis, Tunisia
Engelbert Mephu Nguifo
LIMOS CNRS UMR 6158, Complexe scientifique des C
ezeaux, BP 125, 63173 Aubiere Cedex, France
Ontology alignment, Similarity measures, Composition of similarities.
Ontologies are the kernel of semantic Web. They allow the explicitation of the semantic purpose for structur-
ing different fields of interest. In order to harmonize them and to guarantee the interoperability between these
resources, the topic of alignment of ontologies has emerged as an important process to reduce their heterogene-
ity and improve their exploitation. The paper introduces a new method of alignment of OWL-DL ontologies,
using a combination and aggregation of similarity measures. Both ontologies are transformed into a graph
which describes their information. The proposed method operates in two steps: local (linguistic similarity
composition and neighborhood similarity) step and the aggregation one.
An ontology is defined as ”an explicit specification of
a conceptualization” (Gruber, 1993). Indeed, an on-
tology is a set of concepts, relations and possibly ax-
ioms representing a knowledge field. Consequently,
the diversity of the reality is at the same time a source
of richness of and heterogeneity. This heterogeneity
unfortunately reduces the interoperability levels (Eu-
zenat, 2001). Thus, the process of ontologies align-
ment aims to lower the conflict between them.
The ontology alignment issues grasped the interest
of the community as witnesses the wealthy number
of approaches, e.g., FALCON-AO (Hu et al., 2007),
ONTODNA (Kiu and Lee, 2007) and RIMOM (Li
et al., 2007), to cite but a few. The FALCON-AO sys-
tem, contains five modules integrating a graphic in-
terface. The process begins with a parsing stage
to extract a graph model representing the character-
istics of the ontology. The following stage consists
in choosing the strategy of alignment, through a li-
brary of aligners. Their role is the exploitation of the
properties of the ontology. The resultant alignment
is presented under the format RDF / XML, having
considered the linguistic aspect as well as the struc-
tural aspect of an ontology. The second system, ON-
TODNA uses techniques of data mining to tackle the
issue of semantic heterogeneity. This system operates
in four stages. The algorithm takes in entry two on-
tologies to be aligned. Then, it launches a process of
linguistic and lexical treatments. A stage of Cluster-
ing is performed according to the best obtained corre-
spondences. A final linguistic treatment is applied in
the sake of restoring the semantic relations between
the various ontological entities. The RIMOM sys-
tem is an alignement tool which contains six strate-
gies. Every strategy is defined according to the type
of information that an ontology can contain. Indeed,
the system offers seven different alignment methods,
which are afterward organized through a linear inter-
polation function. The system also exploits the struc-
tural aspect of an ontology, by the propagation of
similarity through its hierarchy. The final alignment
is obtained after a sequence of refinements, through
heuristic rules to keep the best possible alignment.
The new alignment method, OACAS (Ontolo-
gies Alignment using Composition and Aggregation
of Similarities), introduces a new alignment algorithm
of OWL-DL (Ontology Web Language Description
Logic) ontologies. The main thrust of this method is
Zghal S., Kachroudi M., Yahia S. and Nguifo E. (2009).
OACAS - Ontologies Alignment using Composition and Aggregation of Similarities.
In Proceedings of the International Conference on Knowledge Engineering and Ontology Development, pages 233-238
DOI: 10.5220/0002301802330238
the application of the most suitable similarity measure
depending of the category of the node in the ontol-
ogy. In addition, the OACAS method explores a wider
neighborhood than do the pioneering methods of the
literature. Carried out experiments showed that OA-
CAS presents very encouraging values of the com-
monly used evaluation metrics for the assessment of
ontologies alignement, especially for the real ontolo-
The paper is organized as follows. Section 2 intro-
duces the new ontology alignment method and thor-
oughly presents the underlying fundamentals. Sec-
tion 3 presents an evaluation of the new method that
includes a description of the evaluation metrics used,
experimental tests and a comparative study. The con-
clusion and future issues are presented in section 4.
The proposed new ontology alignment method, OA-
CAS, aligns two ontologies. Both ontologies are de-
scribed in the OWL-DL language. Both ontologies
are transformed in two graphs O-GRAPHS. The ob-
tained graphs are parsed in order to produce the align-
ment process out. The process of building the graphs
allows to map the considered ontologies to be aligned
in two graphs, called O-GRAPHS. An O-GRAPH
describes all the information included in an OWL-
DL ontology: classes, relations and instances. Both
classes and instances represent the nodes of the graph.
The relations between these different entities are in-
duced by the links of an O-GRAPH. Each entity of
the ontology is formalized through an associated no-
tion to the RDF formalism. OWL-DL ontology enti-
ties are described thanks to OWL language construc-
tors. These constructors are represented through RDF
triplets: <subject, predicate, object>. In an OWL-
DL ontology, a class or a relation description is an
RDF triplet. The subject corresponds to the class or
to the relation. Predicates are OWL primitives, which
are OWL and RDF properties. Each property, used in
a triplet, sketches a knowledge of the described entity.
The arrangement of tho se knowledge constitues the
entity definition. The representation of an OWL-DL
ontology through an O-GRAPH permits to load the
ontology in main memory only once. An O-GRAPH,
stored in main memory, statistically reduces the time
required to access initial OWL-DL ontology disk res-
ident file.
2.1 The Alignment Method
The introduced OACAS method lays on a compo-
sition of similarity computation based model. The
method starts by exploring the O-GRAPH structure. It
determines the nodes of both ontologies to be aligned
and gets out the similarity measures. For each node of
the same category (or cluster), the alignment model
computes similarity mesures between descriptors by
using appropriate functions. An aggregation func-
tion combines the similarity measures and the node’s
structures of the nodes to be aligned. Thus, this
function considers all the descriptive information of
this couple (name, comment and label) as well as its
neighborhood structure. The algorithm implementing
the OACAS method takes as input two OWL-DL on-
tologies to be aligned and produces an RDF file con-
taining the aligned nodes as well as their similarity
measures. The OACAS method operates into two suc-
cessive steps. The first one computes the local simi-
larity, whereas the second one computes the aggrega-
tion similarity.
2.1.1 The Local Similarity Computation
The local similarity computation is performed into
two successive steps. The first step computes many
linguistic similarity measures and aggregates them for
each couple of nodes belonging to the same category
(or type). The second step computes neighborhood
similarities by exploiting the structures of the nodes
to be aligned.
The Linguistic Similarity Composition. The lin-
guistic similarity computation is carried out once for
each node of the same cluster (node of the same type)
in the beginning of the alignment process. The lin-
guistic similarity measures of couples of entities of
the same type (class, property and instance) are com-
puted using the LINGUISTIC function. The names of
properties and instances are used to compute linguis-
tic similarities. For class category, the computation of
the linguistic similarity considers both the comments
and labels. The computation of linguistic similarities
uses different similarity measures. Those measures
are adapted to different descriptors (names, comments
and labels) of the entities to be aligned. Different sim-
ilarity values obtained, for the descriptors, are com-
posed. This composition assigns weights to each sim-
ilarity measure of descriptors. The sum of the as-
signed weights to different similarity values is equal
to 1. This unit sum guarantees that the composition of
the similarity produces a normalized value (between
0 and 1). The LEVENSHTEIN similarity measure (Eu-
zenat and Shvaiko, 2007) is used to compute the simi-
KEOD 2009 - International Conference on Knowledge Engineering and Ontology Development
larity value between the names of ontological entities.
The Q-GRAM similarity measure (Ukkonen, 1992)
computes the similarity value between the comments
of the ontological entities. The JARO-WINKLER simi-
larity measure (Euzenat and Shvaiko, 2007) computes
the similarity value between the labels of ontological
entities. The LINGUISTIC function computes com-
posed linguistic similarity of couples of nodes of both
ontologies to be aligned, i.e., O
and O
. It takes as in-
put (i) both ontologies sketched by two corresponding
O-GRAPHS; (ii) linguistics similarity functions (i.e.,
Funct); and (iii) weighted attributed to the descriptors
nodes (i.e., Π
). As a result, it produces a composed
linguistic similarity vector, V
, for each couple of
nodes. The similarity function Funct considers two
nodes, N
and N
, and returns the linguistic similarity
value of the descriptor, Sim
GRAM or JARO-WINKLER implements the similarity
function, Funct, depending of the type of the nodes.
Composed linguistic similarity, Sim
, is computed
depending of the descriptors of nodes to be aligned
and associate weights to each descriptor, Π
. Both
nodes (N
and N
) and the associated composed lin-
guistic similarity (Sim
) are added to the composed
linguistic similarity vector (V
). The composed lin-
guistic similarity of different couples of entities will
be used to compute the neighborhood similarity as
sketched in the following.
The Neighborhood Similarity Computation. The
NEIGHBORHOOD function considers both ontologies
to be aligned (i.e., O
and O
), the composed similar-
ity vector (V
), the weights assigned to each cate-
gory (Π
) and the weights associated to the neighbor
level (Π
). Therefore, it produces the neighborhood
similarity vector, V
. The neighborhood similarity
computation needs composed linguistic similarity of
the couple of nodes to be aligned and the nodes struc-
tures. Neighborhood nodes are organized by category,
node having the same type. The neighborhood simi-
larity computation propagates similarity into two suc-
cessive neighborhood levels. The first level (level 1)
includes direct neighbors of the nodes to be aligned
whereas second one (level 2) contains indirect neigh-
bors. Direct neighbors of the first level represent
nodes having direct relationship with the node under
consideration. Neighbors of the second level repre-
sent nodes having relationship with the nodes of the
first one. The neighbors entities of the first level are
clustered into three categories (classes, instances or
properties). Each category (or cluster) includes onto-
logical entities having the same type. After the step of
clustering, the neighborhood similarity is computed
between those categories. The neighborhood nodes
of the level 2 are treated in the same manner as the
neighbors of the first one. The neighborhood similar-
ity by group MSim takes nodes from vectors V N
regrouped by category (where VN
and V N
note a vector nodes of O
and O
). The process com-
putation uses the Match-Based similarity (Valtchev,
1999) as follows:
MSim(E, E
) =
(i, i
Max(|E|, |E
. (1)
Both sets E and E
represent nodes of the same
cluster belonging respectively to vectors V N
. The neighborhood similarity, Sim
, is com-
puted using Equation 2:
MSim(E, E
))), (2)
where i stands for the level (i.e., 1 or 2). The
neighborhood similarity, Sim
is a normalized value,
since the sum of weights assigned to different neigh-
bors is equal to 1, (Π
V 1
+ Π
V 2
= 1). Direct neigh-
bors (level 1) have more important relationships than
those of indirect one (level 2). Thus, nodes of level 1
have an important impact on the produced alignment.
For this reason, the weight assigned to the first level,
V 1
= 0.8, is more important than the one assigned to
the second level, Π
V 2
= 0.2. In addition, the sum of
weights assigned to the category of nodes is equal to 1
) = 1). Those weights are uniformly assigned
between the different categories. The neighborhood
similarity is computed thanks to an iterative process,
level by level. The obtained values of the composed
linguistic similarity, i.e. V
, and neighbors similar-
ity, i.e. V
, are combined in order to compute aggre-
gation similarity.
2.1.2 The Aggregated Similarity Computation
The aggregation similarity is a combined similarity
between the local similarities (the composed linguis-
tic similarity and the neighborhood similarity). Func-
tion AGGREGATION needs to have in input both on-
tologies to be aligned, O
and O
, the two similar-
ity vectors, V
and V
, and the weights attributed
to the both kind of similarities, Π
and Π
. It pro-
duces the aggregated similarity vector, V
. For each
couple of entities, N
and N
, of the same category
of the both ontologies to be aligned, O
and O
, the
aggregated similarity is computed as follows:
, e
) = Π
, e
) +Π
, e
). (3)
Note that the sum of the weights, attributed to each
kind of similarity, is equal to 1 in order to have a nor-
malized aggregation (between 0 and 1). In addition,
OACAS - Ontologies Alignment using Composition and Aggregation of Similarities
the sum of weights is equal to 1 (Π
+ Π
= 1). In
the next section, we focus on the experimental evalu-
ation of OACAS.
The carried out experimental evaluation uses the tests
provided and distributed by the OAEI
in order to pro-
mote activities within the ontology alignment com-
munity. The goal of these benchmark bases is to iden-
tify the strength and weakness areas in each alignment
algorithm. The battery of tests is based on one par-
ticular ontology. This ontology is dedicated to the
very narrow domain of bibliography. From this on-
tology (i.e., Test 101) derives a number of alternative
ontologies of the same domain for which alignments
are provided. The benchmark test library is composed
of a set of 51 pairs of ontologies. Each ontology is
to be aligned with the reference ontology (i.e., Test
101). The Test 101 contains 33 named classes, 24 ob-
ject properties, 40 data properties, 56 named individ-
uals and 20 anonymous individuals. The alignment
method should supply for each test an alignment. The
obtained alignment is compared to the available ref-
erence one. Both, obtained and reference alignments,
are used to compute evaluation metrics in order to as-
sess the quality of the aligner algorithm.
3.1 Evaluation Metrics
Precision and recall are respectively the most used
metrics to evaluate the quality of an alignment method
(Euzenat et al., 2006). The OAEI uses these measures
to assess the quality of the obtained alignment. The
main goal of these measures is to assess the autom-
atization of the comparing process of the alignment
methods. The first step, within the process of evalua-
tion of the quality of alignment, consists in resolving
the problem manually. The manually obtained result
is considered as the reference alignment. The com-
parison between the reference alignment and that ob-
tained by the alignment method produces three sets:
f ound
, N
and N
. The N
f ound
set repre-
sents the set of the couples aligned by an alignment
method. The N
set gathers the couples aligned
in the reference alignment. The N
set is the in-
tersection of both N
f ound
and N
sets. It repre-
sents the couples that concurrently belong to the ref-
erence alignment and to the output of the alignment
method. The precision is the ratio of the number of
Ontology Alignment Evaluation Initiative - OAEI-2007
Campaign, http://oaei.ontologymatching.org/2007/
pertinent found couples, i.e., ”N
”, by the number
of total couples, i.e., ”N
f ound
”. Thus, it represents the
part of the true correspondences between those found.
The precision function is defined as:
precision =
| N
| N
f ound
. (4)
The recall is the ratio of found pertinent couples,
”, by the total number of pertinent couples,
”. It specifies, the part of the true found cor-
respondences. The recall function is defined as:
recall =
| N
| N
. (5)
Precision and recall metrics are used to perform
the evaluation of OACAS method.
3.2 Experimentation and Results
The main objective of the experimentations with the
OACAS method is to find the best combination of lin-
guistic measures. In the experimental study, various
measures have been used. The goal is to experiment
different measures in order to find the more appropri-
ate measure associated to the node descriptors. In or-
der to achieve the objective, 27 arrangements of tests
have been experimented. Each test uses a particular
combination of similarity measures to compute lin-
guistic similarities between the descriptors of entities
to be aligned. During the process of the carried out
tests, different weights were assigned to the descrip-
tors (names, comments and labels). The nodes to be
aligned can have different descriptors. Depending on
the descriptors of the nodes, different weights are at-
tributed. In the case where the nodes are described by
three descriptors, the weights are 0.8, 0.1 and 0.1 as-
sociated respectively to the names, comments and la-
bels. Whereas the nodes contain only names and com-
ments descriptors, the weights are respectively 0.85
and 0.15. The weights 0.85 and 0.15 are assigned to
the names and labels where those the entities are de-
scribed by them. The experimental results obtained
are developed in the next subsection.
The combination using three different linguis-
tic similarities (LEVENSHTEIN, Q-GRAM and JARO-
WINKLER) is the best one. In fact, the LEVENSHTEIN
measure is more appropriate for computing linguistic
similarity between the names of entities to be aligned.
Whereas, the Q-GRAM measure is more indicated
to compute linguistic similarity between comments
of ontological entities. JARO-WINKLER measure is
more appropriated for computing linguistic similarity
between the labels of entities to be aligned. Indeed,
names and labels of ontological entities are short
KEOD 2009 - International Conference on Knowledge Engineering and Ontology Development
strings. For this type of strings, LEVENSHTEIN and
JARO-WINKLER measures are more adapted to com-
pute the linguistic similarity. Comments are strings
composed with many words. For this type strings, the
Q-GRAM measure gives the best linguistic similarity
values. Columns 2-3 of Table 1 of the Appendix show
precision and recall values for the best combination.
The next subsection will compare OACAS vs other
related methods.
3.3 Comparative Study
In order to evaluate the results obtained by OACAS,
Tables 1 and 2 of the Appendix show the obtained pre-
cision and recall values. They also represent values
respectively obtained by FALCON-AO, ONTODNA
and RIMOM methods. With respect to Tables 1 and
2, the OACAS method produces better results than
the three others methods, in particular in the 30x fam-
ily tests. Theses tests represent real ontologies. The
OACAS method gives worse results in the 26x family
tests. In this family of tests, ontological entities do not
have properties (names and comments). Interestingly
enough, these ontological components are the main
factors in computing the alignment score using the
OACAS method. The experimental results also high-
light that the performances of the OACAS method are
highly linked to the different characteristics of onto-
logical components (names, comments and labels).
In the case where the descriptors of the entities are
dropped, the quality (precision and recall values) of
the alignment is degraded. For example, in the tests
257 and 260 the ontological entities are not described
neither by names nor by comments. In addition, rela-
tions and properties are absent. Whenever the name
and comments descriptors are not present, the values
of linguistic similarity measures are lowered and con-
sequently the value of the composed linguistic simi-
larity will follow the same tendency. Moreover, when
the considered ontologies to be aligned do not contain
relations nor properties, the values of neighborhood
similarities decrease. The local (linguistic similarity
composition and neighborhood similarity) similarity
computation in the OACAS method reduces the ag-
gregation similarity value. For this reason, precision
and recall values are degraded.
In this paper, we introduced a new alignment method
of OWL-DL ontologies. The new proposed method
OACAS, allows to exploit at most the informative
present within in an ontology described in OWL-DL.
The process of alignement in the OACAS method,
contains two phases: a local phase and a phase of
aggregation. The local phase allows to calculate the
linguistic similarity consisted as well as the neighbor-
hood similarity. This two similarities are combined
during the second phase to determine the aggregation
similarity. The results obtained by the method OA-
CAS are very encouraging, compared with the results
obtained by the methods FALCON-AO, ONTODNA
and RiMOM. The method OACAS shows more suc-
cessful results compared to the other methods in par-
ticular on the real ontologies (the family of the tests
30x). In order to improve the OACAS method, some
improvements can be brought. Indeed, the method
OACAS have to deal with ontologies of huge sizes.
The integration of the API WORDNET (Miller, 1995)
is necessary, to improve the values of the measures of
the linguistic similarity.
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tology Matching (OM-2007), pages 170–178, Busan,
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OACAS - Ontologies Alignment using Composition and Aggregation of Similarities
Valtchev, P. (1999). Construction automatique de tax-
onomies pour l’aide la repr
esentation de connais-
sance par objets. Th
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Grenoble 1, France.
Table 1: Precision and recall values for OACAS, FALCON-
AO, ONTODNA and RIMOM methods (Part 1).
Valtchev, P. (1999). Construction automatique de tax-
onomies pour l’aide la repr
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Grenoble 1, France.
Table 1: Precision and recall values for OACAS, FALCON-
AO, ONTODNA and RIMOM methods (Part 1).
Tests Pre. Rec. Pre. Rec.
101 1.00 1.00 0.94 1.00
103 1.00 1.00 0.94 1.00
104 1.00 1.00 0.94 1.00
201 0.66 0.15 0.11 0.01
202 0.78 0.15 0.11 0.11
203 1.00 1.00 0.94 1.00
204 1.00 0.95 0.93 0.84
205 0.91 0.40 0.57 0.12
206 0.00 0.00 0.69 0.23
207 0.00 0.00 0.69 0.23
208 1.00 0.95 0.93 0.84
209 0.00 0.00 0.57 0.12
210 0.00 0.00 0.69 0.23
221 1.00 1.00 0.93 0.76
222 1.00 1.00 0.94 1.00
223 1.00 1.00 0.94 1.00
224 1.00 1.00 0.94 1.00
225 1.00 1.00 0.94 1.00
228 1.00 1.00 0.53 0.27
230 1.00 1.00 0.91 1.00
231 1.00 1.00 0.94 1.00
232 1.00 1.00 0.93 0.76
233 1.00 1.00 0.53 0.27
236 1.00 1.00 0.53 0.27
237 1.00 1.00 0.94 1.00
238 1.00 1.00 0.94 1.00
239 0.91 1.00 0.50 0.31
240 1.00 1.00 0.50 0.27
241 1.00 1.00 0.53 0.27
246 1.00 1.00 0.50 0.31
247 1.00 1.00 0.50 0.27
248 1.00 0.10 0.11 0.01
249 1.00 0.10 0.11 0.01
250 0.00 0.00 0.00 0.00
251 0.00 0.00 0.11 0.01
252 1.00 0.16 0.11 0.01
253 1.00 0.08 0.11 0.01
254 0.00 0.00 0.00 0.00
257 0.00 0.00 0.00 0.00
258 1.00 0.08 0.11 0.01
259 1.00 0.08 0.11 0.01
260 0.00 0.00 0.00 0.00
261 0.00 0.00 0.00 0.00
262 0.00 0.00 0.00 0.00
265 0.00 0.00 0.00 0.00
266 0.00 0.00 0.00 0.00
301 0.95 0.83 0.88 0.69
302 0.96 0.88 0.90 0.40
303 0.96 0.85 0.90 0.78
304 0.96 0.95 0.92 0.88
Table 2: Precision and recall values for OACAS, FALCON-
AO, ONTODNA and RIMOM methods (Part 2).
Table 2: Precision and recall values for OACAS, FALCON-
AO, ONTODNA and RIMOM methods (Part 2).
Tests Pre. Rec. Pre. Rec.
101 1.00 1.00 1.00 1.00
103 1.00 1.00 1.00 1.00
104 1.00 1.00 1.00 1.00
201 1.00 0.95 1.00 1.00
202 0.87 0.87 1.00 0.80
203 1.00 1.00 1.00 0.88
204 0.98 0.98 1.00 1.00
205 1.00 0.98 1.00 0.99
206 1.00 0.93 1.00 0.99
207 0.98 0.91 1.00 0.99
208 1.00 1.00 0.98 0.86
209 0.79 0.78 1.00 0.84
210 0.81 0.80 0.99 0.85
221 1.00 1.00 1.00 1.00
222 1.00 1.00 1.00 1.00
223 1.00 1.00 1.00 1.00
224 1.00 0.99 1.00 0.99
225 1.00 1.00 1.00 1.00
228 1.00 1.00 1.00 1.00
230 0.94 1.00 0.94 1.00
231 1.00 1.00 1.00 1.00
232 1.00 0.99 1.00 0.99
233 1.00 1.00 1.00 1.00
236 1.00 1.00 1.00 1.00
237 1.00 0.99 1.00 0.99
238 1.00 0.99 1.00 0.99
239 1.00 1.00 1.00 1.00
240 1.00 1.00 1.00 1.00
241 1.00 1.00 1.00 1.00
246 1.00 1.00 1.00 1.00
247 1.00 1.00 1.00 1.00
248 0.85 0.84 0.99 0.78
249 0.87 0.87 1.00 0.79
250 1.00 0.27 1.00 0.55
251 0.56 0.56 0.76 0.58
252 0.71 0.71 0.85 0.70
253 0.85 0.84 0.99 0.77
254 1.00 0.27 1.00 0.27
257 1.00 0.27 1.00 0.55
258 0.54 0.54 0.76 0.57
259 0.70 0.70 0.85 0.69
260 1.00 0.31 0.93 0.45
261 0.89 0.24 1.00 0.27
262 1.00 0.27 1.00 0.27
265 1.00 0.31 0.93 0.45
266 0.89 0.24 1.00 0.27
301 0.91 0.82 0.75 0.67
302 0.90 0.58 0.72 0.65
303 0.77 0.76 0.45 0.86
304 0.96 0.93 0.90 0.97
KEOD 2009 - International Conference on Knowledge Engineering and Ontology Development