A New Flexible Method for Advising Metamodel Matching
Lamine Lafi
1
, Jamel Feki
2
and Slimane Hammoudi
3
1
University of Gabès, Laboratory Miracl, Gabès, Tunisia
2
University of Sfax, Laboratory Miracl, Sfax, Tunisia
3
ESEO, Ecole Supérieure de l’Ouest, Angers, France
Keywords: Metamodel Matching, Metamodel Benchmarking, Decision Tree, Expert Assistance, Score Measure.
Abstract: One relevant issue in metamodel matching is how to select the most suitable matching technique to execute
for a given couple of metamodels, and how to adjust parameters (e.g., threshold, F-measure, quality). In this
paper, we present a flexible method for selecting the most appropriate metamodel matching technique for a
given couple of metamodels. The proposed method assists the user to choose the most suitable matching
technique that provides good quality of matches. This method relies on a new quality metric called Score
and, on using a decision tree. In order to validate our method, we conduct experimental results on ten real-
world metamodels and four recent matching techniques.
1 INTRODUCTION
Matching different data sources (Schema matching,
ontology, and recently metamodel matching) has
become a critical issue to enable the generation of
transformation rules in the Model Driven
Engineering (MDE) technology. However, some
processes needed for these matching and
transformation cannot be entirely automated due to
their complexity. Among these processes, one
relevant is mapping which became, since more than
a decade, a main topic of research in order to its
automation (Kappel, 2007), (Falleri, 2008-1), (De
souza, 2009), (Garces, 2009). It tackles the problem
of finding correspondences between elements of two
metamodels (Lopes, 2006). In the literature, several
issues around MDE have been studied and subjected
to intensive research, e.g. modeling languages
(Bézivin, 2004), (Blanc, 2005), model
transformation languages (OMG, 2005), mapping
between metamodels (Hammoudi, 2005).
Many efforts have been invested over the past
two decades to develop software tools for mapping
metamodels; the proposed tools aim to automatically
discover mappings between metamodel elements.
However, they perform matching based on specific
criteria, such as large-scale scenario (i.e., size of
metamodels, metamodels type, user preferences ....)
or complex mapping discovery (i.e., inequivalence
size of metamodels, metamodels from different
area).
Unlike research on alignment patterns (Rahm,
2001), (Do, 2007) or ontologies (Shvaiko, 2005),
(Feiyu, 2007), (Rosoiu, 2011), and to the best of our
knowledge, there is lack of platforms for evaluating
these tools in order to compare their results and
identify those best suited for a given scenario (i.e.,
couple of metamodels to be matched). This situation
does not facilitate, for a given scenario, the choice of
an appropriate matching technique that finds the
maximum of good correspondences between
metamodel elements. In a previous work (Lafi,
2013-1), we have proposed a software tool for the
assessment of metamodel matching techniques.
However, this tool lacks a feature to recommend
to the expert-user a matching technique that finds
the best matches for a given matching scenario. To
overcome this shortcoming, we define a new quality
measure called Score which aims to assist the
expert-user to select one among several available
matching techniques; i.e., the technique that
produces good results.
This new Score quality measure is calculated
based on conventional similarity measures (i.e.,
Precision, Recall, F-measure and Overall). Then, in
order to exploit the different values of the Score
metric by several combinations of scenarios with
matching techniques, we elect the concept of
decision tree. The use of decision tree will help
deciding what technique of matching is more
376
Lafi L., Feki J. and Hammoudi S..
A New Flexible Method for Advising Metamodel Matching.
DOI: 10.5220/0004714903760383
In Proceedings of the 2nd International Conference on Model-Driven Engineering and Software Development (MODELSWARD-2014), pages 376-383
ISBN: 978-989-758-007-9
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)
suitable, and then recommended it, for a metamodel
matching scenario.
This paper is organized as follows: Section 2
positions our work and motivates our contribution
for metamodel matching techniques planner. Section
3 overviews the proposed method to assist expert-
users to select a suitable matching technique for a
given scenario. It focuses on the definition of the
new Score measure and decision tree, in addition to
highlighting their usefulness. The experimental
results showing the effectiveness of our method are
presented in Section 4. Finally, Section 5
summarizes our contribution and suggests some
immediate perspectives.
2 RELATED WORK
AND MOTIVATIONS
The problem of finding mappings between database
schemas (Rahm, 2001), (Shvaiko, 2005), (Do,
2007), ontology alignment (Feiyu, 2007), (Rosoiu,
2011), XML schemas or documents and more
recently between metamodels (Kappel, 2007),
(Falleri, 2008-1), (De souza, 2009), (Chukmol,
2005) has been widely addressed during the last
decade. However, there are few works that
addressed the evaluation of metamodel matching
techniques as (Lafi, 2013-1), (Lafi, 2011). In
(Kappel, 2007), the authors propose an approach
called “lifting”, allowing transforming the source
and target metamodels into equivalent ontologies.
This approach proposes a framework for metamodel
matching thanks to a transition of ModelWare into
OntoWare. Once the matching task is over, the
transition of the ontology mapping into a weaving
model is performed. In the same work (Kappel,
2007), the authors concentrate on evaluating
schema-based matching tools. Indeed, they are using
the data provided by metamodels (Element-level)
but not data issued from models (instance-level) to
find equivalences between metamodels elements.
In (Falleri, 2008-1), before applying the
Similarity Flooding (SF) alignment algorithm, a
transformation phase is required; it transforms the
source and target metamodels into directed labeled
graphs called graph source (Gsource) and graph
target (Gtarget) respectively. Along this
transformation phase a set of six strategies to encode
the metamodel into such a graph has been suggested.
In this paper, we restrict ourselves to only three
among these six strategies namely: Standard,
Saturated, and Flattened. We have elected these
strategies since they give the best quality measures
according to (Falleri, 2008-1). SF is a generic
alignment algorithm that allows calculating the
correspondences between the nodes of two labeled
graphs (Melnik, 2002). It is based on the following
intuition: If two nodes stemming from two graphs
have been determined as similar, therefore there
would be strong opportunities for the neighboring
nodes to be similar too. SF applies five successive
phases on the input labeled graphs and then
generates an alignment between a source metamodel
and a target metamodel.
The contribution of (De souza, 2009) to this field
of metamodel matching is an algorithm that uses
structural comparison between a class and its
neighboring classes in order to select equal or
similar classes from the source and target
metamodels. This algorithm is an extension and
enhancement of the algorithm presented in
(Chukmol, 2005); it is implemented in the Semi-
Automatic Matching Tool for Model Driven
Engineering (SAMT4MDE) which is capable of
semi-automatically creating mapping specifications
and making matching suggestions that can be
evaluated by expert-users. This provides more
reliability to the matching process which becomes
less error-prone. The Extended Semi-Automatic
Matching Tool for Model Driven Engineering
(SAMT4MDE+) can identify structural similarities
between metamodels elements. However, elements
are matched based on their structure without sharing
the same meaning; this may leads to semantic
mismatches. This lack about element meaning leads
the tool to find false positives, i.e., derived
unrealistic correspondences.
AtlanMod Matching Language (AML) is a
model adaptation technique that adapts models in
three steps (Garces, 2009). In the first step, AML
computes equivalences and changes between two
input metamodels MM1 and MM2. The second step
translates the output of the previous step into an
adaptation transformation using HOT model
transformations (High Order Transformations).
Finally, the adaptation transformation is executed to
produce a mapping model.
To conclude with this state of the art, we notice
despite most of these approaches use techniques that
improve the measures of quality and the reliability
of the matching process, no attempts have been
made so far towards a comparative study of these
techniques. This had motivated us to propose an
approach for the evaluation of metamodel matching
techniques and benchmarking (Lafi, 2013-1) where
the first step was the design of the whole
ANewFlexibleMethodforAdvisingMetamodelMatching
377
architecture for this approach. This architecture has
two main components: i) Metamodel matching
evaluation and benchmarking, and ii) Generation of
transformation rules. In particular, we discussed
various aspects that contribute to the match quality
obtained as the result of an evaluation. Recently, we
have compared four recent metamodels matching
techniques to build the prototype M
2
BenchMatch
(MetaModel Benchmark Matching tool) of our
benchmark presented in (Lafi, 2013-2). However,
M
2
BenchMatch software tool has a main drawback:
it does not assist the expert-user to select a matching
technique that guarantees good results for a given
matching scenario. In this paper, we continue to
enrich our benchmark with adding a new feature that
addresses the user-expert assistance. In addition, we
can incrementally build a complete repository of
metamodel matching techniques and news
metamodels.
In this paper we are particularly interested in
studying how to assist the expert-user in selecting
the process of metamodels matching. In short, we
aim to advise a well-founded decision for the
following question: What technique should be
adopted for a given pair of input metamodels to be
matched?
For this assistance we propose a generic method
which has the merit to be extensible to new
evaluation criteria, new metamodel matching
techniques, new quality metrics. It is based on a new
measure called Score and the usage of decision trees
(c.f., Section 3.1). The necessity of defining the
Score measure was dictated by the conclusions
drawn from our previous works (Lafi, 2013-1),
(Lafi, 2013-2).
3 OVERVIEW
OF THE PROPOSED METHOD
Figure 1 depicts the proposed method for assisting
the user to choose a suitable matching technique for
a given scenario. This method is built on the
M
2
BenchMatch tool (Lafi, 2013-2), (Lafi, 2013-3)
for the assessment of metamodel matching
techniques.
M
2
BenchMatch accepts as input a set of one or
several couples of metamodels noted MM (c.f.,
Figure 1), a set of one or several matching
techniques MT (c.f., Figure 1), and all their
characteristics. As output, it returns a set of quality
metrics for each combination MM-MT (Lafi, 2013-
1), (Lafi, 2013-3), even when one or both of the
couple of metamodels or the matching technique are
newly inserted into the tool.
Figure 1: Decision Tree Construction.
These quality measures are very useful for
Re/construction of the decision tree which will be
used to assist the expert-user to choose the most
appropriate matching technique for a given scenario
(i.e., available couple of metamodel, new couple of
metamodel). Initially in our M
2
BenchMatch tool we
have studied four techniques ModelCVS, SF,
SAMT4MDE+ and AML applied on ten well known
couples of metamodels. For the first two techniques
the Ecore Alignment model is obtained immediately
after the execution of the matching process, whereas
for SAMT4MDE+ and AML the matching process
produces a first mapping model which will be
updated, adapted and validated by the expert-user.
We can advise that mapping model allows the expert
to accept, discard or modify the obtained mappings,
along with specifying correspondences which the
matcher was unable to find. It produces the Ecore
Alignment model ready for the generation of a
complete evaluation. The Evaluation enables the
expert to compare the results of several matching
techniques applied on the same pair of input
metamodels. It is based on quality metrics
(Precision, Recall and F-measure) (Do, 2002) in
order to identify the appropriate matching technique
MODELSWARD2014-InternationalConferenceonModel-DrivenEngineeringandSoftwareDevelopment
378
that guarantees the generation of good results. The
Comparison with a reference is useful when the
expert assesses a new metamodel matching
technique or a new couple of metamodel in order to
incorporate and add it into the repository of
M
2
BenchMatch. So when the expert-user would like
to add and evaluate a new couple of metamodel, the
matching process will be executed on the four
techniques available on M
2
BenchMatch tool, in
order to produce all quality metrics with all
techniques, this latter helps to the Re-construction of
the decision tree. In the same way, when new
matching technique should be added by the expert-
user to our tool, then this new technique will be
executed on all pairs of metamodels. The news
quality measures obtained are very required to the
Re-construction of the decision tree. In our case, the
decision tree is deduced from quality metrics values
obtained after the execution of matching algorithms
in previous evaluation. It can be also built according
to a new measure that we have called Score (c.f.,
Section 3.2).
3.1 Advantages of using Decision Tree
Several advantages of decision trees have been
pointed out in the literature (Quinlan, 1987),
(
Rokach, 2010); mainly, they
Are often used in context of identifying a strategy
most likely to reach a goal, by modeling decisions
and probable outcomes. In addition, they are easy
to understand and interpret,
Are self explanatory and when compacted (i.e.,
having a reasonable number of leaves) they
become easy to follow,
Can handle both nominal and numeric input
attributes,
Furthermore decision trees can be converted into a
set of rules.
Now, if we want to illustrate the construction of
decision tree based on quality metrics, then two
alternatives are offered: i) Score-Measure based
construction (c.f., Section 3.2 and Section 4.1), and
ii) preference based construction where the expert-
user favors a measure among Precision, Recall and
F-Measure (α) (c.f., Section 4.2).
3.2 Score Measure
In order to assist the expert-user to choose one of the
metamodel matching techniques, we develop a new
measure called Score (c.f., formula (5)) that
estimates the effectiveness of each metamodel
matching technique. The calculation of this Score is
based on the four well-known measures (Do, 2002):
Precision, Recall, F-Measure and Overall. We
remember each of them hereafter:
Precision
||
|
|
||


It reflects the share of real correspondences among
all found ones.
Recall
|B|
|
A
|
|B|
(2)
It specifies the share of real correspondences that are
found.
FMeasure
2∗|B|
|
A
|
|
B
|
|
B
|
|C|
2
PrecisionRecall
Precision Recall
(3)
Overall Recall 2
1
Precision
(4)
To define the Score measure we have elected the
Precision, Recall and Overall; we have intentionally
excluded the F-Measure since it is non basic, i.e.,
derivable from Precision and Recall (c.f., formula
(3)). In the calculation of the Score, the three
measures are weighted. By default, the component
measures of the Score are equally weighted (1/3).
However, in practice, this default weight could
be changed by the skilled user to raise the role of
one among these measures (c.f., Figure 3). In such a
case, the Score will be reevaluated on all couples of
metamodels available in the benchmark in order to
find out one candidate technique.
Scorei,
k
|Vm

|∗W

(5)
With:
n is the number of conventional measures used
in the calculation of Score.
Wj represents the weight for measure j, with Wj
[0, 1].
W
1.

Its default value is 1/n.


is the value of measure j obtained for a
couple k of metamodels and a matching technique i.
This value comes from the benchmark (Lafi, 2013-
2), (Lafi, 2013-3). For some techniques applied on
certain pairs of metamodels the Overall measure is
negative (mainly when precision value is < 0.5); this
can affect the Score values and then decreases the
assistance of the expert. The absolute value |Vm

|
alleviates this problem.
For a given couple of metamodels, thanks to the
Score, we are able to advise an appropriate matching
ANewFlexibleMethodforAdvisingMetamodelMatching
379
technique (i.e., that provides ‘good’ satisfaction) to
the expert-user according to their settings for
weights.
If a pair of metamodels has the maximum value
of Score throughout several matching techniques,
M
2
BenchMatch interacts with the expert-user
advising him which technique is preferable; to do so,
(s)he should enter their preferences (e.g., automatic
or semi-automatic matching technique, high or low
level of post-matching effort).
In addition to the Score, we use a second
measure called F-Measure (α) introduced in (Van
Rijsbergen, 1979); it is useful to recommend
metamodels matching techniques relying the
decision on the Precision and Recall.
3.3 F-Measure (α)
F-Measure (α) is an α-weighted generic combination
of the Precision and Recall; it is adopted from the
information retrieval domain (Van Rijsbergen, 1979)
and defined by formula (6).
FMeasure
α
PrecisionRecall
1α
PrecisionαRecall
(6)
Where α [0, 1] and indicates the importance we
wish to grant to Precision and Recall. The more the
value of α is high the more the Recall is considered
important than the Precision, and inversely. In
particular if α = 0 then F-Measure (α) = Recall,
whereas when α = 1 then F-Measure (α) = Precision.
Note that F-Measure (α) [0, 1].
In (Falleri, 2008-1) , and in order to select the
most appropriate configuration among the six
configurations of the SF metamodel matching
technique, the authors have set the value of α to 0.5;
thus granting the same importance for Precision and
Recall.
In the remaining of this paper, we will use the
Score and F-Measure (α) in order to assist the
expert-user to select a metamodel matching
technique for a given scenario. This assistance will
use decision tree.
3.4 Decision Tree for Matching
Technique Selection
To decide which metamodel matching technique is
suitable for a given pair of metamodels to be
matched, we elaborate one decision tree on the basis
of the quality measures (c.f., Figure 2).
In this decision tree, each internal node
represents a name of a MM matching technique, and
an edge between two nodes from n
i
to n
j
stands for a
condition to move from n
i
to n
j
.
As depicted in Figure 2, two branches are
allowed: Score branch (left) of the tree (i.e., where
the Score is calculated using a default value of
weight Wj=1/3; c.f., Formula (5)) and Preference
branch (right) of the tree (i.e., where the expert-user
can be assisted based on Precision, Recall or F-
Measure (α) according to his choices). Following
the left branch, techniques numbered 3 and 4 (resp.
SAMT4MDE+ and AML) are always recommended
according to the level (high or low) of human Post-
Matching effort desired by the expert-user.
On the other hand, for the Preference branch,
four techniques numbered 1, 2, 3 and 4 are
recommended (resp. SF, ModelCVS, SAMT4MDE+
and AML). Follow this right branch; three decisions
can be made according to whether the expert-user
favors the Recall, the Precision or F-Measure (α).
For instance, favoring the Recall then three
techniques are advised: SF (1), ModelCVS (2) and
SAMT4MDE+ (3).
Figure 2: Decision Tree based on Quality metrics.
Note that both techniques SF and ModelCVS
produce good results for certain scenarios (with
some couples of metamodels and/or for certain
configurations of SF). Nevertheless they sometimes
produce poor results, even very low with
ModelCVS.
For instance, if the expert-user gives more
importance to Precision for example
Precision=50%, Recall= Overall=25%, then this
preferences will be represented in the right branch of
the following decision tree (c.f., Figure 3).
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Figure 3: Score and Default Score Decision Tree.
3.5 Assisting the Matching
of Metamodels via Decision Trees
The kernel of traditional matching tools is the
aggregation measure, which combines the similarity
values computed by different matching techniques.
As this aggregation measure suffers from several
drawbacks (c.f., section 2), our idea consists in using
a decision tree instead.
Given a new couple of metamodels to be
matched, our objective is to select and then apply the
most appropriate matching technique for this couple;
i.e., the matching techniques that gives good
matching result. To do so, we appeal to decision
tree. Decision trees are used in similar contexts in
(OMG, 2005), they assist the expert-user during the
selection of the best matching technique.
The decision and selection of the suitable
matching technique are based on the quality metrics
which are also influenced by the input metamodel
size and the characteristics of each technique. This
decision satisfies the condition and criteria on the
edges that aims to access a next node when other
criteria or preferences need to be considered when
matching two metamodels using decision tree.
According to this tree, the edge for which its
condition is satisfied leads to the next tree node.
This process will iterate until a leaf node is reached,
indicating whether the matching technique should be
recommended or not.
4 DEMONSTRATION
AND EXPERIMENTS
In order to assist the expert-user for a given scenario
or comparison of metamodel matching techniques,
we conduct an experimental evaluation based on the
decision tree of figure 2. More accurately, we
experiment the four techniques ModelCVS, SF,
AML and SAMT4MDE+ on the ten couples of
metamodels below, described in (Walderhaug,
2006), (Kappel, 2006), (Falleri, 2008-2), (Budinsky,
2003), (OMG, 2003) and (Fleurey, 2009):
Table 1: Ten couples of Metamodels.
Couples of Metamodels Size*
Ecore2Minjava2.0 Large
Ecore2UML Large
Webml2ODM Small
traceabilityToolMM2traceRepository Medium
etrace2traceabilityToolMM Medium
Ecore2UML2.0 Large
BibTeXA2BibTeXB Small
Ecore2Minjava Large
Ecore2Kermata Large
Minjava2Kermata Large
(*) The size of a metamodel is the number of its elements
(Classes, data type…). Small: size <80; Medium: 80size<150;
Large: size150.
We are interested in these four techniques since they
are recent and accessible through their software
tools.
Note that both AML and SAMT4MDE+ enable
the expert to intervene and improve the matching
automatically obtained. Therefore, in order to insure
that the evaluation is conducted in the same
conditions for the four matching techniques we
exclude expert interventions during these two
techniques. This implies that matching results are
not influenced by the expert skills. To conduct this
evaluation, we provide two alternative measures for
assisting the expert-user: i) Score-based (c.f.,
section 4.1), and ii) F-Measure (α)-based (c.f.,
section 4.2).
4.1 Score-based Measure for Assisting
the Expert-user
Note that in our experiment we have chosen a
default weight Wj = 1/3 for the three measures
precision, recall, and overall in calculating the
Score. If an expert-user wishes to privilege one of
these three measures (e.g, accuracy, indicating that
the correct number of mappings is more important to
him) in Score calculation then he can set different
weights. This is done through Score calculation
using different weight.
Figure 4 shows the values of Score obtained for the
four metamodel matching techniques. We note that
ANewFlexibleMethodforAdvisingMetamodelMatching
381
Figure 4: Score values produced with four techniques on
ten couples of metamodels.
the SAMT4MDE + technique gives the highest
Score for the two couples of metamodels
Ecore2UML and Ecore2UML2.0. For the other
couples, the AML technique has best Score values.
4.2 F-Measure (α) for Assisting
the Expert-user
The F-Measure (α) measurement (cf., formulas (6))
is presented on the right branch of the decision tree
(c.f., Figure 2) by the label preferences (Precision,
Recall, F-Measure (α)). In this section we restrict
ourselves only to the curve F-Measure (α), since
Precision, Recall have been presented in (Lafi, 2013-
1) and then used during the construction of tree.
Figure 5: F-Measure (0.5) values produced with four
techniques on ten couples of metamodels.
After testing we found that AML gives the best
value of F-Measure (α) with most pairs of
metamodels. Comprehensive results obtained by this
technique are listed in Figure 5. At the end of these
experiments, we recommend the technique AML,
because that the results of the various measures are
good in the most part case (F-Measure (α) close to 1;
Score greater than 0.5).
Note that these results appear to be more
important than those obtained through the alignment
of XML schemas or ontologies. This is due to the
intervention of the expert-user to correct, adapt and
validate the suggestions of mapping produced by the
technique SAMT4MDE+.
5 CONCLUSIONS
Metamodel matching stands for the keystone of the
semi-automatic transformation process. In this
paper, we have tackled one problematic closely
related to this issue; indeed our objective was to
assist expert users to select an appropriate matching
technique for a given couple of metamodels to be
matched. In order to reach this objective, we have
presented a novel and flexible methodology for
metamodel matching assistance; it relies on i) the
definition of two new measures called Score, F-
Measure (α), and ii) the use of decision tree. The
Score measure reuses the standard quality metrics
(Recall, Precision…); it is returns a bonded value (in
the range [0.1]) evaluating the efficiency of applying
a given matching technique on a pair of metamodels.
Whereas the decision tree concept is adopted to
determine the most appropriate technique among all
matching techniques available within our
M
2
BenchMatch software tool (Lafi, 2013-2). Based
on these two elements, the proposed flexible method
improves matching quality. The flexibility comes
from the flexibility of the Score function. Actually,
two strategies are offered for the calculation of the
Score according to whether the expert wants to
privilege the standard Recall measure or not.
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