Sana Sellami, Aicha-Nabila Benharkat, Youssef Amghar
LIRIS-INSA de Lyon, National Institute of Applied Sciences of Lyon, 69621 Villeurbanne, France
Rami Rifaieh
San Diego Supercomputer Center, University of California, La jolla - 92093-0505, California, U.S.A.
Keywords: Matching, Quality of Matching (QoM), Large Scale, Optimization techniques.
Abstract: Matching Techniques are becoming a very attractive research topic. With the development and the use of a
large variety of data (e.g. DB schemas, ontologies, taxonomies), in many domains (e.g. libraries, life
science, etc), Matching Techniques are called to overcome the challenge of aligning and reconciling these
different interrelated representations. In this paper, we are interested in studying large scale matching
approaches. We define a quality of Matching (QoM) that can be used to evaluate large scale Matching
systems. We survey the techniques of large scale matching, when a large number of schemas/ontologies and
attributes are involved. We attempt to cover a variety of techniques for schema matching called Pair-wise
and Holistic, as well as a set of useful optimization techniques. One can acknowledge that this domain is
on top of effervescence and large scale matching need much more advances. So, we propose a
contribution that deals with the creation of a hybrid approach that combines these techniques.
Actually, we are witnessing an explosive growth in
the amount of data being collected in the business
and scientific area. Databases in these domains are
filling up with huge amounts of data information
with different representations. These data are
heterogeneous, frequently changing, distributed, and
their number is increasing rapidly. The presence of
vast heterogeneous collections of data causes one of
the greatest challenges in the data integration field.
Hence, Matching techniques attempt to develop
automatic procedures that search the
correspondences between these data in order to
obtain useful information. In fact, Matching is an
operation that takes data as input and returns the
semantic similarity values of their
elements/attributes. In our paper, we describe new
research work of large scale matching, which differs
from the existing research papers (Rahm and
Bernstein, 2001), (Shvaiko and Euzenat, 2005) in
terms of large scale necessities. In fact, traditional
schema Matching works are developed for small
scale and static integration scenarios, in which
automatic Matching technique is often an option to
reduce human labour. In contrast, in large-scale data
integration scenarios (Madhavan et al., 2007), the
Matching needs to be as automatic as possible and
scalable to large quantity of data. Furthermore,
current matching algorithms have been performed
with simple data holding a small number of
components, whereas in practice, real world data are
voluminous. The size of data can impact match
accuracy because it determines the search space for
match candidates. In consequence, the quality of
Matching will be decreased. We introduce, then, the
major criteria of an ideal Matching system at large
scale. We define a quality of Matching (QoM) in
terms of factors and metrics that can be used to
evaluate large scale matching systems. This analysis
of state of the art allows us to make some
conclusions and observations about the existing
matching works. Depending on these observations,
we suggest the creation and the elaboration of a
hybrid approach that combines these known
techniques to deal with a large scale Matching.
This paper is organized as follows. In section 2, we
define and describe a quality of Matching (QoM) to
evaluate large scale matching systems. Section 3
presents a review of state of the art matching at
Sellami S., Benharkat A., Amghar Y. and Rifaieh R. (2008).
In Proceedings of the Tenth International Conference on Enterprise Information Systems - DISI, pages 355-361
DOI: 10.5220/0001721903550361
large scale. In section 4, we describe our vision for a
large scale matching. Finally, we conclude and
discuss future work.
Evaluations of schema matching systems have been
deeply studied in (Do et al., 2002) discussing
various aspects (input, output, match quality
measures, effort) that contribute to the match quality
obtained as the result of an evaluation. In the large
scale context, we define and propose a Quality of
Matching (QoM) which is an evaluation of large
scale matching systems. The quality concept has
been used in several domains as an important phase
of evaluation in the current information systems.
However, there exists little of work (Bernstein et al.,
2004), (Duchateau et al., 2007) which tackles the
aspect of quality in the matching process at large
scale. Therefore, we estimate that is important and
interesting to relate the aspect of quality to the
scalable matching techniques. In fact, the quality
assessment brings to the users an optimal solution to
accomplish their needs. Quality of matching (QoM)
means for us an optimization of large scale
matching system. We firstly need to identify which
quality factors to be evaluated. The selection of the
appropriate quality factors implies the selection of
metrics and the implementation of evaluation
algorithms that measure and estimate such quality
factors. We distinguish between two aspects: the
factors that influence the quality and the metrics to
evaluate and measure the quality of matching
2.1 Quality Factors in Large Scale
The factors that have an influence on large scale are
essentially related to the context (input data and
domain) and matching systems or algorithms. We
summarize these quality factors in the following
2.1.1 Factors Related to the Context
Input Data. Quality depends on the internal quality
of the sources (their coherence, their completeness,
their freshness, etc.), on the confidence about
producers of these sources. Moreover, we should
determine the type, representation and structure of
data that have been used (schemas, ontologies,
taxonomies, query interfaces etc). These
characteristics influence the quality of matching.
Domain. Data reside at different sources and
consequently they are extracted from different
domains. Data managed by different sources are
typically heterogeneous, and data can be incorrect,
incomplete, and noisy, thus it may be data of poor
quality. Therefore, it is important to determine if the
data source result from different or the same
domains, the characteristic of domains, etc.
2.1.2 Factors Related to Matching Systems/
Techniques. In a context where the information is
produced by sophisticated algorithms, the quality
measurement requires a fine knowledge of the
computing process of this information. Moreover,
the use of these algorithms and techniques (i.e. the
type of the matchers implemented (schema vs.
instance level, element vs. structural level, language
vs. constraint based, etc), auxiliary information,
optimization techniques, etc.) could be very
Needs in Runtime Performance. The quality of
matching solutions is measured in terms of how
long applications take to be run to completion when
tasks of applications are allocated to nodes based on
decisions of matching algorithms. This duration is
called execution time. Efficient matching algorithms
must keep times to a minimum.
Complexity. The matching problem is an extreme
case in terms of size and complexity. In fact, the
schema matching problem is a combinatorial
problem with an exponential complexity. This
complexity is due to the large number and size of
data (number of schemas/components), the
expensive computation of semantic similarity (e.g
using the auxiliary resources). Consequently, this
makes the naïve matching algorithms for large
schemas prohibitively inefficient. Therefore, the
complexity is a property that affects the quality of
matching algorithms.
Human Interaction (Wang et al., 2007). Matching
operation cannot be entirely automated; it is still
largely conducted by hand, in a labor-intensive and
error-prone process. The manual matching has now
become a key bottleneck in building large-scale
information management systems. Therefore, user
or designer input is necessary to generate correct
ICEIS 2008 - International Conference on Enterprise Information Systems
2.2 Quality Metrics in Large Scale
In this section, we define the metrics that are
involved individually in existing large scale
matching systems evaluations. Our classification
could be a support to QoM (Quality of Matching):
Performance. The performance is measured in
terms of efficiency and pertinence: Efficiency is the
time needed by the system to solve a matching
problem. Pertinence evaluates the relevance of
matching results. This metric can be calculated by
precision and recall values (Do et al., 2002).
Accuracy. Called also Overall has been proposed
specifically in schema matching context. This
measure considers the post-match effort needed for
adding false negative and removing false positives.
Accuracy depends on both Recall and Precision
Manual Effort (Wang et al., 2007). It’s very
important to specify the kind of manual effort
during the pre-match process and the post-match
process (correction and improvement of the match
Scalability. It is a property of systems to keep
functioning correctly even with the adding new
elements. A system, whose performance improves
after adding hardware, proportionally to the capacity
added, is said to be a scalable system. An algorithm,
design, program, or other system is said to scale if it
is suitably efficient and practical when applied to
large situations (e.g. large input data set or large
number of participating nodes in the case of a
distributed system).
Adaptability (Bharadwaj et al., 2004). Refers to
the degree to which adjustments in practices,
processes, or structures of systems are possible to
projected or actual changes of their environment.
This criterion could measure the degree of change
that a system can support.
Extensibility. Means that the system has been so
architected that the design includes all of the hooks
and mechanisms for expanding/enhancing the
system with new capabilities without having to
make major changes to the system infrastructure.
Therefore, matching systems should be extended by
adding matching techniques, algorithms or
customized data structures and operators.
We are interested in our work in Matching
techniques that aim at identifying semantic
correspondences between schemas, ontologies,
query interfaces, etc. In the literature, we can
distinguish between two matching approaches:
Pair-Wise matching and holistic matching. We
discuss in this section the research works related to
these approaches and we underline the most
employed optimization techniques.
3.1 Pair-wise Matching
Matching has been approached mainly by finding
pair-wise attribute correspondences, to construct an
integrated schema for two sources. Several
pair-wise matching approaches over schemas and
ontologies have been developed.
3.1.1 Schema Matching
Being a central process for several research topics
like data integration, data transformation, schema
evolution, etc., schema matching has attracted much
attention (Avesani et al., 2007), (Bernstein et al.,
2004), (Lu et al., 2005), (Smiljanic et al., 2006), (Do
and Rahm, 2007) by researchers community. We are
more interested to the approaches that integrate the
clustering and fragmentation techniques. In fact,
these techniques aim at reducing the dimension of
the matching problem and improving the quality of
the matching (QoM). In (Do and Rahm, 2007), the
authors have developed the fragment-based match
approach, i.e., a divide and conquer strategy which
decomposes a large matching problem into smaller
sub-problems by matching at the level of schema
fragments. This approach is done «a priori» before
the matcher’s execution. The fragment-based
approach represents an effective solution to treat
large schemas. However, only few static fragment
types are supported and matching large fragments
lead to long execution time. The authors in
(Smiljanic et al., 2006) propose a clustered schema
matching technique which is a technique for
improving the efficiency of schema matching by
means of clustering. The clustering is introduced «a
posteriori» after the generation of matching
elements. Clustering is then used to quickly identify
regions in the schema repository which are likely to
include good matchings for the smaller schema. The
clustered schema matching is achieved by an
adaptation of the clustering algorithm K-means (Xu
and Wunsch, 2005). Moreover, Clustering was
combined with B&B (Branch and Bound) algorithm
to find highly ranked matchings. Using this
optimization algorithm allows to discover efficiently
the best solutions in the whole search space. Though,
the improved efficiency comes at the cost of the loss
of some matchings. The loss mostly occurs among
the matchings which rank low. However, there is no
measure of cluster’s quality that can be used to
decide which clusters have better chances to
produce good matchings. In addition, the proposed
approach is restricted to 1:1matchings.
3.1.2 Ontology Matching
Ontology matching is a promising solution to the
semantic heterogeneity problem. It finds
correspondences between semantically related
entities of the ontologies. The increasing awareness
of the benefits of ontologies for information
processing has lead to the creation of a number of
large ontologies about real world domains. The size
of these ontologies causes serious problems in
managing them. Actually, many approaches (Hu and
Qu, 2006), (Hu et al., 2006), (Qu et al., 2006),
(Stuckenschmidt and Klein, 2004), (Wang et al.,
2006) have been proposed in literature to study the
large ontology matching problem. For instance, to
cope with the large ontologies matching, (Hu and
Qu, 2006) propose a partitioning-based approach to
address the block matching problem. The authors
consider both linguistic and structural characteristics
of domain entities based on virtual documents for
the relatedness measure. Partitioning ontologies is
achieved by a hierarchical bisection algorithm to
provide block mappings. Another approach has been
proposed (Wang et al., 2006) to deal with large and
complex ontologies. This is a divide-and-conquer
strategy which decomposes a large matching
problem into smaller sub-problems by matching at
the level of ontology modules. This method uses the
E-connection (Grau et al., 2005) to transform the
input ontology into an E-connection with the largest
possible number of connected knowledge bases.
However, this approach does not discover the
complex mappings and does not realize the
matching between several voluminous ontologies.
3.2 Holistic Matching
Traditional schema matching research has been
determined by pair-wise approach. Recently,
holistic schema matching has received much
attention due to its efficiency in exploring the
contextual information and scalability. Holistic
matching matches multiple schemas at the same
time to find attribute correspondences among all the
schemas at once. These schemas are usually
extracted from web query interfaces in the deep
Web. Several current approaches to holistic schema
matching (Chang et al., 2005), (He and Chang,
2006), (He et al., 2004), (He and Chang, 2003),
(Madhavan et al., 2005), (Pei et al., 2006), (Su
et al., 2006b), (Su et al., 2006a) rely on a large
amount of data to discover semantic
correspondences between attributes. Holistic
approach has been introduced in (He and Chang,
2003). The authors propose MGS framework which
is an approach for global evaluation, building upon
the hypothesis of the existence of a hidden schema
model that probabilistically generates the schemas
that we had observed. The authors propose to apply
hypothesis testing to quantify how consistent the
schema model is with the data. Nevertheless, this
approach does not take into consideration complex
mappings. DCM framework has been proposed in
(He et al., 2004) for local evaluation, lying on the
observation that co-occurrence patterns across
schemas often reveal the complex relationships of
attributes. However, these approaches suffer from
noisy data. The works suggested in (Chang et al.,
2005),(He and Chang, 2006) outperform these
approaches by adding sampling («a priori») and
voting («a posteriori») techniques, which is
inspired by bagging predictors. HSM (Holistic
Schema Matching) (Su et al., 2006b) and PSM
(Parallel Schema Matching) (Su et al., 2006a) have
been proposed to find matching attributes across a
set of Web database schemas of the same domain.
HSM and PSM are purely based on the occurrence
patterns of attributes and requires neither
domain-knowledge nor user interaction. The
approach presented in (Pei et al., 2006) proposes a
novel clustering-based approach to schema
matching. However, this approach focused only on
1:1 matchings.
3.3 Summary and Classification of
Matching Approaches
In this section, we propose a classification of the
previous described approaches in (Figure1)
according to the optimization techniques. We
categorize these techniques in four classes: machine
learning techniques, description logics, heuristic
algorithms and statistical algorithms. In fact, most
of the proposed approaches at large scale integrate
these techniques to improve and optimize the
quality of Matching (QoM). (Figure1) can be read
from two points of view: In top down view, we
present different input data occuring in both holistic
and pair-wise approaches. In bottom up view, we
ICEIS 2008 - International Conference on Enterprise Information Systems
can base the classification on methods related to the
optimization techniques (e.g clustering,
modularization, etc). This classification is inspired
from the one presented in (Shvaiko and Euzenat,
2005) by taking into consideration only large scale
matching techniques.
Figure 1: Classification of large scale Matching
We can outline from our study on matching the
following observations and some open issues that
require further research:
In pair-wise approach, matching is only achieved
between two data sources (schemas/ontologies).
However, scalable matching system must be able
to realize matching among great number of data
sources in order to satisfy the needs of real
applications. Therefore, pair-wise approaches do
not satisfy the scalability criterion.
Holistic matching is a statistical approach. This
approach focuses on observations of the
co-occurrence information of attributes across
many web query interfaces which involve small
number of components in the Deep Web. Then,
Holistic approaches have not been applied to
ontologies or taxonomies.
In the majority of existing matching works, the
complex mappings are not determined. Most of
the existing approaches are focused on the
simple matching (1:1). However, discovering
complex mappings is a critical semantic
operation in the matching problem. Since, the
ultimate goal of schema Matching is to derive a
Mapping from multiple sources to target
(Bernstein et al., 2008), (Melnik et al., 2007).
Holistic or pair-wise approaches integrate
optimization techniques, which are usually
performed either in a priori matching or in a
posteriori matching.
Few works have proposed quality factors and
criteria. In the majority of existing works,
quality has been defined in terms of precision
and recall measures. Therefore, this is
insufficient to evaluate the real quality of
matching (QoM) system at large scale.
The majority of Pair-wise matching approaches
find attribute correspondences with using
auxiliary information. Several works have been
proposed for this purpose. For instance,
approaches proposed in (Bernstein et al.,
2004),(Do and Rahm, 2007) describe the utility
to use several matchers. The main idea is to
combine the similarities predicted by multiple
matchers to determine correspondences. Holistic
matching, on the other hand, does not employ
any semantic resource for the determination of
the correspondences.
Based on these observations, we illustrate our vision
about a large scale matching system that must
include the following points: First, we assume that
is interesting to combine the holistic and pair-wise
approaches. In fact, Matching in pair-wise systems
is usually achieved between only two voluminous
data sources. In contrast to this approach, holistic
matching is performed between a set of query
interfaces. The combination of holistic and
pair-wise matchers analyzes schemas/elements
under different aspects, resulting in more stable and
accurate similarity for heterogeneous schemas.
Therefore, their combination can effectively
improve the quality of matching. Second, we note
the importance of optimization techniques, specially
clustering and fragmentation approaches. The main
purpose is to deal with large data. With the reduced
problem size, we aim to optimize and improve the
quality of the matching (QoM). We also underline
that the approaches including optimization
techniques have a better quality match. Moreover,
we notice that these techniques have been integrated
either before matching operation (e.g splitting a
priori) or after matching operation (e.g grouping a
posteriori). We estimate that is interesting to have a
matching system including these techniques in a
priori and posteriori steps. In fact, splitting a priori
represents an efficient alternative to deal with very
large data representations and to reduce the size of
large matching problem into small sub-problems.
Moreover, grouping a posteriori allow us to select
and preserve the highly ranked correspondences
result. This step improves the efficiency of schema
matching. The combination of these techniques
increases the feasibility of large scale matching
system. Third, we consider that is important to
integrate a quality evaluation in every step of a
matching process. Quality evaluation is essential to
guarantee the reliability of data representation in
order to avoid noisy data. It ensures the consistency
of using algorithms and techniques. Moreover, it is
necessary to evaluate the matching results and to
estimate if the matching system satisfies the quality
criteria. Precisely, this quality evaluation allows us
to test the performance, accuracy, scalability,
adaptability and extensibility of matching system at
large scale. Finally, we assess that is essential to
employ some auxiliary semantic information to
identify finer matching and to deal with the lack of
background knowledge in matching tasks. It’s also
the way to obtain semantic mappings between
different input data. Following these ideas, we
describe here an instance of our vision for a large
scale matching system. (Figure 2) outlines a general
procedure for matching at large scale.
Let a set of voluminous (size and number) data, we
are going to split up all these sources. This dividing
step includes several quality constraints: splitting
criteria, reliability of the fragments obtained
characteristics of data (structure, format), etc… This
phase can be either automatic or manual. Thereafter,
we apply a holistic matcher to find similar
fragments with a statistical manner. For data in the
same domain, those are about a specific kind of
topic, usually share common characteristics. The
matching resulted can be saved for reusing in the
next operations. After determining the similar
fragments, we use a pair-wise matcher to find the
more complex relations between components. We
can employ an auxiliary semantic resource to find
these correspondences (e.g determining mapping
expressions). Afterwards, we group a posteriori the
matching results to select the highly ranked
matchings that represent the most pertinent results.
We test then the quality of these results to satisfy the
accuracy criterion. These results will be saved for a
forthcoming use.
Figure 2: A general Procedure for large scale Matching
This paper presented a broad scope of matching at
large scale categories and characteristics, and
surveyed related work. We have presented our
motivation to study the solutions for matching at
large scale. Since quality is very important to
evaluate matching systems, we have described
metrics to measure the quality of Matching (QoM)
and defined the different factors that influence the
quality. We have achieved a state of the art study
covering existing approaches: Pair-wise and holistic
Matching. We have summarized this survey with
listing some important issues and research trends for
Matching techniques at large scale. To resume,
matching at large scale requires deep domain
knowledge: characteristics and representations of
data, user’s needs, time performance, etc. There is
no matching system that can tackle completely all
the problems mentioned in this study. We intend in
the future to design a matching system that provides
all the features described in the previous sections:
formalizing quality metrics, splitting, and grouping
(e.g clustering) techniques (in a priori and
posteriori phases). The finality of this work is to
conceive a complete matching system able to realize
matching at large scale between several schemas,
ontologies, taxonomies to be applied in various
fields such as biology, phylogeny, etc.
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