=Paper=
{{Paper
|id=Vol-2306/paper6
|storemode=property
|title=Holistic Multiple Ontologies Merging
|pdfUrl=https://ceur-ws.org/Vol-2306/paper6.pdf
|volume=Vol-2306
|authors=Samira Babalou
|dblpUrl=https://dblp.org/rec/conf/ekaw/Babalou18
}}
==Holistic Multiple Ontologies Merging==
https://ceur-ws.org/Vol-2306/paper6.pdf
Holistic Multiple Ontologies Merging
Samira Babalou
(Early Stage PhD)
Heinz-Nixdorf Chair for Distributed Information Systems
Institute for Computer Science, Friedrich Schiller University Jena, Germany
samira.babalou@uni-jena.de
Abstract. Ontologies as the main infrastructure to represent data in the
Semantic Web are widely developed independently in each area. When it
comes to finding a suitable ontology for a given application, two problems
occur: Often, an ontology will cover just a part of the domain of interest
or competing ontologies modeling the domain from different viewpoints
exist. Thus, before being able to leverage the power of ontologies, they
themselves need to be integrated. This is a challenging task. The existing
approaches are mostly limited to a binary merge. However, by the
large availability of the relevant ontologies in the desired domain, an
efficient multiple ontologies merging technique is often a necessity to
overcome the scalability problem. This research thus advocates for the
development of a holistic, efficient multiple ontologies merging method
called CoMerger, to satisfy the scalability issue. For efficient processing,
rather than merging a large number of ontologies, we merge a small
number of clusters. To approve the feasibility of our approach, we will
run CoMerger on real-life datasets. Further, our platform will be freely
accessible through a live portal.
Keywords: Semantic web . Ontology merging . Ontology mapping
1 Problem Statement
Ontologies are the semantic model to represent data on the Semantic Web. Often
a domain has more than one ”standard” ontology for the same general concepts.
They either cover just a part of the domain of interest or model the domain
from different viewpoints. In this fashion, multiple heterogeneous ontologies
are independently developed in each domain. In real-world applications of the
Semantic Web, this is an essential demand to interoperate with more than two
ontologies toward acquiring the desired knowledge for scientists. Indeed, different
ontologies cover particular aspects of a domain of discourse but overlap to a
certain degree. Therefore, an efficient technique for merging multiple ontologies
is often a necessity both during ontology development and when ontologies are
used in conjunction with data at the query processing level. This can be a
cost-efficient approach and saves a lot of development effort. Thus, before being
able to leverage the power of ontologies, they themselves need to be integrated.
This is a challenging task.
Existing ontology merging approaches [14,15,18,23] are mostly limited to
merging only two ontologies, partly due to using a binary merge (i.e., merging
two ontologies at a time). In principle, a series of binary merges can be applied
to more than two ontologies, however, they are no longer sufficiently scalable
and viable for a large number of ontologies [17]. Precisely, to merge n ontologies,
a binary-merge approach needs to run the n.(n−1)2 pairwise alignment processes
and (n − 1) combination operations in an incremental fashion. Nevertheless,
merging multiple ontologies (n > 2) at the same time has not been extensively
studied mainly due to the much more complex search space and it still remains
one of the key challenges in the future research agenda. Therefore, to overcome
the binary merge limitations, the holistic strategy has been introduced as a
feasible and efficient method in [17]. Following this approach, in our proposed
framework CoMerger, we advocate for developing an efficient, holistic merging
technique that scales to many ontologies. It gets as an input a set of ontologies
with their alignments and automatically generates a merged ontology with a set
of output mappings between the merged and the input ontologies. At first, the
n input ontologies will be clustered into k clusters. Afterward, the clusters will
be combined based on the corresponding pairs to produce the merged ontology.
To this end, the main problem statement of our research is to enable a holistic
ontology merging method by extending Semantic Web technologies. Thus, the
general research question that my thesis tries to address is:
Research Question: How can the holistic approach be applied for
merging n ontologies to overcome the scalability problem?
To contribute to the main research question, two RQs with further
sub-questions can be concluded.
RQ1- How can the elements of input ontologies be effectively clustered into k
clusters based on the detected correspondences to facilitate the merge process?
RQ1-1- Does the clustering process lead to a significant reduction in
execution time and complexity of merging process without compromising
quality?
RQ1-2- What is the effect of varying the number of clusters k on the merge
quality?
RQ2- How can the combination of k clusters efficiently generate the merged
ontology?
RQ2-1- Which requirements should be considered for the merging step?
RQ2-2- How to fulfill these requirements?
RQ2-3- How can a high quality of the merge result be achieved?
RQ2-4- How to accomplish consistency in the merged ontology?
2 State of the Art
Merging strategies basically have been divided into two main categories [6]:
”binary” and ”n-ary”. The binary approach allows merging of two ontologies
at a time, while n-ary strategy lets to merge n ontologies (n > 2). To
deal with merging more than two ontologies, the binary strategy needs the
quadratic complexity of merging operations and also needs a final analysis to
add missing global properties [6]. However, in the n-ary strategy, the number
of merging steps is minimized. Moreover, a considerable amount of semantic
analysis can be performed before merging, thus avoiding the necessity of a further
analysis and transformation of the merged ontology. This approach also is called
”holistic” strategy [17]. Indeed, with continuously increasing amount of data
being produced, developing solutions to deal with the simultaneous merging of
different ontologies is becoming necessary.
To process multiple ontologies, for instance, in the multiple ontologies
matching scenario in [11], to match 4000 web-extracted ontologies on six
computers using a pairwise strategy took about one year, which indicates the
insufficient scalability of pairwise strategies. As a further example of multiple
ontologies merging, the integration process in the biomedical ontology UMLS
Metathesaurus [7] was highly complex and involved a significant effort by domain
experts. To the best of our knowledge, the holistic ontology merging has not
been practically applied and still is one of the key challenges in this field. As
an example, Porsche [20] semi-automatically merges many tree-structured XML
schemas and holistically clusters all matching elements in the nodes of the merged
schema. The final merge result depends on the order in which the source schemas
are matched and merged. Low alignment accuracy and low minimality on the
merge result arise from its simple heuristic functions. Furthermore, consistency
issues have not been considered.
Principally, general processes in the existing approaches seem to indicate
two different strategies: ”one-level merge” and ”two-levels merge”. In the latter
one, an intermediate merge result is produced at the first level. Then, in the
second phase, the intermediate result is refined to produce a final merge result. In
contrast, one-level merge tends to produce the merge result in one incrementally
processing step [10,13]. In each element combination, they analyzed whether it
does not have any inconsistencies with other previous merged elements. Although
the two-levels merge is the most used strategy in the literature reviews [14,18,23],
there is no comparison between the effectiveness of these two strategies. We have
prioritized to use one-level merge strategy and check the arising inconsistencies
before applying each combination.
3 Proposed Approach
This research aims to comprehensively address the holistic multiple ontologies
merging issue to improve the shortcomings of previous approaches. We have been
investigating the existing methods and found the scalability issue as an ongoing
challenge. Therefore, we have four main objectives:
– Overcoming the scalability problem in merging multiple
ontologies: We aim to develop a framework for holistic multiple ontologies
merging to overcome the scalability issue.
– Achieving a high accuracy in the merge result: In order to gain a high
accuracy, the merge requirements should be fulfilled, however, it should be
possible to customize them to the task at hand. We plan to derive a method
to apply these customized requirements in a way that they do not contradict
each other.
– Developing a web tool: As a proof of concept, this research aims to
develop a web tool for merging multiple ontologies. This tool can be divided
into two sections: merger and evaluator. The latter includes systematic
criteria for evaluating the merged ontology independent from the merge
techniques.
– Validating: To evaluate our framework, we will carry out a set of
experimental tests to analyze the performance of the tool and the merge
algorithm.
4 Methodology
Ontology merging is the process of building a new coherent ontology (called
a merged ontology) from given input ontologies to provide a unified access
on the domain. Principally, it requires to know which elements are equal
to each other. This can be achieved by an ontology alignment method to
detect the corresponding pairs. Many significant advances such as [1,8,21,22]
have already been made for the automatic ontology alignment. Moreover, the
most state-of-the-art matching systems participate in OAEI campaigns 1 have
achieved promising results in several use cases. Therefore, we assume that the
alignments are already determined by an existing tool in this research. Same as
our assumption, using the pre-determined mappings has previously been applied
in [13,16,18].
Below, a schematic of our holistic multiple ontologies merging framework is
illustrated in Fig. 1. It gets as an input a set of ontologies with their alignments,
and automatically generates a merged ontology with a set of output mapping
between the merged and the input ontologies. In the preprocessing step, the
input ontologies and the pre-determined mapping are imported into a repository.
Afterwards, the elements of the n input ontologies will be clustered into the k
clusters in the clustering step. Finally, the combination phase will be applied
to combine the k created clusters into the merged ontology. Thus, instead of
the quadratic merge process, this holistic approach needs k merge operations.
Here the number of clusters is noticeably smaller than the number of input
ontologies (k n). Therefore, to overcome the scalability problem in this
framework, rather than merging a large number of ontologies (n), we merge a
small number of clusters (k). Once the merged ontology is created, the output
1
http://oaei.ontologymatching.org/
Fig. 1: Schematic of the holistic ontologies merging framework
mapping between the merge and the input ontologies will be produced through
a backward process in the postprocessing phase.
Contributing to RQ1, the clustering component in Fig. 1 is accelerating the
combination process by minimizing the search space in the way of breaking
merging n ontologies into the merging of k clusters. Generally, clustering z
elements of all input ontologies into k clusters needs z(z−1)
2 times comparisons.
Alternatively, would be comparing z − k elements with only k elements, which it
requires z×k times comparisons. Therefore, we will use a core-clustering method,
where all z elements will be compared with k core of the clusters through a
semantic similarity function. We deem that the elements with a high number
of correspondences in the input ontologies are more suitable to be considered
as the cores. In this follow, the core of each cluster and the optimal number of
clusters for each set of ontologies will be dynamically determined by using their
mapping information.
To address RQ2, we divide the combination process into two steps
through the combining component (Fig. 1): (i) intra-combination, and (ii)
inter-combination. In the first step, the elements inside the clusters will be
combined to create k sub-ontologies. In the inter-combination step, the k
generated sub-ontologies will be attached to create the final merged ontology.
Here, detecting the finest place of the join is a challenging task. We intend to
find it in a heuristic method by narrowing comparison between the leaves and
the core of sub-ontologies.
To capture the accuracy and consistency of the result, the
predetermined merge requirements will be assessed before applying each
combining process. The existing merge technique partially aims to satisfy some
of them, however, they should be customized by the task at the hand (RQ2-1 ).
To this end, we are providing a checklist including a variety of requirements
(extracted from [13,15,16,18]), where the user can customize it (as is shown in
Fig. 2). Each requirement will be performed as a set of rules with a weighting
strategy (RQ2-2 ), in the way that they do not have a contraindicated with
each other. Utilizing these requirements tends to guarantee the consistency of
the created merged ontology, and consequence the quality of the merge result
(RQ2-3 ). To accomplish consistency (RQ2-4 ), the conflicts and inconsistencies
should be detected at first via a reasoner, then they should be resolved. This is
under our survey to handle inconsistencies with some conflict resolver such as
subjective logic-based approach [12].
To develop a web-based tool, a preliminary version of CoMerger
is being provided by using the OWL API library 2 and a user-friendly
GUI as is shown in Figs. 2 and 3. The further version might also include the
analysis the execution log of the merge evaluator and visualization of the results.
Evaluation Protocol: We address the required evaluations as below:
E1. To study our main research question, we will compare the quality and
complexity of merging multiple ontologies by a series of the binary merges
rather than our holistic merge approach. The quality will be measured as
states in E5, and the complexity will be measured as the number of required
operations.
E2. The aim of clustering is to accelerate the merge process. Therefore, to
evaluate RQ1-1, we will compare the quality of the merge result and
complexity of the merge operations with and without using clustering.
E3. To evaluate our heuristic approach on RQ1-2, we will compare the quality
of the merge result on the experimental tests for k = 1, ..., n. Besides, the
cohesion and coupling [2] of the created clusters will be analyzed.
E4. A use case testing will investigate the feasibility of which requirements are
worth being considered (RQ2-1 ) and to what extent they can be satisfied
(RQ2-2 ).
E5. The quality of the merge result regarding RQ2-3 can be evaluated in three
scenarios: (i) Measuring the integrity of the merged ontology with the merge
quality criteria, namely, compactness, completeness, and minimality [9]. In
addition, we are investigating to recast these measures in a broad range of
systematic criteria in our evaluator tool. (ii) Comparing our result with the
mentioned state-of-the-art approaches. (iii) Comparison with human experts
results on the part of our datasets.
E6. The consistency of the merge result can be evaluated by measuring the
fulfillment of the customized merge requirements with either a reasoner or
an expert (RQ2-4 ).
E7. We demonstrate the method’s scalability by illustrating the performance
test results on the set of real-life ontologies from BioPortal repository 3 and
OAEI datasets. The first one currently contains more than 700 biomedical
ontologies with thousand of classes, and the second one includes several
domains such as biodiversity and ecology, anatomy and conference domains.
Here, the runtime performance will be evaluated based on the number of
ontologies versus the time required for merge operations.
2
http://owlapi.sourceforge.net
3
https://bioportal.bioontology.org
Fig. 2: Merger Fig. 3: Evaluator
5 Preliminary Results
The first conceptual ontology-based data integration workflow has already been
represented in [3]. Additionally, we investigated the role of mapping in the merge
process [5]. Moreover, we developed a highly accurate similarity method 4 by
applying Information Content as an optimization problem [4]. We will revise it
to be extended in our similarity function. Finally, the first version of our tools,
merger (Fig. 2) and evaluator (Fig. 3) are under our development to be published
online.
6 Discussion
Ontology merging is often a necessity in applications of the Semantic Web. To
this end, our aim is to provide a holistic multiple ontologies merging, namely
CoMerger, to satisfy the scalability issue. The efficient processing will be held
by breaking the n ontologies processing into the k clusters merging, with a
minor overhead of the clustering process. Each component has a high effect on
the quality of the final merge result, therefore, the difficulty of this research
would be carefully fine-tune the correctness of each sub-function. Besides, in the
aspect of knowledge integration, the possibility of ambiguous knowledge being
introduced in the final merged ontology will be another obstacle in this research,
which bring us to deal with the reasoning from ambiguous knowledge challenge
[19]. The future works of this research can be extended to the merging data
in the schema-level on the Linked Open Data (LOD) scenarios, also utilizing
parallel techniques in our framework.
4
http://simbio.uni-jena.de
Acknowledgments
The author would like to thank Prof. Birgitta König-Ries and Dr. Alsayed
Algergawy for their valuable supervising. The author is supported by a scholarship
from German Academic Exchange Service (DAAD).
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