=Paper=
{{Paper
|id=Vol-1766/oaei16_paper6
|storemode=property
|title=DKP-AOM: results for OAEI 2016
|pdfUrl=https://ceur-ws.org/Vol-1766/oaei16_paper6.pdf
|volume=Vol-1766
|authors=Muhammad Fahad
|dblpUrl=https://dblp.org/rec/conf/semweb/Fahad16
}}
==DKP-AOM: results for OAEI 2016==
https://ceur-ws.org/Vol-1766/oaei16_paper6.pdf
DKP-AOM: results for OAEI 2016
Muhammad Fahad
Centre Scientifique et Technique du Bâtiment (CSTB),
290 Route des Lucioles, Sophia-Antipolis, FRANCE
firstname.lastname@cstb.fr
Abstract
In this paper, we present the results obtained by our DKP-AOM system within the OAEI 2016 campaign. DKP-
AOM is an ontology merging tool designed to merge heterogeneous ontologies. In OAEI, we have participated
with its ontology mapping component which serves as a basic module capable of matching large scale ontologies
before their merging. This is our second successful participation in the OAEI 2016 campaign and first in the
Process Model Matching track of OAEI. DKP-AOM is participating with two versions (DKP-AOM and DKP-
AOM_lite). The reference alignments contain correspondences between instances of the class task as well as
some correspondences between events. In the lite version of DKP, it does not match classes with the events, as it
is of natural semantics that events should not be mapped on classes and vice versa. Therefore, we designed our
system with two variants. But, our DKP-AOM system identifies cases where tasks are matched on events (where
it makes sense). This is the only difference between two variant, hence for other tracks these two variants produce
the same results. In this track, we can see its competitive results in the evaluation initiative among other reputed
systems. Finally, we discuss some future work towards the development of DKP-AOM.
Keywords: Ontology matching, Ontology merging, disjoint knowledge, inconsistency, incompleteness,
inconciseness, validation of mappings, verification of merged ontology
1 Presentation of the System
Ontology merging is a process of building a new ontology from two or more existing ontologies
with overlapping parts. The merged ontology can be either virtual or physical, but must be consistent,
coherent and include all the information from the source ontologies [1]. Ontology merging is based on
two primary steps. Firstly, the source ontologies are looked-up for correspondences between them.
Secondly, duplicate-free and conflict-free union of source ontologies is achieved based on the
established correspondences [2]. The first part mainly comes under the ontology matching, whereas
the second part targets to achieve the merged ontology based on the results of the first part, i.e.,
mappings between source ontologies. To produce accurate merged ontology, there should be some
mechanism to avoid erroneous intermediate mappings and also to merge them in such a way that
produces consistent, complete and coherent merged ontology. There are many hurdles that come
across in the generation of desired merged output. Firstly, ontological errors and design anomalies that
can occur in the source ontologies detract from reasoning and inference mechanisms, and create
bottleneck in their integration tasks [3]. In addition, conceptualization of domain, explication and
modeling of knowledge over ontologies and semantic heterogeneities make their integration more
difficult [4]. Secondly, even if the individual ontologies are free from errors, some of the identified
mappings lead towards the erroneous situations producing several types of errors in the merged
ontology [5]. For building an effective ontology merging algorithm, it is essential to incorporate
ontological error checking during the validation of ontology mapping process and the verification of
merged ontology to attain the accuracy of resultant output.
In order to meet the above mentioned challenges for the ontology merging research, we proposed
semi-automatic DKP-OM system implemented in Jena framework for the merging of heterogeneous
ontologies with the human user expert [6]. Later, we released a fully Automatic Ontology Merging
(AOM) system named DKP-AOM implemented in OWLAPI 3 [7]. The name DKP comes from the
concept of performing Disjoint Knowledge Analysis (DKA) and Disjoint Knowledge Preservation
(DKP) during the merging process. Disjoint Knowledge Analysis plays a vital role in controlling the
search space for finding similarities between source ontologies. Look-up within disjoint partitions of
source ontologies significantly reduces the time complexity of the mapping phase. Disjoint
Knowledge Preservation in the merged ontology helps to preserve disjoint axioms in the sub-
hierarchies of merged ontology to avoid incompleteness in the resultant merged ontology. In this way,
it also pin-points different conflicts between source ontologies based on disjoint axioms in the source
ontologies and detects inconsistent mappings. Computed mappings that lead in many cases to a large
number of unsatisfiable classes are eliminated so the resultant merged ontology should not suffer from
inconsistencies. The next sub-sections provide more details about DKP-AOM and then discuss our
results of OAEI participation.
1.1 Adaptations made for the evaluation
As you read above, DKP is an automatically merging system. Therefore it was developed based on
user GUIs such as source ontology trees for display, visual alignments between ontologies, merged
ontology tree, etc. The original version of DKP has changed and these visual components are removed
so that it can participate under the seals platform. However, still it needs proper clean-up to improve
its runtime for the future OAEI participations.
1.2 Link to the system
Various versions of my system can be found at my personal site: http://sites.google.com/site/mhdfahad
under plugins tab. The mapping system is separated from the merging system, and can be downloaded
according to needs. For the merging of ontologies, use the same command of seals platform with –o
following three paths, two for source ontologies and one for the output merged ontology. As a result of
this command, a list of ontology mappings and a resultant merged ontology are produced.
2 Results
In order to show the efficiency and effectiveness of our system, this year we participated in
Process Modeling track. The results are very encouraging provided by the OAEI 2016 campaign as
our system is acceptable and comparable with other participants, and are discussed in the following
subsections.
2.1 Process Model Matching
This track concerns with the task of matching process models, originally represented in BPML.
These models have been converted to an ontological representation. The resulting matching task is a
special case of an interesting instance matching problem. Organizers have converted the BPMN
representation of the process models to a set of assertions (ABox) using the vocabulary defined in the
BPMN 2.0 ontology (TBox). For that reason the resulting matching task is an instance matching
task where each ABox is described by the same TBox. By offering this track, OAEI hope to gain
insights in how far ontology matching systems are capable of solving the more specific problem of
matching process models. The collection consists of 9 models ("Cologne", "Frankfurt", "FU_Berlin",
"Hohenheim", "IIS_Erlangen", "Muenster", "Potsdam", "TU_Munich", "Wuerzburg"), for each pair
exists an alignment in the gold standard. However, there is only an alignment named "Cologne-
Frankfurt.rdf" and no alignment "Frankfurt-Cologne.rdf". This is the first time DKP-AOM is
participating in this track.
We have participated with two versions of DKP with some differences. The reference alignments
contain correspondences between instances of the class task as well as some correspondences between
events. In the lite version, we have not matched classes with the events, as it is of natural semantics
that events should not be mapped on classes and vice versa. Therefore, we separated our system with
two variants. Our DKP-AOM system identifies some cases where tasks are matched on events (where
it makes sense). But in its lite version, we did not add this functionality. For an example, consider a
scenario where:
BPMN1: Task (Receive Rejection)
BPMN2: Event (Rejected)
Although in real world for someone, it has the impression that the "Rejected-Event" has within the
workflow the same semantics as the "Receive rejection Task". In these cases, its about getting
informed, receiving a message. That is why in this case an event and a task are used to model the same
real world event/task. Indeed its even hard to say, if this is an event or a task. This leads to have two
variant of DKP in the participation. The following table 1 shows the comparative analysis of DKP-
AOM with other systems participated in the process matching track.
Table 1. Comparative analysis of DKP-AOM with other systems [10]
Participants of the Process Model Matching Contest are depicted in grey font, while OAEI
participants are shown in black font [for details see ref 10]. The OAEI participants are ranked on
position 1, 8/9 and 11 with an overall number of 16 systems listed in the table. In the probabilistic
evaluation, however, the OAEI participants (AML, LogMap, DKP, DKP*) gain position 2, 3, 9 and
10, respectively. Our system DKP generates mediocre results, this indicates that the progress made in
ontology matching has also a positive impact on other related matching problems, like it is the case for
process model matching. While it might require to reconfigure, adapt, and extend some parts of the
ontology matching systems, such a system seems to offer a good starting point which can be turned
with a reasonable amount of work into a good process matching tool.
Table 2 presents the results obtained by DKP-AOM on the PM track of OAEI campaign 2016.
Test Case ID Precision Recall F-measure Test Case ID Precision Recall F-measure
Cologne-FU_Berlin 1 1 1 Frankfurt-Potsdam 0.4 1 0.571
Cologne-Frankfurt 0.889 1 0.941 Frankfurt-TU_Munich 0.857 1 0.923
Cologne-Hohenheim 0 0 0 Frankfurt-Wuerzburg 0.5 0.333 0.4
Cologne-IIS_Erlangen 0.5 1 0.667 Hohenheim-IIS_Erlangen 0.5 0.2 0.286
Cologne-Muenster 0.5 1 0.667 Hohenheim-Muenster 1 0.375 0.545
Cologne-Potsdam 0.5 1 0.667 Hohenheim-Potsdam 0 0 0
Cologne-TU_Munich 0.692 1 0.818 Hohenheim-TU_Munich 0 0 0
Cologne-Wuerzburg 0.5 0.333 0.4 Hohenheim-Wuerzburg 1 0.25 0.4
FU_Berlin-Hohenheim 0 0 0 IIS_Erlangen-Muenster 0.714 0.385 0.5
FU_Berlin-IIS_Erlangen 1 0.857 0.923 IIS_Erlangen-Potsdam 0.857 0.857 0.857
FU_Berlin-Muenster 1 0.5 0.667 IIS_Erlangen-TU_Munich 0.5 0.222 0.307
FU_Berlin-Potsdam 1 0.929 0.963 IIS_Erlangen-Wuerzburg 1 0.333 0.5
FU_Berlin-TU_Munich 0.5 0.333 0.4 Muenster-Potsdam 0.714 0.455 0.556
FU_Berlin-Wuerzburg 0.667 0.333 0.444 Muenster-TU_Munich 0.5 0.222 0.307
Frankfurt-FU_Berlin 0.4 1 0.571 Muenster-Wuerzburg 1 0.333 0.5
Frankfurt-Hohenheim 0 0 0 Potsdam-TU_Munich 0.5 0.333 0.4
Frankfurt-IIS_Erlangen 0.4 1 0.571 Potsdam-Wuerzburg 0.667 0.333 0.444
Frankfurt-Muenster 0.2 1 0.333 TU_Munich-Wuerzburg 0 0 0
Global 0.718 0.547 0.621
Table 2. presents the results obtained by running DKP-AOM
2.2 Conference
The goal of conference track is to find alignments among 16 ontologies relatively smaller in size
(between 14 and 140 entities) but rich in semantic heterogeneities about the conference organization
domain. As a result, Alignments are evaluated automatically against reference alignments. Therefore,
it is very interesting to measure the Precision, Recall and F-measure of our system and also does a
comparison between existing systems to see their performance on real world datasets. Table 2 presents
the results obtained by running DKP-AOM on the Conference track of OAEI campaign 2016. Our
system DKP-AOM has produced very competitive results among top ranked systems. Our precision
measure is significantly high, recall is good giving comparable F-measure value to depict a real effort
towards detecting heterogeneities for the goal of ontology matching.
Matcher Runtime Precision F-Measure Recall
DKP-AOM 9913 0.844 0.626 0.498
Table 2. DKP-AOM results on conference track ontologies
3 Conclusion and Future Directions
The participation of DKP-AOM in OAEI 2016 is a success in the Process Model Matching track. Our
aim was to implement BPMN model matching; therefore, we have only implemented processing
model strategy in our last version of DKP-AOM that participated in 2015. Therefore, it produces
(more or less) the same output in the evaluation tracks as OAEI 2015, hence we haven’t discuss output
on other tracks. We can see DKP-AOM has produced competitive results in the evaluation Process Model
initiative among other reputed systems.
References
1. Bruijn, J.d., Ehrig, M., Feier, C., Martín-Recuerda, F., Scharffe, F., and Weiten., M., Ontology mediation, merging and
aligning. In Semantic Web Technologies. Wiley 2006
2. Euzenat, J., and Shvaiko, P., Ontology Matching. Springer, 2007, ISBN 978-3-540-49611-3.
3. Fahad, M., Qadir, M.A., Noshairwan, M.W., Ontological Errors - Inconsistency, Incompleteness and Redundancy. In
Proceedings of 10th Intl Conference on Enterprise Information Systems, pp. 253-285, 2008, Spain, Springer,
4. Klein, M., (2001): Combining and relating ontologies: an analysis of problems and solution. In Proc. of Workshop on
Ontologies and Information Sharing (IJCAI), pp. 53-62. Seattle, USA (2001)
5. Fahad, M., and Qadir, M.A., A Framework for Ontology Evaluation, 16th ICCS Supplement Proceeding, vol. 354,
2008, France, pp.149-158.
6. Fahad, M., Qadir, M.A., Noshairwan, W., Iftakhir, N., DKP-OM: A Semantic based Ontology Merger, Proceedings of
3rd International Conference on Semantic Technologies (I-Semantics 07) Graz, Austria, 2007, Pages 313-322
7. Fahad, M., Moalla, N., Bouras, A., Detection and Resolution of Semantic Inconsistency and Redundancy in an
Automatic Ontology Merging System, Journal of Intelligent Information System (JIIS), Vol. 39(2) pp. 535-557,
29/4/2012, DOI 10.1007/s10844-012-0202-y
8. Fahad, M., Moalla, N., Bouras, A., Qadir, M.A., Farukh, M., Disjoint Knowledge Analysis and Preservation in
Ontology Merging Process, proceedings of 5th International Conference on Software Engineering Advances
(ICSEA’10), IEEE CS, August 22-27, 2010 - Nice, France.
9. Fahad, M., Moalla, N., Bouras, A., Towards ensuring Satisfiability of Merged Ontology, International conference on
computational science, ICCS 2011, Procedia Computer Science 4 (2011), pp. 2216–222, 1-3 june, 2011
10. Process Matching Results: http://web.informatik.uni-mannheim.de/oaei/pm16/results.html
11. Fahad, M., Merging of axiomatic definitions of concepts in the complex OWL ontologies. Artificial Intelligence
Review, (2016) doi:10.1007/s10462-016-9479-5 pp 1–35