=Paper=
{{Paper
|id=Vol-2288/om2018_poster3
|storemode=property
|title=Partitioning and matching tuning of large biomedical ontologies
|pdfUrl=https://ceur-ws.org/Vol-2288/om2018_poster3.pdf
|volume=Vol-2288
|authors=Amir Laadhar,Faiza Ghozzi,Imen Megdiche,Franck Ravat,Teste Olivier
|dblpUrl=https://dblp.org/rec/conf/semweb/LaadharGMRT18
}}
==Partitioning and matching tuning of large biomedical ontologies==
https://ceur-ws.org/Vol-2288/om2018_poster3.pdf
Partitioning and Matching Tuning of Large
Biomedical Ontologies
Amir Laadhar1 , Faiza Ghozzi2 , Ryutaro Ichise3 , Imen Megdiche1 , Franck
Ravat1 , and Olivier Teste1
1
Toulouse University, IRIT (CNRS/UMR 5505), Toulouse, France
{firstname.lastname}@irit.fr
2
University of Sfax, MIRACL, Sfax, Tunisia
faiza.ghozzi@isims.usf.tn
3
National Institute of Informatics, Tokyo, Japan
ichise@nii.ac.jp
1 Introduction
Large biomedical ontologies such as SNOMED CT, NCI, and FMA are exten-
sively employed in the biomedical domain. These complex ontologies are based
on diverse modelling views and vocabularies. We define an approach that breaks
up a large ontology alignment problem into a set of smaller matching tasks.
We coupled this approach with an automated tuning process, which generates
the adequate thresholds of the available similarity measure for any biomedical
matching task. Experiments demonstrate that the coupling between ontology
partitioning and threshold tuning outperforms the existing approaches.
2 Partitioning and Matching Tuning of Biomedical
Ontologies
2.1 Architecture overview
In figure 1, we depict the different stages for ontologies partitioning and threshold
tuning. These stages are detailed in the following sections.
Fig. 1. Architecture Overview
2.2 Ontologies Partitioning
We employ the hierarchical agglomerative clustering technique to divide an on-
tology into a set of partitions. This method is based on the equation 1 to compute
the structural similarity between the entities of the input ontologies. This equa-
tion is inspired by Wu and Palmer [4] similarity measure. The partitioning of
every ontology results in a dendrogram. We cut each dendrogram automatically
in order to result in a set of partitions. We examine the output of all the possible
cuts until finding the first cut which do not result in any isolated partitions. Iso-
lated partitions are partitions containing only one entity. We identify the similar
partition-pairs through the set of exact matchings between the input ontologies.
Dist(ri , lca) × 2
StrcSim(ei,m , ei,n ) = (1)
Dist(ei,m , lca) + Dist(ei,n , lca) + Dist(ri , lca) × 2
2 Laadhar et al.
2.3 Threshold tuning
The available external knowledge sources represent mediator biomedical ontolo-
gies between the two input ontologies. We cross-search the input ontologies and
the mediating ontology in order to find synthetic reference alignments. We com-
pute the similarity score Sim between all the annotations of thegenerated align-
ments. These similarity scores are represented by: simScore = sim1 ,... ,simn .
The threshold Th value is deducted fromPsimsimScore using the Equation 2:
n
sim1 simi
Th = (2)
3 Experiments |simScore|
In Table 1, we compare our proposed partitioning approach to the currently
available partitioning strategies using two OAEI 2017 biomedical data sets: the
Anatomy task and the LargeBio small segments tasks.
Table 1. Anatomy track partitioning results
Precision F-Measure Recall Number of partitions
Proposed approach 0.945 0.883 0.829 57/57
SeeCOnt [3] 0.951 0.863 0.789 ND
Falcon [2] 0.964 0.730 0.591 139/119
Alsayed et al. [1] 0.975 0.753 0.613 84/80
We employed UBERON as an external biomedical knowledge for deriving
synthetic reference alignments. We use ISUB similarity measure to compute the
similarity scores between the derived mappings. In Table 2, we illustrate the
accuracy of the partitioning approach with the deduced thresholds.
Table 2. Accuracy and derived thresholds for Anatomy and LargeBio tracks
Precision F-Measure Recall Derived Threshold
Anatomy 0.945 0.883 0.829 0.91
FMA-NCI 0.957 0.870 0.789 0.69
FMA-SNOMED 0.860 0.674 0.554 0.75
SNOMED-NCI 0.911 0.697 0.564 0.85
4 Conclusion and Future Work
As future work, we intend to automate all the matching tuning process while
focusing on different type of heterogeneity applied over the partitions-pairs.
References
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