We describe in this paper the XMap system and the results achieved during the 2018 edition of the Ontology Alignment Evaluation Initiative. XMap aims to tackle the issue of matching large scale ontologies by involving particular parallel matching on multiple cores or machines. Our strategies aim to provide a set of requirements that foster the using of a domain-specific thesaurus for the alignment of specialized ontologies.
stage to filter out the set of correspondences having a similarity value below a
given threshold. This strategy skips over the problem of the growing size and the
complexity of the user participation in the process alignment of large ontologies.
– Applying repair techniques from Applying Logical Constraints on Matching
Ontologies (ALCOMO) [
Our prototype leans on the architecture of a sequential/parallel composition. XMap uses various similarity measures of different categories such as string, linguistic, and structural based similarity measures, each contributing to some extent to the alignment results. At a glance, the mapping process of XMap is depicted in Figure 1. XMap receives as an input two source ontologies. The mappings discovered by the terminological level matcher are transferred to the structural level matcher in order to find new correspondences by analyzing the context of the entities in the taxonomy of ontologies. Afterwards, the combined result of the two basic matchers are aggregated by a weighted sum aggregation operator. For the final alignment method, the system uses the threshold method. Moreover, we manually define the filters threshold value to produce the final mappings. A fast repair method is applied so as to detect and remove the inconsistent ones.
In this section, we present the evaluation results obtained by running XMap under the SEALS client with Anatomy, Conference, Multifarm, Interactive matching evaluation, Large Biomedical Ontologies, Disease and Phenotype and Biodiversity and Ecology tracks.
Anatomy The Anatomy track consists of finding an alignment between the Adult Mouse Anatomy (2744 classes) and a part of the NCI Thesaurus (3304 classes) describing the human anatomy. XMap achieves a good F-Measure value of 89% in a reasonable amount of time (37 sec.) (cf., Table 1). Conference The Conference track uses a collection of 16 ontologies from the domain of academic conferences. Most ontologies were equipped with OWL-DL axioms of various types; this opens a useful way to test our semantic matchers. For each reference alignment, three evaluation modalities are applied : a) crisp reference alignments, b) the uncertain version of the reference alignment, c) logical reasoning.
As depicted in Table 2 and 3, XMap produces fairly consistent alignments when
matching the conference ontologies. Finally, XMap generated only one incoherent
alignment for the evaluation based on logical reasoning.
Multifarm This track is based on the translation of the OntoFarm collection of
ontologies into 9 different languages. XMap have low performance due to many internal
exceptions. The results are showed in Table 4.
Interactive matching evaluation The goal of this evaluation is to imitate interactive
alignment [
XMap uses various similarity measures to generate candidate mappings. It applies two thresholds to filter the candidate mappings: one for the mappings that are directly added to the final alignment and another for those that are presented to the user for validation. The latter threshold is selected to be high in order to minimize the number of requests and the rejected candidate mappings from the oracle; the requests are mainly about incorrect mappings. The mappings accepted by the user are moved to the final alignment. For the three years 2016, 2017 and 2018, XMap preserved roughly the same F-Measure value, and it benefits the least from the interaction with the oracle. Whereas, for the conference track, XMap has increases in precision, recall and F-measure. XMap’s measures differ with less than 0:2% from the non-interactive runs, and performance does not change at all with the increasing error rates. Large biomedical ontologies This track consists of finding alignments between the Foundational Model of Anatomy (FMA), SNOMED CT, and the National Cancer Institute Thesaurus (NCI). The results obtained by XMap (Evaluated without UMLS) are depicted by Table 5.
In general, we can conclude that XMap achieved a good precision/recall values. The high recall value can be explained by the fact that UMLS thesaurus contains definitions of highly technical medical terms.
Disease and Phenotype This track based on a real use case where it is required to find alignments between disease and phenotype ontologies. Specifically, the selected ontologies are the Human Phenotype Ontology (HPO), the Mammalian Phenotype Ontology (MP), the Human Disease Ontology (DOID), and the Orphanet and Rare Diseases Ontology (ORDO).
XMap achieved fair results according to the three evaluation (Silver standard, Manually generated mappings and Manual assessment of unique mappings). Biodiversity and Ecology This track aims finding the alignments between the Environment Ontology (ENVO) and the Semantic Web for Earth and Environment Technology Ontology (SWEET), and between the Flora Phenotype Ontology (FLOPO) and the Plant Trait Ontology (PTO). The results are showed in Table .
Comments on the results This is the 6th time that we participate in the OAEI campaign. The official results of OAEI 2018 show that XMap is competitive with other well-known ontology matching systems in all OAEI tracks. As a sixth participation, we found the OAEI procedure very convenient and the organizers very supportive. The OAEI test cases are various, and this leads to a comparison on different levels of difficulty, which is very interesting. We found that SEALS platform is a precious tool to compare the performance of our system with the others. 5
Generally, according to our results obtained during the compaing OAEI 2018, our system delivered good results comparatively to other well-known ontology matching systems. The used benchmark greatly helped to identify the power and weaknesses of the algorithm. used benchmark helped greatly identify the power and weaknesses of the algorithm. In addition, XMap showed the feasibility of our approach especially on large-scale biomedical ontologies which was a thriving challenge in ontology matching domain.