The YAM-BIO ontology alignment system is an extension of YAM++ but dedicated to aligning biomedical ontologies. YAM++ has successfully participated in several editions of the Ontology Alignment Evaluation Initiative (OAEI) between 2011 and 2013, but this is the rst participation of YAM-BIO. The biomedical extension includes a new component that uses existing mappings between multiple biomedical ontologies as background knowledge. In this short system paper, we present YAM-BIO's work ow and the results obtained in the Anatomy and Large Biomedical Ontologies tracks of the OAEI 2017 campaign.
1.1
YAM-BIO may be seen as an extension of YAM++ [
Over last OAEI campaigns, state-of-the-art systems such as AML [
As illustrated in Fig. 1, YAM-BIO's work ow contains three main steps: First, to compute direct matching between source and target ontologies using YAM++. Second, to compose relevant existing mappings in the background knowledge for concepts not aligned during rst step. Third, to compute union of the alignments produced by the two previous steps.
Direct matching with YAM++: Annotations (labels, comments, etc.)
and structures of source and target ontologies are indexed as well as the context
of each entity that may be a concept or a property. Then, candidate mappings
with a low annotation similarity are pre- ltered. Other advanced lexical and
structural similarity measures are applied on the remaining candidate mappings,
before updating their similarity scores using the structure information of source
and target ontologies. Finally, a threshold is dynamically computed to select the
most relevant mapping candidates. For more details on each steps of the
execution of YAM++, readers may refer to [
Indirect matching and union: During this step YAM-BIO nds mappings for the concepts that have not been matched during direct matching with YAM++. First, background knowledge existing mappings are loaded in a list of lists noted A as follows: 1. Identi ers of all concepts in the background knowledge are added to A. The identi er of a given concept is the last part of its URI, for example the identi er of the concept that has the URI http://mouse.owl#MA 0000031 is MA 0000031. 2. Each element x of A points to a list that contains identi ers of all concepts matched to x in the background knowledge.
Then, for each source concept y that is not matched yet, YAM-BIO checks if y's identi er exists in A. If yes, YAM-BIO gets the corresponding list {pointed by y{ and for each element of this list, YAM-BIO veri es if itself points to a list that contains a concept identi er from the target ontology. If so, YAM-BIO derives a new mapping and adds it to the alignment produced previously by the direct matching. 1.3
The existing mappings used as background knowledge have been extracted from
Uberon [
In addition, concept identi ers of the ontologies provided for the Largebio
track are not the original ones, but have been replaced by their standardized
preferred labels. For this reason, we have used the NCBO BioPortal's REST
API [
YAM++ has now a publicly accessible online prototype version [
ontology
mappings
as well as the background knowledge le are available at the following link: https://goo.gl/zNznNz
The Anatomy track consists of nding an alignment between the Adult Mouse
Anatomy [
YAM-BIO scored second position in the Anatomy track and scored rst or second also in the Largebio track (except Task 5). As expected, using existing mappings as background knowledge has improved YAM++ results in terms of recall and consequently F-measure. Mapping compositions extracted from Uberon allowed YAM-BIO to discover non trivial mappings, speci cally in Anatomy track and in Task 1 and Task 2 of Largebio track. Similarly, the composition of mappings extracted from DOID allowed to increase the recall of Task 5 and Task 6. However, the incoherence analysis shows that YAM-BIO returns some incoherent mappings. This may be explained by the fact that the mappings derived using background knowledge have been added to the nal alignment without any semantic veri cation.
In our current system, mappings derived using background knowledge are
not post- ltered and semantically veri ed as in YAM++. A simple union of the
direct and indirect alignments is performed to obtain the nal alignment. In the
future, our goal would be to integrate the use of background knowledge directly
inside YAM++'s internal architecture which, we believe, will improve coherence
of the nal results. More speci cally, we will implement the approach proposed
in [
In addition, we are aware of the importance of the dynamic selection of
ontologies to use as background knowledge [
When possible, we think it would be interesting to publish participants results with and without use of specialized background knowledge. On one hand, this will allow to better evaluate the in uence of background knowledge in matching quality and running time. On the other hand, this will allow a fair comparison with systems that do not use background knowledge.
Some components are common in all ontology matching system architectures; others do not always exist |such as background knowledge selection or semantic veri cation. This makes the comparison of running time executions particularly cumbersome and not always fair. According to us, it would be more appropriate to evaluate execution times for each separate component. For example, YAMBIO used a prede ned background knowledge while LogMapBio made a dynamic selection from an online repository necessarily taking additional time. Splitting running time by components will also help the community to identify less e cient components to improve them, and most e cient ones to reuse them. 4
In 2017 YAM-BIO participated in two tracks: Anatomy and LargeBio. The results obtained in those tracks are very close to top ranked state-of-the-art systems, thanks to di erent content matching techniques implemented in YAM++ and to the use of background knowledge. Due to the high heterogeneity of ontologies, we believe that an advanced generic (i.e., not restricted to biomedicine) module that selects and uses background knowledge should be implemented in the internal architecture of YAM++ to improve its results. In the future, we will work on such a module and hopefully participate in di erent OAEI tracks. 5
This work was done during a LIRMM-ESI collaboration within the Semantic Indexing of French biomedical Resources (grant ANR-12-JS02-01001) and PractikPharma (ANR-15-CE23-0028) projects that received funding from the French National Research Agency as well as by the European H2020 Marie SklodowskaCurie action (agreement No 701771), the University of Montpellier and the CNRS. The authors also acknowledge the Ei el Excellence Scholarship program.