Ontology Alignment Evaluation Initiative - OAEI 2019 Campaign

Results for OAEI 2019 - Anatomy track

Generated alignments

We have collected all generated alignments and make them available in a zip-file via the following link. These alignments are the raw results that the following report is based on.

>>> download raw results

Experimental setting

We conducted experiments by executing each system in its standard setting and we compare precision, recall, F-measure and recall+. The measure recall+ indicates the amount of detected non-trivial correspondences. The matched entities in a non-trivial correspondence do not have the same normalized label. The approach that generates only trivial correspondences is depicted as baseline StringEquiv in the following section.

For SANOM, we took the results from the Hobbit platform in the evaluation. For the other systems, we ran on a server with 3.46 GHz (6 cores) and 8GB RAM allocated to each matching system. Further, we used the SEALS client to execute our evaluation. However, we slightly changed the way how precision and recall are computed, i.e., the results generated by the SEALS client vary in some cases by 0.5% compared to the results presented below. In particular, we removed trivial correspondences in the oboInOwlnamespace like

http://...oboInOwl#Synonym = http://...oboInOwl#Synonym

as well as correspondences expressing relations different from equivalence. Using the Pellet reasoner we also checked whether the generated alignment is coherent, i.e., there are no unsatisfiable concepts when the ontologies are merged with the alignment.


In the following, we analyze all participating systems that could generate an alignment. The listing comprises of 12 entries. LogMap participated with different versions, namely LogMap, LogMapBio, and a lightweight version LogMapLite that uses only some core components as previous years. There are two systems which are Wiktionary and AGM participating in the anatomy track this year for the first time. DOME participates in the anatomy track this year for the second time. Meanwhile, three systems participated for the third time. They are SAMOM, POMAP++ (POMap in 2017) and FCAMap-KG (FCA_MAP in 2016 and FCAMapX in 2018). The previous time ALIN, AML, LogMap(all versions) and Lily participated in the anatomy track was last year. LogMap is a constant participant since 2011. AML joined the track in 2013. ALIN and Lily joined in 2016. For more details, we refer the reader to the papers presenting the systems. Thus, this year we have 10 different systems (not counting different versions) which generated an alignment.

This year 5 out of 12 systems were able to achieve the alignment task in less than 100 seconds. These are LogMapLite, DOME, FCAMap-KG, LogMap and AML. In 2018 and 2017, there were 6 out of 12 systems and 5 out of 11 systems respectively which generated an alignment in this time frame. Similarly to the last 7 years, LogMapLite has the shortest run time. Depending on the specific version of the systems, they require between 19 and 76 seconds to match the ontologies. The table shows that there is no correlation between the required time for running and the quality of the generated alignment in specific metric. This result has also been observed in previous OAEI campaigns.

The table also shows the results for F-measure, recall+ and the size of the alignments. Regarding F-measure, the top 3 ranked systems are AML, LogMapBio, and POMAP++ which are same as last year's result. Among these, AML achieved the highest F-measure (0.943). All of the long-term participants in the track showed comparable results in terms of F-measure to their results last year and at least as good as the results of the best systems in OAEI 2007-2010. ALIN had a notable increase in F-measure from 0.506 in 2017 to 0.758 in last year. In this year, ALIN also has an increase to 0.813. Regarding recall+, AML, LogMap, LogMapLite, POMAP++, SANOM show similar results as earlier. LogMapBio had an increase from 0.733 in 2017 to 0.756 in 2018, further 0.801 in 2019. ALIN has a notable increase in recall+ from 0 in 2018 to 0.365 in 2019 which means the system is able to generate The new systems and systems with new versions in 2019 do not show high results for recall+. Regarding the number of correspondences, some long-term participants computed a similar number of correspondences as last year. AML and LogMap generated the same number of correspondences, LogMapBio generated 57 more correspondences. As notable last year, ALIN generated 412 more correspondences because of the withdrawal of additional criteria for the automatic classification of mappings at the beginning of its execution. Further, Alin generated 158 more correspondences.

This year 10 out of 12 systems achieved an F-measure higher than the baseline which is based on (normalized) string equivalence (StringEquiv in the table). Among these 10 systems, Wiktionary is a new participant.

This year four systems produced coherent alignments which are ALIN, AML, LogMap and LogMapBio.


The number of participating systems varies between the years. In 2019, there are two less participants than in 2018, but one more than that in 2017. As noted earlier there are newly-joined systems as well as long-term participants.

Same as the last year, AML sets the top result for the anatomy track with respect to the F-measure. Following AML, LogMapBio and POMAP++ perform same as last year.


This track is organized by Huanyu Li and Patrick Lambrix. If you have any problems working with the ontologies, any questions related to tool wrapping, or any suggestions related to the anatomy track, feel free to write an email to oaei-anatomy [at] ida [.] liu [.] se.