FCAMapX is an automated ontology matching system based on Formal Concept Analysis, a mathematical model for analyzing individuals and structuring concepts. FCAMapX has succeeded in participating in three tracks of 2018 OAEI this year, including the Conference track, Anatomy, and Large Biomedical Ontologies. Based on our 2016 OAEI submission system FCA-Map which failed some large tasks within a designated time, we pursue improvements in e ciency and precision in FCAMapX. Concretely, we optimize the data structures for saving memory space and implement a more e cient algorithm for computing formal concept lattices. To favor precision, we tighten the condition for identifying lexical mappings and strengthen the structural validation to retrieve negative evidence for matches identi ed lexically and structurally. As a result, the running time for all the tasks has become less than an hour in our experimental setting; and in a majority of the cases, the precision and F-measure are both improved while the recall is lowered. Additionally, in comparison with other OAEI participants, FCAMapX has achieved the best or the second best F-measure and recall in most large biomedical ontology matching tasks.
Based on our 2016 OAEI participant system FCA-Map [
In OAEI 2016, we submitted FCA-Map, a novel system based on Formal
Concept Analysis to identify and validate mappings across ontologies, including
oneto-one mappings and complex mappings. FCA-Map incrementally generates a
total of three types of formal contexts and extracts mappings from the lattices
derived. First, the token-based formal context describes how class names, labels,
and synonyms share lexical tokens, leading to lexical mappings (anchors) across
ontologies. Second, the relation-based formal context describes how classes are
in taxonomic, partonomic and disjoint relationships with the anchors, leading
to positive and negative structural evidence for validating the lexical
matching. Third, the positive relation-based context can be used to discover structural
mappings. The 2016 OAEI evaluation in the Anatomy, the Large Biomedical
Ontologies, and the Disease and Phenotype track demonstrates the e ectiveness of
FCA-Map and its competitiveness with the top-ranked systems. For
SNOMEDNCI(whole), the largest ontology matching task in OAEI, FCA-Map ranks rst
for recall and second for F-measure; ranks second for both F-measures of
FMANCI and FMA-SNOMED, and obtains the best F-measures for most Disease
and Phenotype tasks [
In order to improve the e ciency, we optimize the data structures for saving
memory space and implement a more e cient algorithm Hermes [
Similarly to our previous edition, our SEALS submission included precomputed
word variants originated from UMLS[
SEALS wrapped version of FCAMapX for OAEI 2018 is available at https: //drive.google.com/open?id=1-0upxrcPbu5OVJAJn-DtTOUMOh3QDriM.
The results obtained by FCAMapX for OAEI 2018 are available at https:// drive.google.com/open?id=1DzRD_90O3YwoGpW5FJL9vSy_f1Ia0YZo 2
In this section, we present our evaluation results obtained by running FCAMapX over the tracks of Anatomy, Conference, and Large Biomedical Ontologies. Tests were performed using a desktop computer with 16 GB of RAM and Intel R CoreTM i7-8700 CPU @ 3.20GHz. 2.1
The anatomy track consists of the Adult Mouse Anatomy (2744 classes) and a
fragment of the NCI Thesaurus (3304 classes) for describing the human anatomy.
Compared with our 2016 version, FCAMapX has improved the precision from
0.932 to 0.941, whereas the recall is decreased from 0.837 to 0.791, leading to a
drop of the F-Measure from 0.882 to 0.860, as shown in Table 1).
The Conference 2018 Track contains 16 ontologies describing the domain of
conference organizations. These ontologies are of smaller scale with limited classes
and semantic relations, for which our approach can be ine ective, as analyzed
in [
Task
cmt-conference
cmt-confOf
cmt-edas
cmt-ekaw
cmt-iasted
cmt-sigkdd
conference-confOf
confenrece-edas
conference-ekaw
conference-iasted
conference-sigkdd
confOf-edas
confOf-ekaw
confOf-iasted
conOf-sigkdd
edas-ekaw
edas-iasted
edas-sigkdd
ekaw-iasted
ekaw-sigkdd
iasted-sigkdd
the F-measure while the recall values are lowered. Take FMA-SNOMED (whole)
for example, the precision is 1.8 times of the 2016 version and the F-measure
1.4 times. More importantly, in our own experimental setting, FCAMapX
nished all tasks in the Large Biomedical track within 2 hours as required by 2016
OAEI, whereas our 2016 system failed the three Whole tasks. For the largest task
SNOMED-NCI (whole), our previous version ran about 13 hours as reported in
[
As reported by OAEI 3, out of the six tasks in the track, FCAMapX ranks rst for three and second for two tasks in terms of recall; and for F-measure, FCAMapX ranks rst for two and second for three tasks. 3
This is the second time that we participate in the OAEI campaign with our Formal Concept Analysis based systems. The main goal is to improve the e ciency in regard to our 2016 edition which failed to nish within the designated time for three tasks in Large Biomedical Ontologies track. This has been accomplished by FCAMapX. At the same time, strengthening the structural validation of mappings has yielded higher precisions which can lead to better F-measure values. 3.1
FCAMapX has succeeded in participating in three tracks this year, including the Conference track, Anatomy, and Large Biomedical Ontologies. The running time for all the tasks has become less than an hour now in our experimental setting. In a majority of the cases, the precision and F-measure are both improved while the recall is lowered. That FCAMapX performs unsatisfactorily for FMA-NCI (small) in comparison with our 2016 system deserves a further explanation. 3.2
We intended to run FCAMapX on the Disease and Phenotype track where our
previous 2016 system performs competitively [
Unfortunately, FCAMapX failed this track with errors as reported by the OAEI evaluation. This indicates that the quality of the system shall be improved. 3 http://www.cs.ox.ac.uk/isg/projects/SEALS/oaei/2018/results/ 4 http://oaei.ontologymatching.org/2018/phenotype/
With our participating experience this year, we nd that OAEI is well organized in an e cient way and organizers helpful. Various tracks have di erent levels of di culty, which is challenging and appealing, and the SEALS platform is very convenient to use. 4
In this paper, we present FCAMapX as an improved version of our 2016 OAEI system FCA-Map. The improvement mainly lies in the e ciency, as illustrated by the dramatic drop of running times, for instance from 13 to 1 hour for the largest OAEI task. The second improvement is on the mapping precision which normally causes the F-measure to rise. Compared with other OAEI participants, FCAMapX has achieved the best or the second best F-measure and recall in ve out of the six large biomedical ontology matching tasks. Despite these, our system still has a long way to go in terms of covering all OAEI tracks, especially those instance matching tasks for which the Formal Concept Analysis formalism has a potential to prevail with its capability of clustering commonalities among individuals.
This work has been supported by the National Key Research and Development Program of China under grant 2016YFB1000902, and the Natural Science Foundation of China grant 61621003.