FCAMapX results for OAEI 2018

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 efficiency and precision in FCAMapX. Concretely, we optimize the data structures for saving memory space and implement a more efficient 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 identified 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. 1 Presentation of the system Based on our 2016 OAEI participant system FCA-Map [1,3], this edition, called FCAMapX, pursues to improve the efficiency and precision. 1.1 State, purpose, general statement 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 2 Guowei Chen and Songmao Zhang 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 effectiveness of FCA-Map and its competitiveness with the top-ranked systems. For SNOMEDNCI(whole), the largest ontology matching task in OAEI, FCA-Map ranks first 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 [3]. On the other hand, FCA-Map suffers from long running times due to the high complexity of deriving formal concept lattice in the Formal Concept Analysis formalism, which is a PSPACE-complete problem. Moreover, the performance of FCA-Map in terms of precision is relatively poorer than of recall and F-measure. We intend to address these two issues in the 2018 edition FCAMapX. 1.2 Specific techniques used In order to improve the efficiency, we optimize the data structures for saving memory space and implement a more efficient algorithm Hermes [4] for computing formal concept lattice and Galois sub-hierarchy. The Hermes algorithm has an efficient running time of O(min{nm, n}), where n is the number of objects or attributes, m the size of formal context, and n the time required to perform matrix multiplication (currently α = 2.376). To improve the precision, we tighten the condition for identifying lexical mappings from the token-based lattice computed in the first step. Moreover, the second step for structural validation and the third step for structural mapping are swapped so that the positive and negative evidence can be retrieved for all mappings identified, lexically and structurally. This can favor precision as mappings with negative evidence are discarded. 1.3 Adaptations made for the evaluation Similarly to our previous edition, our SEALS submission included precomputed word variants originated from UMLS[5] for mapping biomedical ontologies. Moreover, in order to augment the performance of FCAMapX in mapping ontologies in general purpose domains like those of the Conference track, we used the synsets of WordNet[6] in the first step for identifying synonymous terms. Property names in the Conference ontologies are also taken into account when constructing the token-based formal context for lexical mapping. 1.4 Link to the system and parameters file SEALS wrapped version of FCAMapX for OAEI 2018 is available at https: //drive.google. com/open? id=1-0upxrcPbu5OVJAJn-DtTOUMOh3QDriM. FCAMapX results for OAEI 2018 3 1.5 Link to the set of provided alignments The results obtained by FCAMapX for OAEI 2018 are available at https:// drive.google. com/open? id=1DzRD_90O3YwoGpW5FJL9vSy_f1Ia0YZo