Tackling the challenges of matching biomedical ontologies

Abstract

BackgroundBiomedical ontologies pose several challenges to ontology matching due both to the complexity of the biomedical domain and to the characteristics of the ontologies themselves. The biomedical tracks in the Ontology Matching Evaluation Initiative (OAEI) have spurred the development of matching systems able to tackle these challenges, and benchmarked their general performance. In this study, we dissect the strategies employed by matching systems to tackle the challenges of matching biomedical ontologies and gauge the impact of the challenges themselves on matching performance, using the AgreementMakerLight (AML) system as the platform for this study.ResultsWe demonstrate that the linear complexity of the hash-based searching strategy implemented by most state-of-the-art ontology matching systems is essential for matching large biomedical ontologies efficiently. We show that accounting for all lexical annotations (e.g., labels and synonyms) in biomedical ontologies leads to a substantial improvement in F-measure over using only the primary name, and that accounting for the reliability of different types of annotations generally also leads to a marked improvement. Finally, we show that cross-references are a reliable source of information and that, when using biomedical ontologies as background knowledge, it is generally more reliable to use them as mediators than to perform lexical expansion.ConclusionsWe anticipate that translating traditional matching algorithms to the hash-based searching paradigm will be a critical direction for the future development of the field. Improving the evaluation carried out in the biomedical tracks of the OAEI will also be important, as without proper reference alignments there is only so much that can be ascertained about matching systems or strategies. Nevertheless, it is clear that, to tackle the various challenges posed by biomedical ontologies, ontology matching systems must be able to efficiently combine multiple strategies into a mature matching approach.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-017-0170-9) contains supplementary material, which is available to authorized users.

The Impact of Imbalanced Training Data on Local Matching Learning of Ontologies

“…Faria et al [8] identified the different challenges to align large biomedical ontologies. Ensuring good quality alignments while aligning these ontologies is challenging.…”

“…For instance, the class UBERON 0001275 ("pubis") of Uberon references the FMA class 16595 ("pubis") and NCI class C33423 ("pubic bone"). Therefore, the later entities construct a positive sample of a local training set [8]. Resampling of the Local Training Data: The training set is not balanced since the number of the negative samples M is higher than the number of positive samples N .…”

“…Ontology mapping becomes a challenging and time-consuming task due to the size and the heterogeneity of biomedical ontologies. In this domain, Faria et al [8] identified different challenges such as handling large ontologies or exploiting background knowledge. In addition to these problems, we highlight the importance of ensuring good matching quality while aligning large ontologies.…”

Interconnection of Biological Knowledge Using NikkajiRDF and Interlinking Ontology for Biological Concepts

“…Furthermore, by appropriately leveraging ontology mapping algorithms mentioned above for biomedical concepts, we would be able to discover new relations among biomedical concepts, such as those of equivalent and overlapping relations, which could not be identified using only string comparison techniques, such as the LOOM. For example, there is an ontology mapping system "AgreementMaker-Light (AML) [52]," which implements some matching algorithms: (1) "The Lex-icalMatcher" to find literal full name matches between the lexicon entries of two ontologies, (2) "The ThesaurusMatcher," to find literal full name matches involving synonyms inferred from an automatically generated thesaurus, and (3) "The XRefMatcher," which uses cross-reference information among data sources. In the AML's matching tasks using anatomy, phenotype, and disease datasets, they have demonstrated that not only the precision rate but also recall rate and F-measure were improved, simply by optimizing the algorithm parameters or combining some algorithms [52].…”

Matching biomedical ontologies through compact differential evolution algorithm

“…In recent years, various biomedical ontologies, such as FMA (Detwiler, Mejino, & Brinkley, 2016) and SNOMED-CT (Filice & Kahn, 2019), have been widely used in the life science domain (Faria et al, 2018). However, existing biomedical ontologies that cover overlapping domains are mostly developed independently, and the different ways of defining the same biomedical concept yield heterogeneous problems among biomedical ontologies, which hampers their inter-operability.…”