What’s in a ‘nym’? Synonyms in Biomedical Ontology Matching

Pesquita, Cátia; Faria, Daniel; Stroe, Cosmin; Santos, Emanuel; Cruz, Isabel F.; Couto, Francisco M.

doi:10.1007/978-3-642-41335-3_33

Cited by 12 publications

(10 citation statements)

References 18 publications

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“…This can be achieved through a process called ontology matching [ 2 ], whereby meaningful links are established between semantically related concepts. The matching of biomedical ontologies poses specific computational challenges due to their large size and vocabulary complexity [ 3 ], and also by their increasing semantic richness in the form of new kinds of relations between classes and complex axioms. These open challenges have attracted the interest of the community and spurred the inclusion of specific tracks dedicated to biomedical ontologies in the Ontology Alignment Evaluation Initiative [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Improving the interoperability of biomedical ontologies with compound alignments

Oliveira

Pesquita

2018

J Biomed Semant

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BackgroundOntologies are commonly used to annotate and help process life sciences data. Although their original goal is to facilitate integration and interoperability among heterogeneous data sources, when these sources are annotated with distinct ontologies, bridging this gap can be challenging. In the last decade, ontology matching systems have been evolving and are now capable of producing high-quality mappings for life sciences ontologies, usually limited to the equivalence between two ontologies. However, life sciences research is becoming increasingly transdisciplinary and integrative, fostering the need to develop matching strategies that are able to handle multiple ontologies and more complex relations between their concepts.ResultsWe have developed ontology matching algorithms that are able to find compound mappings between multiple biomedical ontologies, in the form of ternary mappings, finding for instance that “aortic valve stenosis”(HP:0001650) is equivalent to the intersection between “aortic valve”(FMA:7236) and “constricted” (PATO:0001847). The algorithms take advantage of search space filtering based on partial mappings between ontology pairs, to be able to handle the increased computational demands. The evaluation of the algorithms has shown that they are able to produce meaningful results, with precision in the range of 60-92% for new mappings. The algorithms were also applied to the potential extension of logical definitions of the OBO and the matching of several plant-related ontologies.ConclusionsThis work is a first step towards finding more complex relations between multiple ontologies. The evaluation shows that the results produced are significant and that the algorithms could satisfy specific integration needs.

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Section: Introductionmentioning

confidence: 99%

Improving the interoperability of biomedical ontologies with compound alignments

Oliveira

Pesquita

2018

J Biomed Semant

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“…It should also be straightforward to implement our methodology with pre-computed mappings, such as those in BioPortal [35] . Finally, the mapping gain can also be used to assess the usefulness of specific direct matching strategies such as synonym-derivation [36] .…”

Section: Resultsmentioning

confidence: 99%

Automatic Background Knowledge Selection for Matching Biomedical Ontologies

et al. 2014

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Ontology matching is a growing field of research that is of critical importance for the semantic web initiative. The use of background knowledge for ontology matching is often a key factor for success, particularly in complex and lexically rich domains such as the life sciences. However, in most ontology matching systems, the background knowledge sources are either predefined by the system or have to be provided by the user. In this paper, we present a novel methodology for automatically selecting background knowledge sources for any given ontologies to match. This methodology measures the usefulness of each background knowledge source by assessing the fraction of classes mapped through it over those mapped directly, which we call the mapping gain. We implemented this methodology in the AgreementMakerLight ontology matching framework, and evaluate it using the benchmark biomedical ontology matching tasks from the Ontology Alignment Evaluation Initiative (OAEI) 2013. In each matching problem, our methodology consistently identified the sources of background knowledge that led to the highest improvements over the baseline alignment (i.e., without background knowledge). Furthermore, our proposed mapping gain parameter is strongly correlated with the F-measure of the produced alignments, thus making it a good estimator for ontology matching techniques based on background knowledge.

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“…Another strategy AML employs for synonym generation consists in generating a thesaurus by comparing the various annotations of each class, and then using this thesaurus to generate new synonyms [ 12 , 18 ]. For example, given a lexical analysis of the annotations ’stomach serosa’ and ’gastric serosa’ for Mouse Gross Anatomy Ontology (MA) class MA_0001626, AML would add to its thesaurus that ’stomach’ and ’gastric’ are synonymous words.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, a few matching systems such as FCA-Map [ 16 ] and LogMap [ 13 ] opt for the domain specific UMLS SPECIALIST Lexicon [ 17 ]. The diversity of ontology annotations contemplated by each matching system is unclear, but the use of weights for synonyms is an uncommon feature, and a differentiating weighting scheme to reflect the precision of different types of lexical annotations has only been reported in AML [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Tackling the challenges of matching biomedical ontologies

et al. 2018

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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.

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What’s in a ‘nym’? Synonyms in Biomedical Ontology Matching

Cited by 12 publications

References 18 publications

Improving the interoperability of biomedical ontologies with compound alignments

Improving the interoperability of biomedical ontologies with compound alignments

Automatic Background Knowledge Selection for Matching Biomedical Ontologies

Tackling the challenges of matching biomedical ontologies

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