Matching disease and phenotype ontologies in the ontology alignment evaluation initiative

Harrow, Ian; Jiménez-Ruiz, Ernesto; Splendiani, Andrea; Romacker, Martin; Woollard, Peter; Markel, Scott; Alam-Faruque, Yasmin; Koch, Martin; Malone, James; Waaler, Arild

doi:10.1186/s13326-017-0162-9

Cited by 29 publications

(23 citation statements)

References 26 publications

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“…In the Disease and Phenotype track [ 40 ], note that there are none reference mappings; instead, consensus alignments representing the agreements of the participating OM systems are used for evaluation. Out of the four tasks, FCA-Map produces the closest results to the consensus alignments in terms of F-measure in three tasks, and the second close results in terms of precision in three tasks.…”

Section: Resultsmentioning

confidence: 99%

Matching biomedical ontologies based on formal concept analysis

Zhao

Zhang

et al. 2018

J Biomed Semant

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BackgroundThe goal of ontology matching is to identify correspondences between entities from different yet overlapping ontologies so as to facilitate semantic integration, reuse and interoperability. As a well developed mathematical model for analyzing individuals and structuring concepts, Formal Concept Analysis (FCA) has been applied to ontology matching (OM) tasks since the beginning of OM research, whereas ontological knowledge exploited in FCA-based methods is limited. This motivates the study in this paper, i.e., to empower FCA with as much as ontological knowledge as possible for identifying mappings across ontologies.MethodsWe propose a method based on Formal Concept Analysis to identify and validate mappings across ontologies, including one-to-one mappings, complex mappings and correspondences between object properties. Our method, called FCA-Map, incrementally generates a total of five 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. Afterwards, the property-based formal context describes how object properties are used in axioms to connect anchor classes across ontologies, leading to property mappings. Last, the restriction-based formal context describes co-occurrence of classes across ontologies in anonymous ancestors of anchors, from which extended structural mappings and complex mappings can be identified.ResultsEvaluation on the Anatomy, the Large Biomedical Ontologies, and the Disease and Phenotype track of the 2016 Ontology Alignment Evaluation Initiative campaign demonstrates the effectiveness of FCA-Map and its competitiveness with the top-ranked systems. FCA-Map can achieve a better balance between precision and recall for large-scale domain ontologies through constructing multiple FCA structures, whereas it performs unsatisfactorily for smaller-sized ontologies with less lexical and semantic expressions.ConclusionsCompared with other FCA-based OM systems, the study in this paper is more comprehensive as an attempt to push the envelope of the Formal Concept Analysis formalism in ontology matching tasks. Five types of formal contexts are constructed incrementally, and their derived concept lattices are used to cluster the commonalities among classes at lexical and structural level, respectively. Experiments on large, real-world domain ontologies show promising results and reveal the power of FCA.

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Section: Resultsmentioning

confidence: 99%

Matching biomedical ontologies based on formal concept analysis

Zhao

Zhang

et al. 2018

J Biomed Semant

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“…It consists of two alignment tasks that involve the alignment of ENVO with SWEET and FLOPO with TO. We designed and developed this track based on the methodology presented in Harrow et al (2017) for the creation of the disease and phenotype track which closely follows the OAEI phases, taking place between June and October as depicted in Figure 1. We performed the preparatory phase in advance using ontology matching systems from the OAEI 2017 campaign for the creation of the consensus alignments.…”

Section: Experience From the Ontology Alignment Evaluation Initiativementioning

confidence: 99%

Matching biodiversity and ecology ontologies: challenges and evaluation results

Karam¹,

Khiat²,

Algergawy³

et al. 2020

The Knowledge Engineering Review

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Biodiversity research studies the variability and diversity of organisms, including variability within and between species with particular focus on the functional diversity of traits and their relationship to environment. Managing biodiversity data implies dealing with its heterogeneous nature using semantics and tailored ontologies. These are themselves differently conceived, and combining them in semantically enabled applications necessitates an effective alignment between their concepts. This paper describes the ontology matching of biodiversity- and ecology-related ontologies. We illustrate diverse challenges introduced by this kind of ontologies to ontology matching in general. Real use cases requiring pairwise alignments between environment and trait ontologies are introduced. We describe our experience creating a new track at the Ontology Alignment Evaluation Initiative designed for this specific domain and report on the results obtained by state-of-the-art participating systems. The biodiversity and ecology use case turns out to be a strong one for ontology matching, introducing new interesting challenges. Even if most of the matching systems perform relatively well in the proposed matching tasks, there is still room for improvement. We highlight possible directions in that matter and elaborate on our plan to further progress with the track.

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“…We also applied SANOM to the OAEI disease and phenotype track (Harrow et al, 2017) that consists of matching various disease and phenotype ontologies. For this experiment, we consider the alignment of the human phenotype (HP) to the mammalian phenotype (MP), and the human disease ontology (DOID) and the orphanet and rare diseases ontology (ORDO).…”

Section: The Disease and Phenotype Trackmentioning

confidence: 99%

SANOM-HOBBIT: simulated annealing-based ontology matching on HOBBIT platform

Mohammadi

Hofman²,

Tan

2020

The Knowledge Engineering Review

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Ontology alignment is an important and inescapable problem for the interconnections of two ontologies stating the same concepts. Ontology alignment evaluation initiative (OAEI) has been taken place for more than a decade to monitor and help the progress of the field and to compare systematically existing alignment systems. As of 2018, the evaluation of systems is partly transitioned to the HOBBIT platform. This paper contains the description of our alignment system, simulated annealing-based ontology matching (SANOM), and its adaption into the HOBBIT platform. The outcomes of SANOM on the HOBBIT for several OAEI tracks are reported, and the results are compared with other competing systems in the corresponding tracks.

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Matching disease and phenotype ontologies in the ontology alignment evaluation initiative

Cited by 29 publications

References 26 publications

Matching biomedical ontologies based on formal concept analysis

Matching biomedical ontologies based on formal concept analysis

Matching biodiversity and ecology ontologies: challenges and evaluation results

SANOM-HOBBIT: simulated annealing-based ontology matching on HOBBIT platform

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