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Discovering Cross-Ontology Subsumption Relationships by Using Ontological Annotations on Biomedical Literature

Cross-ontology concept subsumption relationships facilitate the integration of ontologies by explicitly defining the generalization of a concept over other concepts in different ontologies. However, existing methods for discovering these relationships show poor performances and one of the problems is the lack of instance data in ontologies that can be used to identify cross-ontology subsumptions reliably. To address the problem, we present a novel method, SURD (SUbsumption Relation Discovery), which uses annotations on biomedical text corpora for populating ontologies with instances. Subsumption relationships between pairs of concepts are then determined based on their shared instances. SURD shows good performance when applied to biomedical ontologies, achieving precision values of 0.786 and 0.729 for cross-ontology subsumptions between the ontology pairs GRO-UMLS Metathesaurus and GENIAUMLS Metathesaurus respectively. As a practical application, we used SURD’s subsumptions for automated ontological corpus annotation and achieved F-measures of 0.693 and 0.783 on the GRO and GENIA corpora respectively. These results are superior to the results of using subsumption relations inferred from equivalence relations (F-measures of 0.569 and 0.645) and subsumption relations identified with Hearst patterns (F-measures of 0.002 and 0.096).