A compact firefly algorithm for matching biomedical ontologies

Biomedical ontologies have gained particular relevance in the life science domain due to its prominent role in representing knowledge in this domain. However, the existing biomedical ontologies could define the same biomedical concept in different ways, which yields the biomedical ontology heterogeneous problem. To implement the inter-operability among the biomedical ontologies, it is critical to establish the semantic links between heterogenous biomedical concepts, so-called biomedical ontology matching. Since modeling the ontology matching problem is a complex and time-consuming task, swarm intelligent algorithm (SIA) becomes a state-of-the-art methodology for solving this problem. However, when addressing the biomedical ontology matching problem, the existing SIA-based matchers tend to be inefficient due to biomedical ontology’s large-scale concepts and complex semantic relationships. In this work, we propose a compact firefly algorithm (CFA), where the explicit representation of the population is replaced by a probability distribution and two compact movement operators are presented to save the memory consumption and runtime of the population-based SIAs. We exploit the anatomy track, disease and phenotype track and biodiversity and ecology track from the ontology alignment evaluation initiative (OAEI) to test CFA-based matcher’s performance. The experimental results show that CFA can improve the FA-based matcher’s memory consumption and runtime by, respectively, 68.92% and 38.97% on average, and its results significantly outperform other SIA-based matchers and OAEI’s participants.