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In the semantic web applied to life sciences, ontologies provide a basis to define concepts and to describe data in biological databases, thereby facilitate data interoperability across multiple resources. In the context of evolutionary genetics, the best corresponding genes across different species (eg the insulin genes in the pig and the human) are called “orthologs”. Dozens of bioinformatic resources identify and describe such orthologs. To represent the orthology content, an OWL-based orthology ontology (ORTH) was recently proposed. However, ORTH ontology lacks a basis to infer pairwise relations between genes besides more specific and accurate definitions of class restrictions, property domains and property ranges-which is hampering wider adoption by orthology resources. To address this issue, we present in this paper our common efforts to define a release candidate of a second version of ORTH ontology. By using this ontology, we propose a logical rule-based approach to infer information which is not explicitly defined in the primary data. As a benefit of our approach, for example, we can avoid the materialization of several billion triples to represent “is orthologous to” relation when considering the Orthologous Matrix (OMA) dataset.