BIM: an open ontology for the annotation of biomedical images

Biomedical images published within the scientific literature play a central role in reporting and facilitating life science discoveries. Existing ontologies and vocabularies describing biomedical imag-­‐ es, particularly sequence images, do not provide sufficient seman-­‐ tic representation for image annotations generated automatically and/or semi-­‐automatically. We present an open ontology for the annotation of biomedical images (BIM) scripted in OWL/RDF. The BIM ontology provides semantic vocabularies to describe the manually curated image annotations as well as annotations gener-­‐ ated by online bioinformatics services using content extracted from images by the Semantic Enrichment of Biomedical Images (SEBI) system. The BIM ontology is represented in three parts; (i) image vocabularies -­‐ which holds vocabularies for the annotation of an image and/or region of interests (ROI) inside an image, as well as vocabularies to represent the pre and post processing states of an image, (ii) text entities -­‐ covers annotations from the text that are associated with an image (e.g. image captions) and provides semantic representation for NLP algorithm outputs, (iii) a provenance model -­‐ that contributes towards the maintenance of annotation versioning. To illustrate the BIM ontology’s utility, we provide three annotation cases generated by BIM in conjunction with the SEBI image annotation engine.