In a drug development process, appropriate drug-binding selectivity is critical for a success drug. However the selectivity in a data source, showing the intensity of efforts, may be limited to prior knowledge of the expertise or be biased towards the hypothesis testing. With the increasing of drug screening data, it is challenging to coordinate the efforts and execute data governance at a large scale. Visual selectivity analysis for examining target selection is in demand. We proposed a knowledge-driven approach and designed an ontology reference model to provide an intuitive view of the selectivity in a drug-target interaction network data. We employed the model to carry out the visual selectivity analysis on the NIMH Psychoactive Drug Screening Program (PDSP) data and the LINCS Compounds-interacting ChEMBL Database. The analysis indicates the possible 'dark matter' drug targets. The approach can be expanded to coordinate other experimental screening data and set a stage for the analysis of the mechanism of action of biological therapies.