Text-to-image generation via Generative Adversarial Networks (GANs) is largely explored within image generation from captions. However, semantic exploration or integrating knowledge bases into image generation is uncharted, which is why we seek to generate images from ideas or concepts that are obscure to imagine. Thus, to understand the visualization of concepts, we synthesize GAN visuals from a semantic knowledge graph with meanings and understanding of words.