The paper develops the first deepfake domain ontology that could assist common individuals in understanding the growing concerns of AI-manipulated digital media and researchers in domain knowledge integration and inference. For a foundational ontology, authors focused on structuring knowledge related to a deepfake attack, like the vulnerable entity, deepfake creator, attack goal, medium, generation technique, consequences, preventive measures, etc. The authors used knowledge engineering methodology, Protégé Desktop, and the W3C Web Ontology Development Language for ontology creation. The manual literature review from prominent research publications, and evaluation of existing ontologies helped identify 19 core entities and 28 relations describing the deepfake domain. The paper also presents knowledge graphs application of the developed ontology. The textual data of 35 plus global deepfake events in the context of politics, law, world security, etc. is collected and visualized in the form of knowledge graphs. The authors created SWRL rules that helped infer additional information from the deepfake attack knowledgebase via knowledge graphs application, such as various ways a particular entity can be affected by a deepfake, mediums used for attacks, and online security measures victims can adopt. The ontology can be extended iteratively with new domain advancements. As a next step, authors plan on adopting NLP approaches for automating domain entity research and deepfake event knowledge base population.