In disease information retrieval, usually, users only know a top of basic information about the disease such as headache and high fever symptoms. Most search engines rely on keyword search and do not return exact results because they only literally find the records matching with the keywords of the queries. In this work, we propose a novel interactive search for disease information by using data mining-based ontology. In particular, a human disease ontology is used as a knowledge base to semantically recognize the input for the search engine. The association rule is applied to generate associated relations among keywords (including symptoms, derives from, located in …) for interactive query refinement. A Bayesian-based ranking algorithm is also proposed to arrange the search result. Prospectively, our approach is valuable not only to increase the accuracy of searching human disease, but also provide a significant approach in data mining ontology.