Ontology plays an essential role in the formalization of business information (e.g., products, services, relationships of businesses) for effective human-computer interactions. However, engineering of domain ontologies turns out to be very labor intensive and time consuming. Recently, some machine learning methods have been proposed for automatic discovery of domain ontologies. Nevertheless, the accuracy and computational ef ciency of the existing methods need to be improved to support large scale ontology construction for real-world business applications. This paper illustrates a novel fuzzy domain ontology discovery algorithm for supporting real-world business ontology engineering. By combining lexico-syntactic and statistical learning methods, the accuracy and the computational ef ciency of the ontology discovery process is improved. Empirical studies have con rme d that the proposed method can discover high quality fuzzy domain ontology which leads to signi cant improvement in information retrieval performance.