In the era of Web 2.0, the amount of information has been growing exponentially, intensifying the information overload problem. The inconvenience caused by information overload not only lies in casual search by every Internet user, but also in finding business related information. Business analysts now need to explore an overwhelming amount of information in order to understand business ecosystem or technology development. This study designed a mechanism hoping to decrease the information searching cost and improve the effectiveness of information searching as well. Based on the business ecosystem identified by relation extraction technique, we combined semantically networked knowledge base to organize information of diverse sources to facilitate exploratory search. On top of that, we adopted personalization technique to filter irrelevant information, and applied spreading activation model in the business ecological knowledge. Through experimental design and evaluation, we found that users can identify more relevant information facilitated by the built system than searching only via term matching or user profile matching.