This research aims to develop a knowledge base for a disaster management question-answering dialogue system. The rapid growth of the amount of data has led to the variance of data in terms of their formats, sources, and attributes. Hence, the difficulties of decision makers to accomplish their missions accurately and efficiently have increased. To solve this problem, we developed a question-answering dialogue system for disaster management. In our previous research, we found that the information most likely retrieved in response to a users request can be determined by calculating the similarity between the keywords and the users input in a handcrafted keyword-information mapping table. However, we also noticed that managing the mapping table was a tedious task. Moreover, for the inputs that had more than one keyword, the system was unable to provide integrated information. Therefore, we constructed a knowledge base to optimize the performance and maintainability of the system. To build the knowledge base for disaster management, we designed the domain model by performing an abstraction on the knowledge of professional information providers and the required data on disaster management, while considering their source, certainty, and spatiotemporal features. The query of requested information from the knowledge base is composed of mentioned entities in the users input. For the dialogue system to recognize the entities, we applied entity recognition. The subtasks include segmentation, tagging, similarity calculation with the names of the entities in the knowledge base, and intent detection to determine the desired knowledge of the user.