Formalized knowledge representation is the foundation of Big Data computing, mining and visualization. Current knowledge representations regard information as items linked to relevant objects or concepts by tree or graph structures. However, geographic knowledge differs from general knowledge, which is more focused on temporal, spatial, and changing knowledge. Thus, discrete knowledge items are difficult to represent geographic states, evolutions, and mechanisms, e.g., the processes of a storm "{9:30-60 mm-precipitation}-{12:00-80 mm-precipitation}-. . . ". The underlying problem is the constructors of the logic foundation (ALC description language) of current geographic knowledge representations, which cannot provide these descriptions. To address this issue, this study designed a formalized geographic knowledge representation called GeoKG and supplemented the constructors of the ALC description language. Then, an evolution case of administrative divisions of Nanjing was represented with the GeoKG. In order to evaluate the capabilities of our formalized model, two knowledge graphs were constructed by using the GeoKG and the YAGO by using the administrative division case. Then, a set of geographic questions were defined and translated into queries. The query results have shown that GeoKG results are more accurate and complete than the YAGO's with the enhancing state information. Additionally, the user evaluation verified these improvements, which indicates it is a promising powerful model for geographic knowledge representation.At present, the most popular knowledge representation is the knowledge graph. It organizes knowledge with a set of concepts, relations, and facts, which are associated by two types {entity, relation, entity} and {entity, attribute, attribute value} [4]. There are only three basic elements in knowledge graphs: the entity, relation, and attribute. These three elements can explicitly represent general information, such as "when did the Beijing storm occur on 21 July-9:30, 21 July". However, geographic knowledge is more complicated than general knowledge. More processes and evolutions need to be answered, e.g., "what caused the 7·21 Beijing storm", "how did it develop", and "what were the effects of the 7·21 Beijing storm". Entities, relations, and attributes cannot easily and directly answer these mechanics questions. For example, the geographic knowledge graph representation of the 7·21 Beijing storm is shown in Figure 1.knowledge graphs: the entity, relation, and attribute. These three elements can explicitly represent general information, such as "when did the Beijing storm occur on 21 July-9:30, 21 July". However, geographic knowledge is more complicated than general knowledge. More processes and evolutions need to be answered, e.g., "what caused the 7·21 Beijing storm", "how did it develop", and "what were the effects of the 7·21 Beijing storm". Entities, relations, and attributes cannot easily and directly answer these mechanics questions. For example, the geographic knowledge graph rep...
