Ontologies and uncertainty in multi-sources geographical data fusion estimation

Multi-sources geographic data give rise to a challenge for data integration and fusion and their application in natural resources and environmental assessment. Data heterogeneous and uncertainty are the main barriers for the integrated assessment and modeling. This paper will give formal modeling methods of assessment targets and related uncertainty theories. A target Geo-Entity model and graph based ontology method for data fusion estimation has been given. Two important relations of association and causal relations are considered. Uncertainty in multi-sources data fusion, including random and epistemic uncertainty, entity property, and relation uncertainty are discussed. By combining the uncertainty with the graph model based ontology, a formal target entity model for multi-sources data fusion was design. Finally, two examples of multi-sources data fusion were developed to illustrate the proposed methods. The association relation and causal relation are involved in the examples, and corresponding fusion operators were given.