Ontology-Based Probabilistic Estimation for Assessing Semantic Similarity of Land Use/Land Cover Classification Systems

To accurately and formally represent the historical trajectory present current situation of land use/land cover (LULC), numerous types classification standards for LULC have been developed by different nations, institutes, organizations, etc.; however, these systems legends generate polysemy ambiguity in integration sharing. The approaches dealing with semantic heterogeneity terms similarity. Generally speaking, lack domain ontologies, which might be a significant barrier to implementing similarity assessment. In this paper, we propose an ontological approach assess standards. We develop ontologies explicitly define descriptions codes as information, organize information rules logical reasoning. Then, utilize Bayes algorithm create conditional probabilistic model computing two separate systems. experiment shows that can effectively measured integrating based on content ontology.