Valoración de la similitud entre representaciones múltiples de datos geográficos

Abstract

Nowadays we face a variety of geographic representations of the same phenomenon, particularly in the field of geographic information. This diversity is known as multiple representations and has its origin in areas such as the purpose of data collection methods, digitization, the dynamic of the world and errors in the process itself. Multiple representations have significant and abundant knowledge but its common to find disagreements about the meaning, interpretation, use and geometric aspect of the data in its parts. This disagreement is known as semantic heterogeneity and arises because each part of multiple representations is produced and managed independently. To make the knowledge in multiple representations available is necessary to find the semantic relation between its parts and the similarity is one way to do it because it is the key to semantic integration. Based on the foregoing, this paper proposes a set-theoretic approach to assess the similarity between representations of geographic data. The assessment of similarity also includes the design and implementation of a degree of semantic interoperability that is intended to serve as a criterion of semantic integration between parts of multiple representations, and thus make knowledge more available. As case study we work with representations of locations Mexico. Different representations of locations are put into the system, then the user selects two to assess their similarities and their degree of semantic interoperability. The similarity is analyzed at different levels and at each level, similarity is represented graphically, seeking to provide the user useful and easy to understand results.