Introduction:

Geographic Information Systems (GIS) have become very relevant by increasing the value of geographic information for decision making. Improving the efficacy of information processing and retrieval in GIS -taking into account the large geographic data volumes available, their heterogeneity, and the absence of formalized semantic meanings- is a challenge and constitutes the general objective of this research.

Methods:

Ontology-based approaches (e.g. geographic ontologies) are a very promising alternative to provide semantics to data and its processing. Ontology-based data access and integration paradigms enable the management of large ontologies and the interoperability of heterogeneous data, issues also addressed in the research. The paper presents a new semi-automatic method for the generation of geographic ontologies from heterogeneous spatial data sources and a data management model based on ontologies and Case-Based Reasoning (CBR).

Results:

The proposed method provides more robust knowledge resources to the GIS, increasing their information retrieval capabilities and efficacy. In addition, the paradigm of database-supported ontologies applied allows for better management of the knowledge volume generated. This paradigm, also applied to the proposed ontology-based data management model, permitted solving the interoperability problems existing in Electric Union systems between geographic and electric domain data, while extending query formulation capabilities and information retrieval with the RBC.

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