Providing Geographical Information Systems with mechanisms for processing geo‐data based on their semantics may help to solve problems like heterogeneity. This is because GIS could process geo‐data focusing on their meaning and not on their syntax and/or structure. An important aspect for achieving these objectives is the establishment of an automatic means of correspondence between geo‐data and their conceptualization in Higher Levels Ontologies (HLO). In this article, a new type of Ontology is proposed (Data‐Representation Ontology (DRO)). This Ontology describes the semantic embedded in geo‐data, which cannot be represented in current types of Ontologies. Across this Ontology, heterogeneous geographical data can be integrated in the semantic space contributing positively to the development of solutions for the problems of interoperability between heterogeneous systems. Likewise, we propose a new method for the automatic generation of the DRO and its interrelationships with HLO, based on pattern classification techniques. The experiments show that once the DRO is generated, the classifier can classify all data correctly. Thus, these data are semantically enriched. Moreover, this article shows how the topological relationships can enrich the semantics in the generated Ontology and increase the effectiveness of spatial analysis.
Geographical data is obtained through abstractions made from objects in the real world. Generally, each of these abstractions is obtained by taking into account only one point of view about the object being analyzed. When different abstractions are made on the same object different data sources regarding to it are produced. These data sources are generally heterogeneous. Thus the semantic processing of these objects become challenge since different data sources must be combined to obtain good results in tasks such as information retrieval and analysis for decision-making. This paper presents an approach based on ontologies to enrich the semantic representation of geospatial objects taking into account different abstractions made on them. The experimental results show the usefulness of this approach and how it is possible to make a multidimensional semantic representation automatically using classification algorithms and search techniques on trees.
Providing Geographical Information Systems (GIS) with the mechanisms for processing geographical data based on their semantic abstraction is a task that at present is carried out in a number of research given their scope of applications. Tackling this issue may help to solve many problems of geographical data like its heterogeneity, since the SIG could process geographical data focusing on their meaning and not on their syntax and/or structure, thus reducing the Man-Machine semantic gap. An important aspect for achieving these objectives is the establishment of an automatic way of correspondence between geographical data and their conceptualization in a Domain Ontology. In this work, we propose a new type of Ontology, a Data-Representation Ontology. We also propose a new method for the automatic generation of the Data-Representation Ontology from geographical data and his interrelationships with the Domain Ontology. For this we use pattern classification techniques and a dissimilarity measure. The experiments showed that once the Data-Representation Ontology was generated, the classifier using dissimilarities could correctly classify all the data.
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