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Considerable amounts of geographical data are still collected not in form of GIS data but just as natural language texts. This paper proposes an approach for the automatic geocoding of itineraries described in natural language. This approach needs as an input a text annotated with part-of-speech and geosemantic tags. The proposed method is divided into three main steps. Firstly we build a complete graph where vertices represent locations, all vertices are connected to each other by undirected edges. We assign a weight to all the edges of the complete graph using a multi-criteria analysis approach. Then we compute a minimum spanning tree to obtain an undirected acyclic graph connecting all vertices. And finally, we transform this graph into a partially directed acyclic graph in order to identify the sequence of waypoints and build an approximation of a plausible footprint of the itinerary described. Additionally, the rationale of the proposed approach has been verified with a set of experiments on a corpus of hiking descriptions.

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Pests in crops produce important economic loses all around the world. To deal with them without damaging people or the environment, governments have established strict legislation and norms describing the products and procedures of use. However, since these norms frequently change to reflect scientific and technological advances, it is needed to perform a frequent review of affected norms in order to update pest related information systems. This is not an easy task because they are usually human-oriented, so intensive manual labour is required. To facilitate the use of this information, this work proposes the construction of a recommendation system that facilitates the identification of pests and the selection of suitable treatments. The core of this system is an ontology that models the interactions between crops, pests and treatments.

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With recent advances in remote sensing, location-based services and other related technologies, the production of geospatial information has exponentially increased in the last decades. Furthermore, to facilitate discovery and efficient access to such information, spatial data infrastructures were promoted and standardized, with a consideration that metadata is essential to describing data and services. Standardization bodies such as the International Organization for Standardization have defined well-known metadata models such as ISO 19115. However, current metadata assets exhibit heterogeneous quality levels because they are created by different producers with different perspectives. To address quality-related concerns, several initiatives attempted to define a common framework and test the suitability of metadata through automatic controls. Nevertheless, these controls are focused on interoperability by testing the format of metadata and a set of controlled elements. In this paper, we propose a methodology of testing the quality of metadata by considering aspects other than interoperability. The proposal adapts ISO 19157 to the metadata case and has been applied to a corpus of the Spanish Spatial Data Infrastructure. The results demonstrate that our quality check helps determine different types of errors for all metadata elements and can be almost completely automated to enhance the significance of metadata.

Knowledge organization systems denotes formally represented knowledge that is used within the context of digital libraries to improve data sharing and information retrieval. To increase their use, and to reuse them when possible, it is vital to manage them adequately and to provide them in a standard interchange format. Simple knowledge organization systems (SKOS) seem to be the most promising representation for the type of knowledge models used in digital libraries, but there is a lack of tools that are able to properly manage it. This work presents a tool that fills this gap, facilitating their use in different environments and using SKOS as an interchange format.