International audienceBillions of RDF triples are currently available on the Web through the Linked Open Data cloud (e.g., DBpedia, LinkedGeoData and New York Times). Governments, universities as well as companies (e.g., BBC, CNN) are also producing huge collections of RDF triples and exchanging them through different serialization formats (e.g., RDF/XML, Turtle, N-Triple, etc.). However, RDF descriptions (i.e., graphs and serializations) are verbose in syntax, often contain redundancies, and could be generated differently even when describing the same resources, which would have a negative impact on their processing. Hence, we propose here an approach to clean and eliminate redundancies from such RDF descriptions as a means of transforming different descriptions of the same information into one representation, which can then be tuned, depending on the target application (information retrieval, compression, etc.). Experimental tests show significant improvements, namely in reducing RDF description loading time and file size
Los robots autónomos están desempeñando un papel importante en las actividades académicas, tecnológicas y científicas. Por lo tanto, su comportamiento se está volviendo más complejo. Las principales tareas de los robots autónomos incluyen el mapeo de un entorno y la localización de sí mismos. Estas tareas comprenden el problema de la Localización y Mapeo Simultáneo (SLAM). La representación del conocimiento SLAM (por ejemplo, las características de los robots, la información del medio ambiente, el mapeo y la información de localización), con un modelo estándar y bien definido, proporciona la base para desarrollar soluciones eficientes e interoperables. Sin embargo, hasta donde sabemos, no existe una clasificación común de esos conocimientos. Muchos trabajos existentes basados en la Web Semántica, han formulado ontologías para modelar información relacionada sólo con algunos aspectos del SLAM, sin un estándar. En este trabajo, proponemos una categorización del conocimiento manejado en el SLAM, basado en las ontolog´ıas existentes y los principios delSLAM. También clasificamos ontologías recientes y populares de acuerdo a las categor´ıas propuestas y resaltamos las lecciones a aprender de las ontolog´ıas existentes. Evidenciando la necesidad de desarrollar una ontolog´ıa completa para representar la información de SLAM en los robot móviles.
Autonomous robots are playing important roles in academic, technological, and scientific activities. Thus, their behavior is getting more complex, particularly, in tasks related to mapping an environment and localizing themselves. These tasks comprise the Simultaneous Localization and Mapping (SLAM) problem. Representation of knowledge related to the SLAM problem with a standard, flexible, and well-defined model, provides the base to develop efficient and interoperable solutions. As many existing works demonstrate, Semantic Web seems to be a clear approach, since they have formulated ontologies, as the base data model to represent such knowledge. In this article, we survey the most popular and recent SLAM ontologies with our aim being threefold: (i) propose a classification of SLAM ontologies according to the main knowledge needed to model the SLAM problem; (ii) identify existing ontologies for classifying, comparing, and contrasting them, in order to conceptualize SLAM domain for mobile robots; and (iii) pin-down lessons to learn from existing solutions in order to design better solutions and identify new research directions and further improvements. We compare the identified SLAM ontologies according to the proposed classification and, finally, we explore new data fields to enrich existing ontologies and highlight new possibilities in terms of performance and efficiency for SLAM solutions.
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