747 research outputs found

    On Reasoning with RDF Statements about Statements using Singleton Property Triples

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    The Singleton Property (SP) approach has been proposed for representing and querying metadata about RDF triples such as provenance, time, location, and evidence. In this approach, one singleton property is created to uniquely represent a relationship in a particular context, and in general, generates a large property hierarchy in the schema. It has become the subject of important questions from Semantic Web practitioners. Can an existing reasoner recognize the singleton property triples? And how? If the singleton property triples describe a data triple, then how can a reasoner infer this data triple from the singleton property triples? Or would the large property hierarchy affect the reasoners in some way? We address these questions in this paper and present our study about the reasoning aspects of the singleton properties. We propose a simple mechanism to enable existing reasoners to recognize the singleton property triples, as well as to infer the data triples described by the singleton property triples. We evaluate the effect of the singleton property triples in the reasoning processes by comparing the performance on RDF datasets with and without singleton properties. Our evaluation uses as benchmark the LUBM datasets and the LUBM-SP datasets derived from LUBM with temporal information added through singleton properties

    Data mining and fusion

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    A model of provenance applied to biodiversity datasets

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    Nowadays, the Web has become one of the main sources of biodiversity information. An increasing number of biodiversity research institutions add new specimens and their related information to their biological collections and make this information available on the Web. However, mechanisms which are currently available provide insufficient provenance of biodiversity information. In this paper, we propose a new biodiversity provenance model extending the W3C PROV Data Model. Biodiversity data is mapped to terms from relevant ontologies, such as Dublin Core and GeoSPARQL, stored in triple stores and queried using SPARQL endpoints. Additionally, we provide a use case using our provenance model to enrich collection data

    A provenance metadata model integrating ISO geospatial lineage and the OGC WPS : conceptual model and implementation

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    Nowadays, there are still some gaps in the description of provenance metadata. These gaps prevent the capture of comprehensive provenance, useful for reuse and reproducibility. In addition, the lack of automated tools for capturing provenance hinders the broad generation and compilation of provenance information. This work presents a provenance engine (PE) that captures and represents provenance information using a combination of the Web Processing Service (WPS) standard and the ISO 19115 geospatial lineage model. The PE, developed within the MiraMon GIS & RS software, automatically records detailed information about sources and processes. The PE also includes a metadata editor that shows a graphical representation of the provenance and allows users to complement provenance information by adding missing processes or deleting redundant process steps or sources, thus building a consistent geospatial workflow. One use case is presented to demonstrate the usefulness and effectiveness of the PE: the generation of a radiometric pseudo-invariant areas bench for the Iberian Peninsula. This remote-sensing use case shows how provenance can be automatically captured, also in a non-sequential complex flow, and its essential role in the automation and replication tasks in work with very large amounts of geospatial data

    Community Framework for Geoscience Education Research

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    In order to guide future investments of time and resources in geoscience education research (GER), the community has developed a framework of grand challenges across ten major themes in GER. These grand challenges can provide direction to current and future researchers about where the community thinks effort should be made to answer some fundamental questions about undergraduate geoscience teaching and learning. This Community Framework for GER is comprised of ten theme chapters, as well as chapters on the development of the framework project, a synthesis of the findings and potential synergies, and on communication strategies for the transformation of geoscience teaching practice

    StraboSpot data system for structural geology

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    This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.StraboSpot is a geologic data system that allows researchers to digitally collect, store, and share both field and laboratory data. StraboSpot is based on how geologists actually work to collect field data; although initially developed for the structural geology research community, the approach is easily extensible to other disciplines. The data system uses two main concepts to organize data: spots and tags. A spot is any observation that characterizes a specific area, a concept applicable at any spatial scale from regional to microscopic. Spots are related in a purely spatial manner, and consequently, one spot can enclose multiple other spots that themselves contain other spots. In contrast, tags provide conceptual grouping of spots, allowing linkages between spots that are independent of their spatial position. The StraboSpot data system uses a graph database, rather than a relational database approach, to increase flexibility and to track geologically complex relationships. StraboSpot operates on two different platform types: (1) a fieldbased application that runs on iOS and Android mobile devices, which can function in either Internet-connected or disconnected environments; and (2) a web application that runs only in Internet-connected settings. We are presently engaged in incorporating microstructural data into StraboSpot, as well as expanding to include additional field-based (sedimentology, petrology) and lab-based (experimental rock deformation) data. The StraboSpot database will be linked to other existing and future databases in order to provide integration with other digital efforts in the geological sciences and allow researchers to do types of science that were not possible without easy access to digital data
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