Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Databases/Information Systems
Title: Heuristics-based query optimisation for SPARQL
Abstract: Query optimization in RDF Stores is a challenging problem as SPARQL queries typically contain many more joins than equivalent relational plans, and hence lead to a large join order search space. In such cases, cost-based query optimization often is not possible. One practical reason for this is that statistics typically are missing in web scale setting such as the Linked Open Datasets (LOD). The more profound reason is that due to the absence of schematic structure in RDF, join-hit ratio estimation requires complicated forms of correlated join statistics; and currently there are no methods to identify the relevant correlations beforehand. For this reason, the use of good heuristics is essential in SPARQL query optimization, even in the case that are partially used with cost-based statistics (i.e., hybrid query optimization). In this paper we describe a set of useful heuristics for SPARQL query optimizers. We present these in the context of a new Heuristic SPARQL Planner (HSP) that is capable of exploiting the syntactic and the structural variations of the triple patterns in a SPARQL query in order to choose an execution plan without the need of any cost model. For this, we define the variable graph and we show a reduction of the SPARQL query optimization problem to the maximum weight independent set problem. We implemented our planner on top of the MonetDB open source column-store and evaluated its effectiveness against the state-of-the-art RDF-3X engine as well as comparing the plan quality with a relational (SQL) equivalent of the benchmarks.
Extracted statement — Algorithm: HSP
Research field: Databases/Information Systems
Title: A learning object ontology repository to support annotation and discovery of educational resources using semantic thesauri
Abstract: <jats:p> Open educational resources are currently becoming increasingly available from a multitude of sources and are consequently annotated in many diverse ways. Interoperability concerns that naturally arise can often be resolved through the semantification of metadata descriptions, while at the same time strengthening the knowledge value of resources. SKOS can be a solid linking point offering a standard vocabulary for thematic descriptions, by referencing semantic thesauri. We propose the enhancement and maintenance of educational resources’ metadata in the form of learning object ontologies and introduce the notion of a learning object ontology repository that can help towards their publication, discovery and reuse. At the same time, linking to thesauri datasets and contextualized sources interrelates learning objects with linked data and exposes them to the Web of Data. We build a set of extensions and workflows on top of contemporary ontology management tools, such as WebProtégé, that can make it suitable as a learning object ontology repository. The proposed approach and implementation can help libraries and universities in discovering, managing and incorporating open educational resources and enhancing current curricula. </jats:p>
Extracted statement — Development in: OWL
Research field: Databases/Information Systems
Title: Capturing Knowledge in Semantically-typed Relational Patterns to Enhance Relation Linking
Abstract: Transforming natural language questions into formal queries is an integral task in Question Answering (QA) systems. QA systems built on knowledge graphs like DBpedia, require a step after natural language processing for linking words, specifically including named entities and relations, to their corresponding entities in a knowledge graph. To achieve this task, several approaches rely on background knowledge bases containing semantically-typed relations, e.g., PATTY, for an extra disambiguation step. Two major factors may affect the performance of relation linking approaches whenever background knowledge bases are accessed: a) limited availability of such semantic knowledge sources, and b) lack of a systematic approach on how to maximize the benefits of the collected knowledge. We tackle this problem and devise SIBKB, a semantic-based index able to capture knowledge encoded on background knowledge bases like PATTY. SIBKB represents a background knowledge base as a bi-partite and a dynamic index over the relation patterns included in the knowledge base. Moreover, we develop a relation linking component able to exploit SIBKB features. The benefits of SIBKB are empirically studied on existing QA benchmarks and observed results suggest that SIBKB is able to enhance the accuracy of relation linking by up to three times.
Extracted statement — uses: PATTY
Research field: Databases/Information Systems
Title: Capturing Knowledge in Semantically-typed Relational Patterns to Enhance Relation Linking
Abstract: Transforming natural language questions into formal queries is an integral task in Question Answering (QA) systems. QA systems built on knowledge graphs like DBpedia, require a step after natural language processing for linking words, specifically including named entities and relations, to their corresponding entities in a knowledge graph. To achieve this task, several approaches rely on background knowledge bases containing semantically-typed relations, e.g., PATTY, for an extra disambiguation step. Two major factors may affect the performance of relation linking approaches whenever background knowledge bases are accessed: a) limited availability of such semantic knowledge sources, and b) lack of a systematic approach on how to maximize the benefits of the collected knowledge. We tackle this problem and devise SIBKB, a semantic-based index able to capture knowledge encoded on background knowledge bases like PATTY. SIBKB represents a background knowledge base as a bi-partite and a dynamic index over the relation patterns included in the knowledge base. Moreover, we develop a relation linking component able to exploit SIBKB features. The benefits of SIBKB are empirically studied on existing QA benchmarks and observed results suggest that SIBKB is able to enhance the accuracy of relation linking by up to three times.
Extracted statement — presents: SIBKB
Research field: Databases/Information Systems
Title: Heuristics-based query optimisation for SPARQL
Abstract: Query optimization in RDF Stores is a challenging problem as SPARQL queries typically contain many more joins than equivalent relational plans, and hence lead to a large join order search space. In such cases, cost-based query optimization often is not possible. One practical reason for this is that statistics typically are missing in web scale setting such as the Linked Open Datasets (LOD). The more profound reason is that due to the absence of schematic structure in RDF, join-hit ratio estimation requires complicated forms of correlated join statistics; and currently there are no methods to identify the relevant correlations beforehand. For this reason, the use of good heuristics is essential in SPARQL query optimization, even in the case that are partially used with cost-based statistics (i.e., hybrid query optimization). In this paper we describe a set of useful heuristics for SPARQL query optimizers. We present these in the context of a new Heuristic SPARQL Planner (HSP) that is capable of exploiting the syntactic and the structural variations of the triple patterns in a SPARQL query in order to choose an execution plan without the need of any cost model. For this, we define the variable graph and we show a reduction of the SPARQL query optimization problem to the maximum weight independent set problem. We implemented our planner on top of the MonetDB open source column-store and evaluated its effectiveness against the state-of-the-art RDF-3X engine as well as comparing the plan quality with a relational (SQL) equivalent of the benchmarks.
Extracted statement — Has approach: SPARQL
Research field: Databases/Information Systems
Title: Random Walk TripleRush: Asynchronous Graph Querying and Sampling
Abstract: Most Semantic Web applications rely on querying graphs, typically by using SPARQL with a triple store. Increasingly, applications also analyze properties of the graph structure to compute statistical inferences. The current Semantic Web infrastructure, however, does not efficiently support such operations. This forces developers to extract the relevant data for external statistical post-processing. In this paper we propose to rethink query execution in a triple store as a highly parallelized asynchronous graph exploration on an active index data structure. This approach also allows to integrate SPARQL-querying with the sampling of graph properties. To evaluate this architecture we implemented Random Walk TripleRush, which is built on a distributed graph processing system. Our evaluations show that this architecture enables both competitive graph querying, as well as the ability to execute various types of random walks with restarts that sample interesting graph properties. Thanks to the asynchronous architecture, first results are sometimes returned in a fraction of the full execution time. We also evaluate the scalability and show that the architecture supports fast query-times on a dataset with more than a billion triples.
Extracted statement — Has implementation: SPARQL
Research field: Databases/Information Systems
Title: Heuristics-based query optimisation for SPARQL
Abstract: Query optimization in RDF Stores is a challenging problem as SPARQL queries typically contain many more joins than equivalent relational plans, and hence lead to a large join order search space. In such cases, cost-based query optimization often is not possible. One practical reason for this is that statistics typically are missing in web scale setting such as the Linked Open Datasets (LOD). The more profound reason is that due to the absence of schematic structure in RDF, join-hit ratio estimation requires complicated forms of correlated join statistics; and currently there are no methods to identify the relevant correlations beforehand. For this reason, the use of good heuristics is essential in SPARQL query optimization, even in the case that are partially used with cost-based statistics (i.e., hybrid query optimization). In this paper we describe a set of useful heuristics for SPARQL query optimizers. We present these in the context of a new Heuristic SPARQL Planner (HSP) that is capable of exploiting the syntactic and the structural variations of the triple patterns in a SPARQL query in order to choose an execution plan without the need of any cost model. For this, we define the variable graph and we show a reduction of the SPARQL query optimization problem to the maximum weight independent set problem. We implemented our planner on top of the MonetDB open source column-store and evaluated its effectiveness against the state-of-the-art RDF-3X engine as well as comparing the plan quality with a relational (SQL) equivalent of the benchmarks.
Extracted statement — Has implementation: RDF-3X
Research field: Earth Sciences
Title: ASTER Data Analyses for Lithological Discrimination of Sittampundi Anorthositic Complex, Southern India
Abstract: ASTER is an advanced Thermal Emission and Reflection Radiometer, a multispectral sensor, which measures reflected and emitted electromagnetic radiation of earth surface with 14 bands. The present study aims to delineate different rock types in the Sittampundi Anorthositic Complex (SAC), Tamil Nadu using Visible (VIS), near-infrared (NIR) and short wave infrared (SWIR) reflectance data of ASTER 9 band data. We used different band ratioing, band combinations in the VNIR and SWIR region for discriminating lithological boundaries. SAC is also considered as a lunar highland analog rock. Anorthosite is a plagioclase-rich igneous rock with subordinate amounts of pyroxenes, olivine and other minerals. A methodology has been applied to correct the cross talk effect and radiance to reflectance. Principal Component Analysis (PCA) has been realized on the 9 ASTER bands in order to reduce the redundancy information in highly correlated bands. PCA derived FCC results enable the validation and support to demarcate the different lithological boundaries defined on previous geological map. The image derived spectral profiles for anorthosite are compared with the ASTER resampled laboratory spectra, JHU spectral library spectra and Apollo 14 lunar anorthosites spectra. The Spectral Angle Mapping imaging spectroscopy technique has been practiced to classify the ASTER image of the study area and found that, the processing of ASTER remote sensing data set can be used as a powerful tool for mapping the terrestrial Anorthositic regions and similar kind of process could be applied to map the planetary surfaces (E.g. Moon).
Extracted statement — Data used: ASTER
Research field: Earth Sciences
Title: An image processing approach for converging ASTER-derived spectral maps for mapping Kolhan limestone, Jharkhand, India
Abstract: In the present study, we have attempted the delineation of limestone using different spectral mapping algorithms in ASTER data. Each spectral mapping algorithm derives limestone exposure map independently. Although these spectral maps are broadly similar to each other, they are also different at places in terms of spatial disposition of limestone pixels. Therefore, an attempt is made to integrate the results of these spectral maps to derive an integrated map using minimum noise fraction (MNF) method. The first MNF image is the result of two cascaded principal component methods suitable for preserving complementary information derived from each spectral map. While implementing MNF, noise or non-coherent pixels occurring within a homogeneous patch of limestone are removed first using shift difference method, before attempting principal component analysis on input spectral maps for deriving composite spectral map of limestone exposures. The limestone exposure map is further validated based on spectral data and ancillary geological data.
Extracted statement — Data used: ASTER
Research field: Earth Sciences
Title: Mapping Hydrothermally Altered Rocks at Cuprite, Nevada, Using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), a New Satellite-Imaging System
Abstract: The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a 14-band multispectral instrument on board the Earth Observing System (EOS), TERRA. The three bands between 0.52 and 0.86 μ m and the six bands from 1.60 and 2.43 μ m, which have 15- and 30-m spatial resolution, respectively, were selected primarily for making remote mineralogical determinations. The Cuprite, Nevada, mining district comprises two hydrothermal alteration centers where Tertiary volcanic rocks have been hydrothermally altered mainly to bleached silicified rocks and opalized rocks, with a marginal zone of limonitic argillized rocks. Country rocks are mainly Cambrian phyllitic siltstone and limestone. Evaluation of an ASTER image of the Cuprite district shows that spectral reflectance differences in the nine bands in the 0.52 to 2.43 μ m region provide a basis for identifying and mapping mineralogical components which characterize the main hydrothermal alteration zones: opal is the spectrally dominant mineral in the silicified zone; whereas, alunite and kaolinite are dominant in the opalized zone. In addition, the distribution of unaltered country rocks was mapped because of the presence of spectrally dominant muscovite in the siltstone and calcite in limestone, and the tuffaceous rocks and playa deposits were distinguishable due to their relatively flat spectra and weak absorption features at 2.33 and 2.20 μ m, respectively. An Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) image of the study area was processed using a similar methodology used with the ASTER data. Comparison of the ASTER and AVIRIS results shows that the results are generally similar, but the higher spectral resolution of AVIRIS (224 bands) permits identification of more individual minerals, including certain polymorphs. However, ASTER has recorded images of more than 90 percent of the Earth’s land surface with less than 20 percent cloud cover, and these data are available at nominal or no cost. Landsat TM images have a similar spatial resolution to ASTER images, but TM has fewer bands, which limits its usefulness for making mineral determinations.
Extracted statement — Data used: ASTER
Research field: Earth Sciences
Title: Spectral indices for lithologic discrimination and mapping by using the ASTER SWIR bands
Abstract: The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a research facility instrument launched on NASA's Terra spacecraft in December 1999. Spectral indices, a kind of orthogonal transformation in the five-dimensional space formed by the five ASTER short-wave-infrared (SWIR) bands, were proposed for discrimination and mapping of surface rock types. These include Alunite Index, Kaolinite Index, Calcite Index, and Montmorillonite Index, and can be calculated by linear combination of reflectance values of the five SWIR bands. The transform coefficients were determined so as to direct transform axes to the average spectral pattern of the typical minerals. The spectral indices were applied to the simulated ASTER dataset of Cuprite, Nevada, USA after converting its digital numbers to surface reflectance. The resultant spectral index images were useful for lithologic mapping and were easy to interpret geologically. An advantage of this method is that we can use the pre-determined transform coefficients, as long as image data are converted to surface reflectance.
Extracted statement — Data used: ASTER
Research field: Earth Sciences
Title: Pegmatite spectral behavior considering ASTER and Landsat 8 OLI data in Naipa and Muiane mines (Alto Ligonha, Mozambique)
Abstract: The Naipa and Muiane mines are located on the Nampula complex, a stratigraphic tectonic subdivision of the Mozambique Belt, in the Alto Ligonha region. The pegmatites are of the Li-Cs-Ta type, intrude a chlorite phyllite and gneisses with amphibole and biotite. The mines are still active. The main objective of this work was to analyze the pegmatite’s spectral behavior considering ASTER and Landsat 8 OLI data. An ASTER image from 27/05/2005, and an image Landsat OLI image from 02/02/2018 were considered. The data were radiometric calibrated and after atmospheric corrected considered the Dark Object Subtraction algorithm available in the Semi-Automatic Classification Plugin accessible in QGIS software. In the field, samples were collected from lepidolite waste pile in Naipa and Muaine mines. A spectroadiometer was used in order to analyze the spectral behavior of several pegmatite’s samples collected in the field in Alto Ligonha (Naipa and Muiane mines). In addition, QGIS software was also used for the spectral mapping of the hypothetical hydrothermal alterations associated with occurrences of basic metals, beryl gemstones, tourmalines, columbite-tantalites, and lithium minerals. A supervised classification algorithm was employed - Spectral Angle Mapper for the data processing, and the overall accuracy achieved was 80%. The integration of ASTER and Landsat 8 OLI data have proved very useful for pegmatite’s mapping. From the results obtained, we can conclude that: (i) the combination of ASTER and Landsat 8 OLI data allows us to obtain more information about mineral composition than just one sensor, i.e., these two sensors are complementary; (ii) the alteration spots identified in the mines area are composed of clay minerals. In the future, more data and others image processing algorithms can be applied in order to identify the different Lithium minerals, as spodumene, petalite, amblygonite and lepidolite.
Extracted statement — Other datasets: ASTER
Research field: Earth Sciences
Title: Detection of Pb–Zn mineralization zones in west Kunlun using Landsat 8 and ASTER remote sensing data
Abstract: Abstract. The integration of Landsat 8 OLI and ASTER data is an efficient tool for interpreting lead–zinc mineralization in the Huoshaoyun Pb–Zn mining region located in the west Kunlun mountains at high altitude and very rugged terrain, where traditional geological work becomes limited and time-consuming. This task was accomplished by using band ratios (BRs), principal component analysis, and spectral matched filtering methods. It is concluded that some BR color composites and principal components of each imagery contain useful information for lithological mapping. SMF technique is useful for detecting lead–zinc mineralization zones, and the results could be verified by handheld portable X-ray fluorescence analysis. Therefore, the proposed methodology shows strong potential of Landsat 8 OLI and ASTER data in lithological mapping and lead–zinc mineralization zone extraction in carbonate stratum.
Extracted statement — Other datasets: ASTER
Research field: Earth Sciences
Title: Lithological mapping using Landsat 8 OLI and Terra ASTER multispectral data in the Bas Drâa inlier, Moroccan Anti Atlas
Abstract: Abstract. Lithological mapping is a fundamental step in various mineral prospecting studies because it forms the basis of the interpretation and validation of retrieved results. Therefore, this study exploited the multispectral Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat 8 Operational Land Imager (OLI) data in order to map lithological units in the Bas Drâa inlier, at the Moroccan Anti Atlas. This task was completed by using principal component analysis (PCA), band ratios (BR), and support vector machine (SVM) classification. Overall accuracy and the kappa coefficient of SVM based on ground truth in addition to the results of PCA and BR show an excellent correlation with the existing geological map of the study area. Consequently, the methodology proposed demonstrates a high potential of ASTER and Landsat 8 OLI data in lithological units discrimination.
Extracted statement — Other datasets: ASTER