Moussab/ORKG-training-evaluation-set huggingface dataset:
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 — Algorithm: Random walk
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: Random Walks
Research field: Digital Communications and Networking
Title: CUBIC: a new TCP-friendly high-speed TCP variant
Abstract: CUBIC is a congestion control protocol for TCP (transmission control protocol) and the current default TCP algorithm in Linux. The protocol modifies the linear window growth function of existing TCP standards to be a cubic function in order to improve the scalability of TCP over fast and long distance networks. It also achieves more equitable bandwidth allocations among flows with different RTTs (round trip times) by making the window growth to be independent of RTT -- thus those flows grow their congestion window at the same rate. During steady state, CUBIC increases the window size aggressively when the window is far from the saturation point, and the slowly when it is close to the saturation point. This feature allows CUBIC to be very scalable when the bandwidth and delay product of the network is large, and at the same time, be highly stable and also fair to standard TCP flows. The implementation of CUBIC in Linux has gone through several upgrades. This paper documents its design, implementation, performance and evolution as the default TCP algorithm of Linux.
Extracted statement — Method: Congestion Window
Research field: Digital Communications and Networking
Title: MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach
Abstract: Entity linking has recently been the subject of a significant body of research. Currently, the best performing approaches rely on trained mono-lingual models. Porting these approaches to other languages is consequently a difficult endeavor as it requires corresponding training data and retraining of the models. We address this drawback by presenting a novel multilingual, knowledge-base agnostic and deterministic approach to entity linking, dubbed MAG. MAG is based on a combination of context-based retrieval on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data sets and in 7 languages. Our results show that the best approach trained on English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse on datasets in other languages. MAG on the other hand achieves state-of-the-art performance on English datasets and reaches a micro F-measure that is up to 0.6 higher than that of PBOH on non-English languages.
Extracted statement — Material: datasets in other languages
Research field: Digital Communications and Networking
Title: MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach
Abstract: Entity linking has recently been the subject of a significant body of research. Currently, the best performing approaches rely on trained mono-lingual models. Porting these approaches to other languages is consequently a difficult endeavor as it requires corresponding training data and retraining of the models. We address this drawback by presenting a novel multilingual, knowledge-base agnostic and deterministic approach to entity linking, dubbed MAG. MAG is based on a combination of context-based retrieval on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data sets and in 7 languages. Our results show that the best approach trained on English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse on datasets in other languages. MAG on the other hand achieves state-of-the-art performance on English datasets and reaches a micro F-measure that is up to 0.6 higher than that of PBOH on non-English languages.
Extracted statement — Material: English datasets (PBOH)
Research field: Digital Communications and Networking
Title: Design and Evaluate Immersive Learning Experience for Massive Open Online Courses (MOOCs)
Abstract: Massive open online courses (MOOCs), a unique form of online education enabled by web-based learning technologies, allow learners from anywhere in the world with any level of educational background to enjoy online education experience provided by many top universities all around the world. Traditionally, MOOC learning contents are always delivered as text-based or video-based materials. Although introducing immersive learning experience for MOOCs may sound exciting and potentially significative, there are a number of challenges given this unique setting. In this paper, we present the design and evaluation methodologies for delivering immersive learning experience to MOOC learners via multiple media. Specifically, we have applied the techniques in the production of a MOOC entitled Virtual Hong Kong: New World, Old Traditions, led by AIMtech Centre, City University of Hong Kong, which is the first MOOC (as our knowledge) that delivers immersive learning content for distant learners to appreciate and experience how the traditional culture and folklore of Hong Kong impact upon the lives of its inhabitants in the 21st Century. The methodologies applied here can be further generalized as the fundamental framework of delivering immersive learning for future MOOCs.
Extracted statement — Type of MOOC: Immersive learning
Research field: Digital Communications and Networking
Title: MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach
Abstract: Entity linking has recently been the subject of a significant body of research. Currently, the best performing approaches rely on trained mono-lingual models. Porting these approaches to other languages is consequently a difficult endeavor as it requires corresponding training data and retraining of the models. We address this drawback by presenting a novel multilingual, knowledge-base agnostic and deterministic approach to entity linking, dubbed MAG. MAG is based on a combination of context-based retrieval on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data sets and in 7 languages. Our results show that the best approach trained on English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse on datasets in other languages. MAG on the other hand achieves state-of-the-art performance on English datasets and reaches a micro F-measure that is up to 0.6 higher than that of PBOH on non-English languages.
Extracted statement — Data: micro F-measure
Research field: Digital Communications and Networking
Title: MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach
Abstract: Entity linking has recently been the subject of a significant body of research. Currently, the best performing approaches rely on trained mono-lingual models. Porting these approaches to other languages is consequently a difficult endeavor as it requires corresponding training data and retraining of the models. We address this drawback by presenting a novel multilingual, knowledge-base agnostic and deterministic approach to entity linking, dubbed MAG. MAG is based on a combination of context-based retrieval on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data sets and in 7 languages. Our results show that the best approach trained on English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse on datasets in other languages. MAG on the other hand achieves state-of-the-art performance on English datasets and reaches a micro F-measure that is up to 0.6 higher than that of PBOH on non-English languages.
Extracted statement — Material: non-English languages
Research field: Digital Communications and Networking
Title: MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach
Abstract: Entity linking has recently been the subject of a significant body of research. Currently, the best performing approaches rely on trained mono-lingual models. Porting these approaches to other languages is consequently a difficult endeavor as it requires corresponding training data and retraining of the models. We address this drawback by presenting a novel multilingual, knowledge-base agnostic and deterministic approach to entity linking, dubbed MAG. MAG is based on a combination of context-based retrieval on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data sets and in 7 languages. Our results show that the best approach trained on English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse on datasets in other languages. MAG on the other hand achieves state-of-the-art performance on English datasets and reaches a micro F-measure that is up to 0.6 higher than that of PBOH on non-English languages.
Extracted statement — Material: structured knowledge bases
Research field: Digital Communications and Networking
Title: MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach
Abstract: Entity linking has recently been the subject of a significant body of research. Currently, the best performing approaches rely on trained mono-lingual models. Porting these approaches to other languages is consequently a difficult endeavor as it requires corresponding training data and retraining of the models. We address this drawback by presenting a novel multilingual, knowledge-base agnostic and deterministic approach to entity linking, dubbed MAG. MAG is based on a combination of context-based retrieval on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data sets and in 7 languages. Our results show that the best approach trained on English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse on datasets in other languages. MAG on the other hand achieves state-of-the-art performance on English datasets and reaches a micro F-measure that is up to 0.6 higher than that of PBOH on non-English languages.
Extracted statement — Material: trained mono-lingual models
Research field: Digital Communications and Networking
Title: Deeper Text Understanding for IR with Contextual Neural Language Modeling
Abstract: Neural networks provide new possibilities to automatically learn complex language patterns and query-document relations. Neural IR models have achieved promising results in learning query-document relevance patterns, but few explorations have been done on understanding the text content of a query or a document. This paper studies leveraging a recently-proposed contextual neural language model, BERT, to provide deeper text understanding for IR. Experimental results demonstrate that the contextual text representations from BERT are more effective than traditional word embeddings. Compared to bag-of-words retrieval models, the contextual language model can better leverage language structures, bringing large improvements on queries written in natural languages. Combining the text understanding ability with search knowledge leads to an enhanced pre-trained BERT model that can benefit related search tasks where training data are limited.
Extracted statement — Has value: Word embedding
Research field: Earth Sciences
Title: Performance of Spectral Angle Mapper and Parallelepiped Classifiers in Agriculture Hyperspectral Image
Abstract: Hyperspectral Imaging (HSI) is used to provide a wealth of information which can be used to address a variety of problems in different applications. The main requirement in all applications is the classification of HSI data. In this paper, supervised HSI classification algorithms are used to extract agriculture areas that specialize in wheat growing and get a classified image. In particular, Parallelepiped and Spectral Angel Mapper (SAM) algorithms are used. They are implemented by a software tool used to analyse and process geospatial images that is an Environment of Visualizing Images (ENVI). They are applied on Al-Kharj, Saudi Arabia as the study area. The overall accuracy after applying the algorithms on the image of the study area for SAM classification was 66.67%, and 33.33% for Parallelepiped classification. Therefore, SAM algorithm has provided a better a study area image classification.
Extracted statement — Study Area:  Al-kharj, Saudi Arabia
Research field: Earth Sciences
Title: Automated Seasonal Separation of Mine and Non Mine Water Bodies From Landsat 8 OLI/TIRS Using Clay Mineral and Iron Oxide Ratio
Abstract: Opencast mining has huge effects on water pollution for several reasons. Fresh water is heavily used to process ore. Mine effluent and seepage from various mine related areas especially tailing reservoir, increase water pollution immensely. Monitoring and classification of mine water bodies, which have such environmental impacts, have several research challenges. In the past, land cover classification of a mining region detects mine and non mine water bodies simultaneously. Water bodies inside surface mines have different characteristics from other water bodies. In this paper, a novel method has been proposed to differentiate mine and non mine water bodies over the seasons, which does not require to set a threshold value manually. Here, water body regions are detected over the entire scene by any classical water body detection algorithm. Further, each water body is treated independently, and reflectance properties of a bounding box over each water body region are analyzed. In the past, there were efforts to use clay mineral ratio (CLM) to separate mine and non mine water bodies. In this paper, it has been observed that iron oxide ratio (IO) can also separate mine and non mine water bodies. The accuracy is observed to increase, if the difference of CLM and IO is used for segregation. The proposed algorithm separates these regions by taking into account seasonal variations. Means of differences of CLM and IO of each bounding box have been clustered using K-means clustering algorithm. The automation provides precision and recall for mine, and non mine water bodies as $[77.83\%,76.55\%]$ and $[75.18\%,75.84\%]$, respectively, using ground truths from high-definition Google Earth images.
Extracted statement — Methods:  Clay mineral ratio (CLM)
Research field: Earth Sciences
Title: Mapping hydrothermal alteration minerals using high-resolution AVIRIS-NG hyperspectral data in the Hutti-Maski gold deposit area, India
Abstract: ABSTRACT The present study exploits high-resolution hyperspectral imagery acquired by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor from the Hutti-Maski gold deposit area, India, to map hydrothermal alteration minerals. The study area is a volcanic-dominated late Archean greenstone belt that hosts major gold mineralization in the Eastern Dharwar Craton of southern India. The study encompasses pre-processing, spectral and spatial image reduction using Minimum Noise Fraction (MNF) and Fast Pixel Purity Index (FPPI), followed by endmember extraction using n-dimensional visualizer and the United States Geological Survey (USGS) mineral spectral library. Image derived endmembers such as goethite, chlorite, chlorite at the mine site (chlorite mixed with mined materials), kaolinite, and muscovite were subsequently used in spectral mapping methods such as Spectral Angle Mapper (SAM), Spectral Information Divergence (SID) and its hybrid, i.e. SIDSAMtan. Spectral similarity matrix of the target and non-target-based method has been proposed to find the possible optimum threshold needed to obtain mineral map using spectral mapping methods. Relative Spectral Discrimination Power (RSDPW) and Confusion Matrix (CM) have been used to evaluate the performance of SAM, SID, and SIDSAMtan. The RSDPW and CM illustrate that the SIDSAMtan benefits from the unique characteristics of SAM and SID to achieve better discrimination capability. The Overall Accuracy (OA) and kappa coefficient (ҡ) of SAM, SID, and SIDSAMtan were computed using 900 random validation points and obtained 90% (OA) and 0.88 (ҡ), 91.4% and 0.90, and 94.4% and 0.93, respectively. Obtained mineral map demonstrates that the northern portion of the area mainly consists of muscovite whereas the southern part is marked by chlorite, goethite, muscovite and kaolinite, indicating the propylitic alteration. Most of these minerals are associated with altered metavolcanic rocks and migmatite.
Extracted statement — yields:  Confusion Matrix
Research field: Earth Sciences
Title: A Raman spectroscopic study of humite minerals
Abstract: Raman spectroscopy has been used to study the structure of the humite mineral group ((A2SiO4)n–A(OH, F)2 where n represents the number of olivine and brucite layers in the structure and is 1, 2, 3 or 4 and A2+ is Mg, Mn, Fe or some mix of these cations). The humite group of minerals forms a morphotropic series with the minerals olivine and brucite. The members of the humite group contain layers of the olivine structure that alternate with layers of the brucite-like sheets. The minerals are characterized by a complex set of bands in the 800–1000 cm−1 region attributed to the stretching vibrations of the olivine (SiO4)4− units. The number of bands in this region is influenced by the number of olivine layers. Characteristic bending modes of the (SiO4)4− units are observed in the 500–650 cm−1 region. The brucite sheets are characterized by the OH stretching vibrations in the 3475–3625 cm−1 wavenumber region. The position of the OH stretching vibrations is determined by the strength of the hydrogen bond formed between the brucite-like OH units and the olivine silica layer. The number of olivine sheets and not the chemical composition determines the strength of the hydrogen bonds. Copyright © 2006 John Wiley & Sons, Ltd.
Extracted statement — Minerals in consideration:  Humite Group
Research field: Earth Sciences
Title: Automated Seasonal Separation of Mine and Non Mine Water Bodies From Landsat 8 OLI/TIRS Using Clay Mineral and Iron Oxide Ratio
Abstract: Opencast mining has huge effects on water pollution for several reasons. Fresh water is heavily used to process ore. Mine effluent and seepage from various mine related areas especially tailing reservoir, increase water pollution immensely. Monitoring and classification of mine water bodies, which have such environmental impacts, have several research challenges. In the past, land cover classification of a mining region detects mine and non mine water bodies simultaneously. Water bodies inside surface mines have different characteristics from other water bodies. In this paper, a novel method has been proposed to differentiate mine and non mine water bodies over the seasons, which does not require to set a threshold value manually. Here, water body regions are detected over the entire scene by any classical water body detection algorithm. Further, each water body is treated independently, and reflectance properties of a bounding box over each water body region are analyzed. In the past, there were efforts to use clay mineral ratio (CLM) to separate mine and non mine water bodies. In this paper, it has been observed that iron oxide ratio (IO) can also separate mine and non mine water bodies. The accuracy is observed to increase, if the difference of CLM and IO is used for segregation. The proposed algorithm separates these regions by taking into account seasonal variations. Means of differences of CLM and IO of each bounding box have been clustered using K-means clustering algorithm. The automation provides precision and recall for mine, and non mine water bodies as $[77.83\%,76.55\%]$ and $[75.18\%,75.84\%]$, respectively, using ground truths from high-definition Google Earth images.
Extracted statement — Methods:  iron oxide ratio (IO)
Research field: Earth Sciences
Title: Application of remote sensing methods to hydrology and water resources
Abstract: Abstract A brief review of research in remote sensing of water resources indicates that there are many positive results, and some techniques have been applied operationally. Currently, remote sensing data are being used operationally in precipitation estimates, soil moisture measurements for irrigation scheduling, snow water equivalent and snow cover extent assessments, seasonal and short term snowmelt runoff forecasts, and surface water inventories. In the next decade other operational applications are likely using remote measurements of land cover, sediment loads, erosion, groundwater, and areal inputs to hydrological models. Many research challenges remain, and significant progress is expected in areas like albedo measurements, energy budgets, and evapotranspiration estimation. The research in remote sensing and water resources also has much relevance for related studies of climate change and global habitability.
Extracted statement — Outcomes:  Irrigation scheduling