Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Databases/Information Systems
Title: Self-training author name disambiguation for information scarce scenarios
Abstract: We present a novel 3‐step self‐training method for author name disambiguation—SAND (self‐training associative name disambiguator)—which requires no manual labeling, no parameterization (in real‐world scenarios) and is particularly suitable for the common situation in which only the most basic information about a citation record is available (i.e., author names, and work and venue titles). During the first step, real‐world heuristics on coauthors are able to produce highly pure (although fragmented) clusters. The most representative of these clusters are then selected to serve as training data for the third supervised author assignment step. The third step exploits a state‐of‐the‐art transductive disambiguation method capable of detecting unseen authors not included in any training example and incorporating reliable predictions to the training data. Experiments conducted with standard public collections, using the minimum set of attributes present in a citation, demonstrate that our proposed method outperforms all representative unsupervised author grouping disambiguation methods and is very competitive with fully supervised author assignment methods. Thus, different from other bootstrapping methods that explore privileged, hard to obtain information such as self‐citations and personal information, our proposed method produces topnotch performance with no (manual) training data or parameterization and in the presence of scarce information.
Extracted statement — has research problem: Author name disambiguation
Research field: Databases/Information Systems
Title: Scalable Methods for Measuring the Connectivity and Quality of Large Numbers of Linked Datasets
Abstract: Although the ultimate objective of Linked Data is linking and integration, it is not currently evident how connected the current Linked Open Data (LOD) cloud is. In this article, we focus on methods, supported by special indexes and algorithms, for performing measurements related to the connectivity of more than two datasets that are useful in various tasks including (a) Dataset Discovery and Selection; (b) Object Coreference, i.e., for obtaining complete information about a set of entities, including provenance information; (c) Data Quality Assessment and Improvement, i.e., for assessing the connectivity between any set of datasets and monitoring their evolution over time, as well as for estimating data veracity; (d) Dataset Visualizations; and various other tasks. Since it would be prohibitively expensive to perform all these measurements in a naïve way, in this article, we introduce indexes (and their construction algorithms) that can speed up such tasks. In brief, we introduce (i) a namespace-based prefix index, (ii) a sameAs catalog for computing the symmetric and transitive closure of the owl:sameAs relationships encountered in the datasets, (iii) a semantics-aware element index (that exploits the aforementioned indexes), and, finally, (iv) two lattice-based incremental algorithms for speeding up the computation of the intersection of URIs of any set of datasets. For enhancing scalability, we propose parallel index construction algorithms and parallel lattice-based incremental algorithms, we evaluate the achieved speedup using either a single machine or a cluster of machines, and we provide insights regarding the factors that affect efficiency. Finally, we report measurements about the connectivity of the (billion triples-sized) LOD cloud that have never been carried out so far.
Extracted statement — has research problem: Data Quality
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 — has research problem: educational resources
Research field: Databases/Information Systems
Title: A survey of current Link Discovery frameworks
Abstract: Links build the backbone of the Linked Data Cloud. With the steady growth in size of datasets comes an increased need for end users to know which frameworks to use for deriving links between datasets. In this survey, we comparatively evaluate current Link Discovery tools and frameworks. For this purpose, we outline general requirements and derive a generic architecture of Link Discovery frameworks. Based on this generic architecture, we study and compare the features of state-ofthe-art linking frameworks. We also analyze reported performance evaluations for the different frameworks. Finally, we derive insights pertaining to possible future developments in the domain of Link Discovery.
Extracted statement — has research problem: Link Discovery
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 — has research problem: metadata description
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 — has research problem: Question Answering
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 — has research problem: Relation linking
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 research problem: SPARQL query optimization
Research field: Earth Sciences
Title: A short survey of hyperspectral remote sensing applications in agriculture
Abstract: Hyperspectral sensors are devices that acquire images over hundreds of spectral bands, thereby enabling the extraction of spectral signatures for objects or materials observed. Hyperspectral remote sensing has been used over a wide range of applications, such as agriculture, forestry, geology, ecological monitoring and disaster monitoring. In this paper, the specific application of hyperspectral remote sensing to agriculture is examined. The technological development of agricultural methods is of critical importance as the world's population is anticipated to continuously rise much beyond the current number of 7 billion. One area upon which hyperspectral sensing can yield considerable impact is that of precision agriculture - the use of observations to optimize the use of resources and management of farming practices. For example, hyperspectral image processing is used in the monitoring of plant diseases, insect pests and invasive plant species; the estimation of crop yield; and the fine classification of crop distributions. This paper also presents a detailed overview of hyperspectral data processing techniques and suggestions for advancing the agricultural applications of hyperspectral technologies in Turkey.
Extracted statement — has research problem: Application of Hyperspectral remote sensing to Agriculture
Research field: Economics
Title: COVID-19 Disruptions Disproportionately Affect Female Academics
Abstract: The rapid spread of the COVID-19 pandemic and subsequent countermeasures, such as school closures, the shift to working from home, and social distancing are disrupting economic activity around the world. As with other major economic shocks, there are winners and losers, leading to increased inequality across certain groups. In this project, we investigate the effects of COVID-19 disruptions on the gender gap in academia. We administer a global survey to a broad range of academics across various disciplines to collect nuanced data on the respondents’ circumstances, such as a spouse’s employment, the number and ages of children, and time use. We find that female academics, particularly those who have children, report a disproportionate reduction in time dedicated to research relative to what comparable men and women without children experience. Both men and women report substantial increases in childcare and housework burdens, but women experienced significantly larger increases than men did.
Extracted statement — has research problem: child
Research field: Economics
Title: COVID-19 Disruptions Disproportionately Affect Female Academics
Abstract: The rapid spread of the COVID-19 pandemic and subsequent countermeasures, such as school closures, the shift to working from home, and social distancing are disrupting economic activity around the world. As with other major economic shocks, there are winners and losers, leading to increased inequality across certain groups. In this project, we investigate the effects of COVID-19 disruptions on the gender gap in academia. We administer a global survey to a broad range of academics across various disciplines to collect nuanced data on the respondents’ circumstances, such as a spouse’s employment, the number and ages of children, and time use. We find that female academics, particularly those who have children, report a disproportionate reduction in time dedicated to research relative to what comparable men and women without children experience. Both men and women report substantial increases in childcare and housework burdens, but women experienced significantly larger increases than men did.
Extracted statement — has research problem: childcare
Research field: Economics
Title: COVID-19 Disruptions Disproportionately Affect Female Academics
Abstract: The rapid spread of the COVID-19 pandemic and subsequent countermeasures, such as school closures, the shift to working from home, and social distancing are disrupting economic activity around the world. As with other major economic shocks, there are winners and losers, leading to increased inequality across certain groups. In this project, we investigate the effects of COVID-19 disruptions on the gender gap in academia. We administer a global survey to a broad range of academics across various disciplines to collect nuanced data on the respondents’ circumstances, such as a spouse’s employment, the number and ages of children, and time use. We find that female academics, particularly those who have children, report a disproportionate reduction in time dedicated to research relative to what comparable men and women without children experience. Both men and women report substantial increases in childcare and housework burdens, but women experienced significantly larger increases than men did.
Extracted statement — has research problem: COVID-19 pandemic
Research field: Economics
Title: Enterprise resource planning: An integrative review
Abstract: Enterprise resource planning (ERP) system solutions are currently in high demand by both manufacturing and service organisations because they provide a tightly integrated solution to an organisation's information system needs. During the last decade, ERP systems have received a significant amount of attention from researchers and practitioners from a variety of functional disciplines. In this paper, a comprehensive review of the research literature (1990‐2003) concerning ERP systems is presented. The literature is further classified and the major outcomes of each study are addressed and analysed. Following a comprehensive review of the literature, proposals for future research are formulated to identify topics where fruitful opportunities exist.
Extracted statement — has research problem: Enterprise resource planning
Research field: Economics
Title: COVID-19 Disruptions Disproportionately Affect Female Academics
Abstract: The rapid spread of the COVID-19 pandemic and subsequent countermeasures, such as school closures, the shift to working from home, and social distancing are disrupting economic activity around the world. As with other major economic shocks, there are winners and losers, leading to increased inequality across certain groups. In this project, we investigate the effects of COVID-19 disruptions on the gender gap in academia. We administer a global survey to a broad range of academics across various disciplines to collect nuanced data on the respondents’ circumstances, such as a spouse’s employment, the number and ages of children, and time use. We find that female academics, particularly those who have children, report a disproportionate reduction in time dedicated to research relative to what comparable men and women without children experience. Both men and women report substantial increases in childcare and housework burdens, but women experienced significantly larger increases than men did.
Extracted statement — has research problem: gender
Research field: Economics
Title: COVID-19 Disruptions Disproportionately Affect Female Academics
Abstract: The rapid spread of the COVID-19 pandemic and subsequent countermeasures, such as school closures, the shift to working from home, and social distancing are disrupting economic activity around the world. As with other major economic shocks, there are winners and losers, leading to increased inequality across certain groups. In this project, we investigate the effects of COVID-19 disruptions on the gender gap in academia. We administer a global survey to a broad range of academics across various disciplines to collect nuanced data on the respondents’ circumstances, such as a spouse’s employment, the number and ages of children, and time use. We find that female academics, particularly those who have children, report a disproportionate reduction in time dedicated to research relative to what comparable men and women without children experience. Both men and women report substantial increases in childcare and housework burdens, but women experienced significantly larger increases than men did.
Extracted statement — has research problem: housework
Research field: Economics
Title: COVID-19 Disruptions Disproportionately Affect Female Academics
Abstract: The rapid spread of the COVID-19 pandemic and subsequent countermeasures, such as school closures, the shift to working from home, and social distancing are disrupting economic activity around the world. As with other major economic shocks, there are winners and losers, leading to increased inequality across certain groups. In this project, we investigate the effects of COVID-19 disruptions on the gender gap in academia. We administer a global survey to a broad range of academics across various disciplines to collect nuanced data on the respondents’ circumstances, such as a spouse’s employment, the number and ages of children, and time use. We find that female academics, particularly those who have children, report a disproportionate reduction in time dedicated to research relative to what comparable men and women without children experience. Both men and women report substantial increases in childcare and housework burdens, but women experienced significantly larger increases than men did.
Extracted statement — has research problem: research
Research field: Environmental Health
Title: Towards Consistent Data Representation in the IoT Healthcare Landscape
Abstract: Nowadays, the enormous volume of health and fitness data gathered from IoT wearable devices offers favourable opportunities to the research community. For instance, it can be exploited using sophisticated data analysis techniques, such as automatic reasoning, to find patterns and, extract information and new knowledge in order to enhance decision-making and deliver better healthcare. However, due to the high heterogeneity of data representation formats, the IoT healthcare landscape is characterised by an ubiquitous presence of data silos which prevents users and clinicians from obtaining a consistent representation of the whole knowledge. Semantic web technologies, such as ontologies and inference rules, have been shown as a promising way for the integration and exploitation of data from heterogeneous sources. In this paper, we present a semantic data model useful to: (1) consistently represent health and fitness data from heterogeneous IoT sources; (2) integrate and exchange them; and (3) enable automatic reasoning by inference engines.
Extracted statement — has research problem: consistently represent health and fitness data from heterogeneous IoT sources
Research field: Environmental Health
Title: HealthIoT Ontology for Data Semantic Representation and Interpretation Obtained from Medical Connected Objects
Abstract: Internet of Things (IoT) covers a variety of applications including the Healthcare field. Consequently, medical objects become connected to each other with the purpose to share and exchange health data. These medical connected objects raise issues on how to ensure the analysis, interpretation and semantic interoperability of the extensive obtained health data with the purpose to make an appropriate decision. This paper proposes a HealthIoT ontology for representing the semantic interoperability of the medical connected objects and their data; while an algorithm alleviates the analysis of the detected vital signs and the decision-making of the doctor. The execution of this algorithm needs the definition of several SWRL rules (Semantic Web Rule Language).
Extracted statement — has research problem: semantic interoperability of the medical connected objects and their data