Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Information Science
Title: OpenBiodiv: A Knowledge Graph for Literature-Extracted Linked Open Data in Biodiversity Science
Abstract: Hundreds of years of biodiversity research have resulted in the accumulation of a substantial pool of communal knowledge; however, most of it is stored in silos isolated from each other, such as published articles or monographs. The need for a system to store and manage collective biodiversity knowledge in a community-agreed and interoperable open format has evolved into the concept of the Open Biodiversity Knowledge Management System (OBKMS). This paper presents OpenBiodiv: An OBKMS that utilizes semantic publishing workflows, text and data mining, common standards, ontology modelling and graph database technologies to establish a robust infrastructure for managing biodiversity knowledge. It is presented as a Linked Open Dataset generated from scientific literature. OpenBiodiv encompasses data extracted from more than 5000 scholarly articles published by Pensoft and many more taxonomic treatments extracted by Plazi from journals of other publishers. The data from both sources are converted to Resource Description Framework (RDF) and integrated in a graph database using the OpenBiodiv-O ontology and an RDF version of the Global Biodiversity Information Facility (GBIF) taxonomic backbone. Through the application of semantic technologies, the project showcases the value of open publishing of Findable, Accessible, Interoperable, Reusable (FAIR) data towards the establishment of open science practices in the biodiversity domain.
Extracted statement — Material: taxonomic treatments
Research field: Information Science
Title: Evaluating the electronic tuberculosis register surveillance system in Eden District, Western Cape, South Africa, 2015
Abstract: ABSTRACT Background: Tuberculosis (TB) surveillance data are crucial to the effectiveness of National TB Control Programs. In South Africa, few surveillance system evaluations have been undertaken to provide a rigorous assessment of the platform from which the national and district health systems draws data to inform programs and policies. Objective: Evaluate the attributes of Eden District’s TB surveillance system, Western Cape Province, South Africa. Methods: Data quality, sensitivity and positive predictive value were assessed using secondary data from 40,033 TB cases entered in Eden District’s ETR.Net from 2007 to 2013, and 79 purposively selected TB Blue Cards (TBCs), a medical patient file and source document for data entered into ETR.Net. Simplicity, flexibility, acceptability, stability and usefulness of the ETR.Net were assessed qualitatively through interviews with TB nurses, information health officers, sub-district and district coordinators involved in the TB surveillance. Results: TB surveillance system stakeholders report that Eden District’s ETR.Net system was simple, acceptable, flexible and stable, and achieves its objective of informing TB control program, policies and activities. Data were less complete in the ETR.Net (66–100%) than in the TBCs (76–100%), and concordant for most variables except pre-treatment smear results, antiretroviral therapy (ART) and treatment outcome. The sensitivity of recorded variables in ETR.Net was 98% for gender, 97% for patient category, 93% for ART, 92% for treatment outcome and 90% for pre-treatment smear grading. Conclusions: Our results reveal that the system provides useful information to guide TB control program activities in Eden District. However, urgent attention is needed to address gaps in clinical recording on the TBC and data capturing into the ETR.Net system. We recommend continuous training and support of TB personnel involved with TB care, management and surveillance on TB data recording into the TBCs and ETR.Net as well as the implementation of a well-structured quality control and assurance system.
Extracted statement — Epidemiological surveillance users: TB nurses
Research field: Information Science
Title: OpenBiodiv: A Knowledge Graph for Literature-Extracted Linked Open Data in Biodiversity Science
Abstract: Hundreds of years of biodiversity research have resulted in the accumulation of a substantial pool of communal knowledge; however, most of it is stored in silos isolated from each other, such as published articles or monographs. The need for a system to store and manage collective biodiversity knowledge in a community-agreed and interoperable open format has evolved into the concept of the Open Biodiversity Knowledge Management System (OBKMS). This paper presents OpenBiodiv: An OBKMS that utilizes semantic publishing workflows, text and data mining, common standards, ontology modelling and graph database technologies to establish a robust infrastructure for managing biodiversity knowledge. It is presented as a Linked Open Dataset generated from scientific literature. OpenBiodiv encompasses data extracted from more than 5000 scholarly articles published by Pensoft and many more taxonomic treatments extracted by Plazi from journals of other publishers. The data from both sources are converted to Resource Description Framework (RDF) and integrated in a graph database using the OpenBiodiv-O ontology and an RDF version of the Global Biodiversity Information Facility (GBIF) taxonomic backbone. Through the application of semantic technologies, the project showcases the value of open publishing of Findable, Accessible, Interoperable, Reusable (FAIR) data towards the establishment of open science practices in the biodiversity domain.
Extracted statement — Material: the biodiversity domain
Research field: Information Science
Title: More Complete Resultset Retrieval from Large Heterogeneous RDF Sources
Abstract: Over the last years, the Web of Data has grown significantly. Various interfaces such as LOD Stats, LOD Laudromat, SPARQL endpoints provide access to the hundered of thousands of RDF datasets, representing billions of facts. These datasets are available in different formats such as raw data dumps and HDT files or directly accessible via SPARQL endpoints. Querying such large amount of distributed data is particularly challenging and many of these datasets cannot be directly queried using the SPARQL query language. In order to tackle these problems, we present WimuQ, an integrated query engine to execute SPARQL queries and retrieve results from large amount of heterogeneous RDF data sources. Presently, WimuQ is able to execute both federated and non-federated SPARQL queries over a total of 668,166 datasets from LOD Stats and LOD Laudromat as well as 559 active SPARQL endpoints. These data sources represent a total of 221.7 billion triples from more than 5 terabytes of information from datasets retrieved using the service "Where is My URI" (WIMU). Our evaluation on state-of-the-art real-data benchmarks shows that WimuQ retrieves more complete results for the benchmark queries.
Extracted statement — Material: the hundered of thousands of RDF datasets
Research field: Information Science
Title: OpenBiodiv: A Knowledge Graph for Literature-Extracted Linked Open Data in Biodiversity Science
Abstract: Hundreds of years of biodiversity research have resulted in the accumulation of a substantial pool of communal knowledge; however, most of it is stored in silos isolated from each other, such as published articles or monographs. The need for a system to store and manage collective biodiversity knowledge in a community-agreed and interoperable open format has evolved into the concept of the Open Biodiversity Knowledge Management System (OBKMS). This paper presents OpenBiodiv: An OBKMS that utilizes semantic publishing workflows, text and data mining, common standards, ontology modelling and graph database technologies to establish a robust infrastructure for managing biodiversity knowledge. It is presented as a Linked Open Dataset generated from scientific literature. OpenBiodiv encompasses data extracted from more than 5000 scholarly articles published by Pensoft and many more taxonomic treatments extracted by Plazi from journals of other publishers. The data from both sources are converted to Resource Description Framework (RDF) and integrated in a graph database using the OpenBiodiv-O ontology and an RDF version of the Global Biodiversity Information Facility (GBIF) taxonomic backbone. Through the application of semantic technologies, the project showcases the value of open publishing of Findable, Accessible, Interoperable, Reusable (FAIR) data towards the establishment of open science practices in the biodiversity domain.
Extracted statement — method: the OpenBiodiv-O ontology
Research field: Information Science
Title: Curating Scientific Information in Knowledge Infrastructures
Abstract: Interpreting observational data is a fundamental task in the sciences, specifically in earth and environmental science where observational data are increasingly acquired, curated, and published systematically by environmental research infrastructures. Typically subject to substantial processing, observational data are used by research communities, their research groups and individual scientists, who interpret such primary data for their meaning in the context of research investigations. The result of interpretation is information – meaningful secondary or derived data – about the observed environment. Research infrastructures and research communities are thus essential to evolving uninterpreted observational data to information. In digital form, the classical bearer of information are the commonly known “(elaborated) data products,” for instance maps. In such form, meaning is generally implicit e.g., in map colour coding, and thus largely inaccessible to machines. The systematic acquisition, curation, possible publishing and further processing of information gained in observational data interpretation – as machine readable data and their machine-readable meaning – is not common practice among environmental research infrastructures. For a use case in aerosol science, we elucidate these problems and present a Jupyter based prototype infrastructure that exploits a machine learning approach to interpretation and could support a research community in interpreting observational data and, more importantly, in curating and further using resulting information about a studied natural phenomenon.
Extracted statement — Material: the sciences
Research field: Information Science
Title: More Complete Resultset Retrieval from Large Heterogeneous RDF Sources
Abstract: Over the last years, the Web of Data has grown significantly. Various interfaces such as LOD Stats, LOD Laudromat, SPARQL endpoints provide access to the hundered of thousands of RDF datasets, representing billions of facts. These datasets are available in different formats such as raw data dumps and HDT files or directly accessible via SPARQL endpoints. Querying such large amount of distributed data is particularly challenging and many of these datasets cannot be directly queried using the SPARQL query language. In order to tackle these problems, we present WimuQ, an integrated query engine to execute SPARQL queries and retrieve results from large amount of heterogeneous RDF data sources. Presently, WimuQ is able to execute both federated and non-federated SPARQL queries over a total of 668,166 datasets from LOD Stats and LOD Laudromat as well as 559 active SPARQL endpoints. These data sources represent a total of 221.7 billion triples from more than 5 terabytes of information from datasets retrieved using the service "Where is My URI" (WIMU). Our evaluation on state-of-the-art real-data benchmarks shows that WimuQ retrieves more complete results for the benchmark queries.
Extracted statement — Material: the SPARQL query language
Research field: Information Science
Title: More Complete Resultset Retrieval from Large Heterogeneous RDF Sources
Abstract: Over the last years, the Web of Data has grown significantly. Various interfaces such as LOD Stats, LOD Laudromat, SPARQL endpoints provide access to the hundered of thousands of RDF datasets, representing billions of facts. These datasets are available in different formats such as raw data dumps and HDT files or directly accessible via SPARQL endpoints. Querying such large amount of distributed data is particularly challenging and many of these datasets cannot be directly queried using the SPARQL query language. In order to tackle these problems, we present WimuQ, an integrated query engine to execute SPARQL queries and retrieve results from large amount of heterogeneous RDF data sources. Presently, WimuQ is able to execute both federated and non-federated SPARQL queries over a total of 668,166 datasets from LOD Stats and LOD Laudromat as well as 559 active SPARQL endpoints. These data sources represent a total of 221.7 billion triples from more than 5 terabytes of information from datasets retrieved using the service "Where is My URI" (WIMU). Our evaluation on state-of-the-art real-data benchmarks shows that WimuQ retrieves more complete results for the benchmark queries.
Extracted statement — Material: the Web of Data
Research field: Information Science
Title: Curating Scientific Information in Knowledge Infrastructures
Abstract: Interpreting observational data is a fundamental task in the sciences, specifically in earth and environmental science where observational data are increasingly acquired, curated, and published systematically by environmental research infrastructures. Typically subject to substantial processing, observational data are used by research communities, their research groups and individual scientists, who interpret such primary data for their meaning in the context of research investigations. The result of interpretation is information – meaningful secondary or derived data – about the observed environment. Research infrastructures and research communities are thus essential to evolving uninterpreted observational data to information. In digital form, the classical bearer of information are the commonly known “(elaborated) data products,” for instance maps. In such form, meaning is generally implicit e.g., in map colour coding, and thus largely inaccessible to machines. The systematic acquisition, curation, possible publishing and further processing of information gained in observational data interpretation – as machine readable data and their machine-readable meaning – is not common practice among environmental research infrastructures. For a use case in aerosol science, we elucidate these problems and present a Jupyter based prototype infrastructure that exploits a machine learning approach to interpretation and could support a research community in interpreting observational data and, more importantly, in curating and further using resulting information about a studied natural phenomenon.
Extracted statement — Material: their research groups
Research field: Information Science
Title: The Pattern of Patterns: What is a pattern in conceptual modeling?
Abstract: It has been proven that using structured methods to represent the domain reduces human errors in the process of creating models and also in the process of using them. Using modeling patterns is a proven structural method in this regard. A pattern is a generalizable reusable solution to a design problem. Positive effects of using patterns were demonstrated in several experimental studies and explained using theories. However, detailed knowledge about how properties of patterns lead to increased performance in writing and reading conceptual models is currently lacking. This paper proposes a theoretical framework to characterize the properties of ontology-driven conceptual model patterns. The development of such framework is the first step in investigating the effects of pattern properties and devising rules to compose patterns based on well-understood properties.
Extracted statement — Material: theoretical framework
Research field: Information Science
Title: More Complete Resultset Retrieval from Large Heterogeneous RDF Sources
Abstract: Over the last years, the Web of Data has grown significantly. Various interfaces such as LOD Stats, LOD Laudromat, SPARQL endpoints provide access to the hundered of thousands of RDF datasets, representing billions of facts. These datasets are available in different formats such as raw data dumps and HDT files or directly accessible via SPARQL endpoints. Querying such large amount of distributed data is particularly challenging and many of these datasets cannot be directly queried using the SPARQL query language. In order to tackle these problems, we present WimuQ, an integrated query engine to execute SPARQL queries and retrieve results from large amount of heterogeneous RDF data sources. Presently, WimuQ is able to execute both federated and non-federated SPARQL queries over a total of 668,166 datasets from LOD Stats and LOD Laudromat as well as 559 active SPARQL endpoints. These data sources represent a total of 221.7 billion triples from more than 5 terabytes of information from datasets retrieved using the service "Where is My URI" (WIMU). Our evaluation on state-of-the-art real-data benchmarks shows that WimuQ retrieves more complete results for the benchmark queries.
Extracted statement — Material: These data sources
Research field: Information Science
Title: More Complete Resultset Retrieval from Large Heterogeneous RDF Sources
Abstract: Over the last years, the Web of Data has grown significantly. Various interfaces such as LOD Stats, LOD Laudromat, SPARQL endpoints provide access to the hundered of thousands of RDF datasets, representing billions of facts. These datasets are available in different formats such as raw data dumps and HDT files or directly accessible via SPARQL endpoints. Querying such large amount of distributed data is particularly challenging and many of these datasets cannot be directly queried using the SPARQL query language. In order to tackle these problems, we present WimuQ, an integrated query engine to execute SPARQL queries and retrieve results from large amount of heterogeneous RDF data sources. Presently, WimuQ is able to execute both federated and non-federated SPARQL queries over a total of 668,166 datasets from LOD Stats and LOD Laudromat as well as 559 active SPARQL endpoints. These data sources represent a total of 221.7 billion triples from more than 5 terabytes of information from datasets retrieved using the service "Where is My URI" (WIMU). Our evaluation on state-of-the-art real-data benchmarks shows that WimuQ retrieves more complete results for the benchmark queries.
Extracted statement — Material: These datasets
Research field: Information Science
Title: A Survey of Scholarly Data Visualization
Abstract: Scholarly information usually contains millions of raw data, such as authors, papers, citations, as well as scholarly networks. With the rapid growth of the digital publishing and harvesting, how to visually present the data efficiently becomes challenging. Nowadays, various visualization techniques can be easily applied on scholarly data visualization and visual analysis, which enables scientists to have a better way to represent the structure of scholarly data sets and reveal hidden patterns in the data. In this paper, we first introduce the basic concepts and the collection of scholarly data. Then, we provide a comprehensive overview of related data visualization tools, existing techniques, as well as systems for the analyzing volumes of diverse scholarly data. Finally, open issues are discussed to pursue new solutions for abundant and complicated scholarly data visualization, as well as techniques, that support a multitude of facets.
Extracted statement — has elements: Visualization tools
Research field: Information Science
Title: Applying weighted PageRank to author citation networks
Abstract: This article aims to identify whether different weighted PageRank algorithms can be applied to author citation networks to measure the popularity and prestige of a scholar from a citation perspective. Information retrieval (IR) was selected as a test field and data from 1956–2008 were collected from Web of Science. Weighted PageRank with citation and publication as weighted vectors were calculated on author citation networks. The results indicate that both popularity rank and prestige rank were highly correlated with the weighted PageRank. Principal component analysis was conducted to detect relationships among these different measures. For capturing prize winners within the IR field, prestige rank outperformed all the other measures. © 2011 Wiley Periodicals, Inc.
Extracted statement — Bibliographic data source: Web of Science
Research field: Information Science
Title: Applying weighted PageRank to author citation networks
Abstract: This article aims to identify whether different weighted PageRank algorithms can be applied to author citation networks to measure the popularity and prestige of a scholar from a citation perspective. Information retrieval (IR) was selected as a test field and data from 1956–2008 were collected from Web of Science. Weighted PageRank with citation and publication as weighted vectors were calculated on author citation networks. The results indicate that both popularity rank and prestige rank were highly correlated with the weighted PageRank. Principal component analysis was conducted to detect relationships among these different measures. For capturing prize winners within the IR field, prestige rank outperformed all the other measures. © 2011 Wiley Periodicals, Inc.
Extracted statement — Social network analysis: Weighted PageRank
Research field: Information Science
Title: The Pattern of Patterns: What is a pattern in conceptual modeling?
Abstract: It has been proven that using structured methods to represent the domain reduces human errors in the process of creating models and also in the process of using them. Using modeling patterns is a proven structural method in this regard. A pattern is a generalizable reusable solution to a design problem. Positive effects of using patterns were demonstrated in several experimental studies and explained using theories. However, detailed knowledge about how properties of patterns lead to increased performance in writing and reading conceptual models is currently lacking. This paper proposes a theoretical framework to characterize the properties of ontology-driven conceptual model patterns. The development of such framework is the first step in investigating the effects of pattern properties and devising rules to compose patterns based on well-understood properties.
Extracted statement — Data: well-understood properties
Research field: Information Science
Title: The Pattern of Patterns: What is a pattern in conceptual modeling?
Abstract: It has been proven that using structured methods to represent the domain reduces human errors in the process of creating models and also in the process of using them. Using modeling patterns is a proven structural method in this regard. A pattern is a generalizable reusable solution to a design problem. Positive effects of using patterns were demonstrated in several experimental studies and explained using theories. However, detailed knowledge about how properties of patterns lead to increased performance in writing and reading conceptual models is currently lacking. This paper proposes a theoretical framework to characterize the properties of ontology-driven conceptual model patterns. The development of such framework is the first step in investigating the effects of pattern properties and devising rules to compose patterns based on well-understood properties.
Extracted statement — Material: writing and reading conceptual models