Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Information Science
Title: A streamlined workflow for conversion, peer review, and publication of genomics metadata as omics data papers 
Abstract: Abstract Background Data papers have emerged as a powerful instrument for open data publishing, obtaining credit, and establishing priority for datasets generated in scientific experiments. Academic publishing improves data and metadata quality through peer review and increases the impact of datasets by enhancing their visibility, accessibility, and reusability. Objective We aimed to establish a new type of article structure and template for omics studies: the omics data paper. To improve data interoperability and further incentivize researchers to publish well-described datasets, we created a prototype workflow for streamlined import of genomics metadata from the European Nucleotide Archive directly into a data paper manuscript. Methods An omics data paper template was designed by defining key article sections that encourage the description of omics datasets and methodologies. A metadata import workflow, based on REpresentational State Transfer services and Xpath, was prototyped to extract information from the European Nucleotide Archive, ArrayExpress, and BioSamples databases. Findings The template and workflow for automatic import of standard-compliant metadata into an omics data paper manuscript provide a mechanism for enhancing existing metadata through publishing. Conclusion The omics data paper structure and workflow for import of genomics metadata will help to bring genomic and other omics datasets into the spotlight. Promoting enhanced metadata descriptions and enforcing manuscript peer review and data auditing of the underlying datasets brings additional quality to datasets. We hope that streamlined metadata reuse for scholarly publishing encourages authors to create enhanced metadata descriptions in the form of data papers to improve both the quality of their metadata and its findability and accessibility.
Extracted statement — Data: genomics metadata
Research field: Information Science
Title: A streamlined workflow for conversion, peer review, and publication of genomics metadata as omics data papers 
Abstract: Abstract Background Data papers have emerged as a powerful instrument for open data publishing, obtaining credit, and establishing priority for datasets generated in scientific experiments. Academic publishing improves data and metadata quality through peer review and increases the impact of datasets by enhancing their visibility, accessibility, and reusability. Objective We aimed to establish a new type of article structure and template for omics studies: the omics data paper. To improve data interoperability and further incentivize researchers to publish well-described datasets, we created a prototype workflow for streamlined import of genomics metadata from the European Nucleotide Archive directly into a data paper manuscript. Methods An omics data paper template was designed by defining key article sections that encourage the description of omics datasets and methodologies. A metadata import workflow, based on REpresentational State Transfer services and Xpath, was prototyped to extract information from the European Nucleotide Archive, ArrayExpress, and BioSamples databases. Findings The template and workflow for automatic import of standard-compliant metadata into an omics data paper manuscript provide a mechanism for enhancing existing metadata through publishing. Conclusion The omics data paper structure and workflow for import of genomics metadata will help to bring genomic and other omics datasets into the spotlight. Promoting enhanced metadata descriptions and enforcing manuscript peer review and data auditing of the underlying datasets brings additional quality to datasets. We hope that streamlined metadata reuse for scholarly publishing encourages authors to create enhanced metadata descriptions in the form of data papers to improve both the quality of their metadata and its findability and accessibility.
Extracted statement — Material: genomics metadata
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: heterogeneous RDF data sources
Research field: Information Science
Title: A Systematic Review of Information Literacy Programs in Higher Education: Effects of Face-to-Face, Online, and Blended Formats on Student Skills and Views
Abstract: Objective – Evidence from systematic reviews a decade ago suggested that face-to-face and online methods to provide information literacy training in universities were equally effective in terms of skills learnt, but there was a lack of robust comparative research. The objectives of this review were (1) to update these findings with the inclusion of more recent primary research; (2) to further enhance the summary of existing evidence by including studies of blended formats (with components of both online and face-to-face teaching) compared to single format education; and (3) to explore student views on the various formats employed. Methods – Authors searched seven databases along with a range of supplementary search methods to identify comparative research studies, dated January 1995 to October 2016, exploring skill outcomes for students enrolled in higher education programs. There were 33 studies included, of which 19 also contained comparative data on student views. Where feasible, meta-analyses were carried out to provide summary estimates of skills development and a thematic analysis was completed to identify student views across the different formats. Results – A large majority of studies (27 of 33; 82%) found no statistically significant difference between formats in skills outcomes for students. Of 13 studies that could be included in a meta-analysis, the standardized mean difference (SMD) between skill test results for face-to-face versus online formats was -0.01 (95% confidence interval -0.28 to 0.26). Of ten studies comparing blended to single delivery format, seven (70%) found no statistically significant difference between formats, and the remaining studies had mixed outcomes. From the limited evidence available across all studies, there is a potential dichotomy between outcomes measured via skill test and assignment (course work) which is worthy of further investigation. The thematic analysis of student views found no preference in relation to format on a range of measures in 14 of 19 studies (74%). The remainder identified that students perceived advantages and disadvantages for each format but had no overall preference. Conclusions – There is compelling evidence that information literacy training is effective and well received across a range of delivery formats. Further research looking at blended versus single format methods, and the time implications for each, as well as comparing assignment to skill test outcomes would be valuable. Future studies should adopt a methodologically robust design (such as the randomized controlled trial) with a large student population and validated outcome measures.
Extracted statement — Educational context: Higher Education
Research field: Information Science
Title: The New DBpedia Release Cycle: Increasing Agility and Efficiency in Knowledge Extraction Workflows
Abstract: Abstract Since its inception in 2007, DBpedia has been constantly releasing open data in RDF, extracted from various Wikimedia projects using a complex software system called the DBpedia Information Extraction Framework (DIEF). For the past 12 years, the software received a plethora of extensions by the community, which positively affected the size and data quality. Due to the increase in size and complexity, the release process was facing huge delays (from 12 to 17 months cycle), thus impacting the agility of the development. In this paper, we describe the new DBpedia release cycle including our innovative release workflow, which allows development teams (in particular those who publish large, open data) to implement agile, cost-efficient processes and scale up productivity. The DBpedia release workflow has been re-engineered, its new primary focus is on productivity and agility , to address the challenges of size and complexity. At the same time, quality is assured by implementing a comprehensive testing methodology. We run an experimental evaluation and argue that the implemented measures increase agility and allow for cost-effective quality-control and debugging and thus achieve a higher level of maintainability. As a result, DBpedia now publishes regular (i.e. monthly) releases with over 21 billion triples with minimal publishing effort .
Extracted statement — Data: higher level of maintainability
Research field: Information Science
Title: Active machine learning technique for named entity recognition
Abstract: One difficulty with machine learning for information extraction is the high cost of collecting labeled examples. Active Learning can make more efficient use of the learner's time by asking them to label only instances that are most useful for the trainer. In random sampling approach, unlabeled data is selected for annotation at random and thus can't yield the desired results. In contrast, active learning selects the useful data from a huge pool of unlabeled data for the classifier. The strategies used often classify the corpus tokens (or, data points) into wrong classes. The classifier is confused between two categories if the token is located near the margin. We propose a novel method for solving this problem and show that it favorably results in the increased performance. Our approach is based on the supervised machine learning algorithm, namely Support Vector Machine (SVM). The proposed approach is applied for solving the problem of named entity recognition (NER) in two Indian languages, namely Hindi and Bengali. Results show that proposed active learning based technique indeed improves the performance of the system.
Extracted statement — Language/domain: Hindi and Bengali
Research field: Information Science
Title: The Cooperation Databank
Abstract: <p>Publishing studies using standardized, machine-readable formats will enable machines toperform meta-analyses on-demand. To build a semantically-enhanced technology that embodiesthese functions, we developed the Cooperation Databank (CoDa) – a databank that contains2,641 studies on human cooperation (1958-2017) conducted in 78 countries involving 356,680participants. Experts annotated these studies for 312 variables, including the quantitative results(13, 959 effect sizes). We designed an ontology that defines and relates concepts in cooperationresearch and that can represent the relationships between individual study results. We havecreated a research platform that, based on the dataset, enables users to retrieve studies that testthe relation of variables with cooperation, visualize these study results, and perform (1) metaanalyses, (2) meta-regressions, (3) estimates of publication bias, and (4) statistical poweranalyses for future studies. We leveraged the dataset with visualization tools that allow users toexplore the ontology of concepts in cooperation research and to plot a citation network of thehistory of studies. CoDa offers a vision of how publishing studies in a machine-readable formatcan establish institutions and tools that improve scientific practices and knowledge.</p>
Extracted statement — Domain: Human cooperation
Research field: Information Science
Title: Integrity constraints in OWL
Abstract: In many data-centric semantic web applications, it is desirable to use OWL to encode the Integrity Constraints (IC) that must be satisfied by instance data. However, challenges arise due to the Open World Assumption (OWA) and the lack of a Unique Name Assumption (UNA) in OWL's standard semantics. In particular, conditions that trigger constraint violations in systems using the Closed World Assumption (CWA), will generate new inferences in standard OWL-based reasoning applications. In this paper, we present an alternative IC semantics for OWL that allows applications to work with the CWA and the weak UNA. Ontology modelers can choose which OWL axioms to be interpreted with our IC semantics. Thus application developers are able to combine open world reasoning with closed world constraint validation in a flexible way. We also show that IC validation can be reduced to query answering under certain conditions. Finally, we describe our prototype implementation based on the OWL reasoner Pellet.
Extracted statement — Has result: IC validation can be reduced to query answering
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: increased performance
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: individual scientists
Research field: Information Science
Title: The New DBpedia Release Cycle: Increasing Agility and Efficiency in Knowledge Extraction Workflows
Abstract: Abstract Since its inception in 2007, DBpedia has been constantly releasing open data in RDF, extracted from various Wikimedia projects using a complex software system called the DBpedia Information Extraction Framework (DIEF). For the past 12 years, the software received a plethora of extensions by the community, which positively affected the size and data quality. Due to the increase in size and complexity, the release process was facing huge delays (from 12 to 17 months cycle), thus impacting the agility of the development. In this paper, we describe the new DBpedia release cycle including our innovative release workflow, which allows development teams (in particular those who publish large, open data) to implement agile, cost-efficient processes and scale up productivity. The DBpedia release workflow has been re-engineered, its new primary focus is on productivity and agility , to address the challenges of size and complexity. At the same time, quality is assured by implementing a comprehensive testing methodology. We run an experimental evaluation and argue that the implemented measures increase agility and allow for cost-effective quality-control and debugging and thus achieve a higher level of maintainability. As a result, DBpedia now publishes regular (i.e. monthly) releases with over 21 billion triples with minimal publishing effort .
Extracted statement — Material: large, open data
Research field: Information Science
Title: A Hybrid Knowlegde-Based Approach for Recommending Massive Learning Activities
Abstract: In recent years, the development of recommender systems has attracted increased interest in several domains, especially in e-learning. Massive Open Online Courses have brought a revolution. However, deficiency in support and personalization in this context drive learners to lose their motivation and leave the learning process. To overcome this problem we focus on adapting learning activities to learners' needs using a recommender system.This paper attempts to provide an introduction to different recommender systems for e-learning settings, as well as to present our proposed recommender system for massive learning activities in order to provide learners with the suitable learning activities to follow the learning process and maintain their motivation. We propose a hybrid knowledge-based recommender system based on ontology for recommendation of e-learning activities to learners in the context of MOOCs. In the proposed recommendation approach, ontology is used to model and represent the knowledge about the domain model, learners and learning activities.
Extracted statement — keywords: learning activities
Research field: Information Science
Title: A Hybrid Knowlegde-Based Approach for Recommending Massive Learning Activities
Abstract: In recent years, the development of recommender systems has attracted increased interest in several domains, especially in e-learning. Massive Open Online Courses have brought a revolution. However, deficiency in support and personalization in this context drive learners to lose their motivation and leave the learning process. To overcome this problem we focus on adapting learning activities to learners' needs using a recommender system.This paper attempts to provide an introduction to different recommender systems for e-learning settings, as well as to present our proposed recommender system for massive learning activities in order to provide learners with the suitable learning activities to follow the learning process and maintain their motivation. We propose a hybrid knowledge-based recommender system based on ontology for recommendation of e-learning activities to learners in the context of MOOCs. In the proposed recommendation approach, ontology is used to model and represent the knowledge about the domain model, learners and learning activities.
Extracted statement — Personalisation features: learning activities
Research field: Information Science
Title: Ontology-based E-learning Content Recommender System for Addressing the Pure Cold-start Problem
Abstract: E-learning recommender systems are gaining significance nowadays due to its ability to enhance the learning experience by providing tailor-made services based on learner preferences. A Personalized Learning Environment (PLE) that automatically adapts to learner characteristics such as learning styles and knowledge level can recommend appropriate learning resources that would favor the learning process and improve learning outcomes. The pure cold-start problem is a relevant issue in PLEs, which arises due to the lack of prior information about the new learner in the PLE to create appropriate recommendations. This article introduces a semantic framework based on ontology to address the pure cold-start problem in content recommenders. The ontology encapsulates the domain knowledge about the learners as well as Learning Objects (LOs). The semantic model that we built has been experimented with different combinations of the key learner parameters such as learning style, knowledge level, and background knowledge. The proposed framework utilizes these parameters to build natural learner groups from the learner ontology using SPARQL queries. The ontology holds 480 learners’ data, 468 annotated learning objects with 5,600 learner ratings. A multivariate k-means clustering algorithm, an unsupervised machine learning technique for grouping similar data, is used to evaluate the learner similarity computation accuracy. The learner satisfaction achieved with the proposed model is measured based on the ratings given by the 40 participants of the experiments. From the evaluation perspective, it is evident that 79% of the learners are satisfied with the recommendations generated by the proposed model in pure cold-start condition.
Extracted statement — keywords: learning objects
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: LOD Laudromat, SPARQL endpoints