Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Information Science
Title: Ontology-Based Personalized Course Recommendation Framework
Abstract: Choosing a higher education course at university is not an easy task for students. A wide range of courses are offered by the individual universities whose delivery mode and entry requirements differ. A personalized recommendation system can be an effective way of suggesting the relevant courses to the prospective students. This paper introduces a novel approach that personalizes course recommendations that will match the individual needs of users. The proposed approach developed a framework of an ontology-based hybrid-filtering system called the ontology-based personalized course recommendation (OPCR). This approach aims to integrate the information from multiple sources based on the hierarchical ontology similarity with a view to enhancing the efficiency and the user satisfaction and to provide students with appropriate recommendations. The OPCR combines collaborative-based filtering with content-based filtering. It also considers familiar related concepts that are evident in the profiles of both the student and the course, determining the similarity between them. Furthermore, OPCR uses an ontology mapping technique, recommending jobs that will be available following the completion of each course. This method can enable students to gain a comprehensive knowledge of courses based on their relevance, using dynamic ontology mapping to link the course profiles and student profiles with job profiles. Results show that a filtering algorithm that uses hierarchically related concepts produces better outcomes compared to a filtering method that considers only keyword similarity. In addition, the quality of the recommendations is improved when the ontology similarity between the items’ and the users’ profiles were utilized. This approach, using a dynamic ontology mapping, is flexible and can be adapted to different domains. The proposed framework can be used to filter the items for both postgraduate courses and items from other domains.
Extracted statement — Development in: OWL
Research field: Information Science
Title: OntoIMM: An Ontology for Product Intelligent Master Model
Abstract: Information organizing principle is one of the key issues of intelligent master model (IMM), which is an enhancement of the master model (MM) based on KBE (knowledge-based engineering). Despite the fact that the core product model (CPM) has been confirmed to be an organizing mechanism for product master model, the key issue of supporting the information organizing for IMM is not yet well addressed, mainly due to the following two reasons; (1) lack of representation of complete information and knowledge with regard to product and process, including the know-why, know-how, and know-what information and knowledge, and (2) lack of semantic richness. Therefore, a multiaspect extension to CPM was first defined, and then an ontology was constructed to represent the information and design knowledge. The extension refers to adding a design process model, context model, product control structure model, and design rationale model to CPM concerning the enhancement of master model, which is to comprehensively represent the reason, process, and result information and knowledge of theproduct. The ontology construction refers to representing the concepts, relationships among these concepts and consistency rules of IMM information structure. Finally, an example of barrel design and analysis process is illustrated to verify the effectiveness of proposed method.
Extracted statement — hasRepresentationMasterData: OWL
Research field: Information Science
Title: Coronavirus disease 2019 (COVID-19) surveillance system: Development of COVID-19 minimum data set and interoperable reporting framework
Abstract: INTRODUCTION: The 2019 coronavirus disease (COVID-19) is a major global health concern. Joint efforts for effective surveillance of COVID-19 require immediate transmission of reliable data. In this regard, a standardized and interoperable reporting framework is essential in a consistent and timely manner. Thus, this research aimed at to determine data requirements towards interoperability. MATERIALS AND METHODS: In this cross-sectional and descriptive study, a combination of literature study and expert consensus approach was used to design COVID-19 Minimum Data Set (MDS). A MDS checklist was extracted and validated. The definitive data elements of the MDS were determined by applying the Delphi technique. Then, the existing messaging and data standard templates (Health Level Seven-Clinical Document Architecture [HL7-CDA] and SNOMED-CT) were used to design the surveillance interoperable framework. RESULTS: The proposed MDS was divided into administrative and clinical sections with three and eight data classes and 29 and 40 data fields, respectively. Then, for each data field, structured data values along with SNOMED-CT codes were defined and structured according HL7-CDA standard. DISCUSSION AND CONCLUSION: The absence of effective and integrated system for COVID-19 surveillance can delay critical public health measures, leading to increased disease prevalence and mortality. The heterogeneity of reporting templates and lack of uniform data sets hamper the optimal information exchange among multiple systems. Thus, developing a unified and interoperable reporting framework is more effective to prompt reaction to the COVID-19 outbreak.
Extracted statement — Epidemiological surveillance software: SNOMED
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 — Database: CoDa
Research field: Information Science
Title: Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction
Abstract: We introduce a multi-task setup of identifying entities, relations, and coreference clusters in scientific articles. We create SciERC, a dataset that includes annotations for all three tasks and develop a unified framework called SciIE with shared span representations. The multi-task setup reduces cascading errors between tasks and leverages cross-sentence relations through coreference links. Experiments show that our multi-task model outperforms previous models in scientific information extraction without using any domain-specific features. We further show that the framework supports construction of a scientific knowledge graph, which we use to analyze information in scientific literature.
Extracted statement — Dataset name: SciERC
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 — Has method: xpath
Research field: Information Science
Title: COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology
Abstract: Abstract Summary The COVID-19 crisis has elicited a global response by the scientific community that has led to a burst of publications on the pathophysiology of the virus. However, without coordinated efforts to organize this knowledge, it can remain hidden away from individual research groups. By extracting and formalizing this knowledge in a structured and computable form, as in the form of a knowledge graph, researchers can readily reason and analyze this information on a much larger scale. Here, we present the COVID-19 Knowledge Graph, an expansive cause-and-effect network constructed from scientific literature on the new coronavirus that aims to provide a comprehensive view of its pathophysiology. To make this resource available to the research community and facilitate its exploration and analysis, we also implemented a web application and released the KG in multiple standard formats. Availability and implementation The COVID-19 Knowledge Graph is publicly available under CC-0 license at https://github.com/covid19kg and https://bikmi.covid19-knowledgespace.de. Supplementary information Supplementary data are available at Bioinformatics online.
Extracted statement — Domain: COVID-19
Research field: Information Science
Title: Persistent Identification of Instruments
Abstract: Instruments play an essential role in creating research data. Given the importance of instruments and associated metadata to the assessment of data quality and data reuse, globally unique, persistent and resolvable identification of instruments is crucial. The Research Data Alliance Working Group Persistent Identification of Instruments (PIDINST) developed a community-driven solution for persistent identification of instruments which we present and discuss in this paper. Based on an analysis of 10 use cases, PIDINST developed a metadata schema and prototyped schema implementation with DataCite and ePIC as representative persistent identifier infrastructures and with HZB (Helmholtz-Zentrum Berlin fur Materialien und Energie) and BODC (British Oceanographic Data Centre) as representative institutional instrument providers. These implementations demonstrate the viability of the proposed solution in practice. Moving forward, PIDINST will further catalyse adoption and consolidate the schema by addressing new stakeholder requirements.
Extracted statement — uses identifier system: ePIC
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 software: ETR.net
Research field: Information Science
Title: TeKET: a Tree-Based Unsupervised Keyphrase Extraction Technique 
Abstract: Abstract Automatic keyphrase extraction techniques aim to extract quality keyphrases for higher level summarization of a document. Majority of the existing techniques are mainly domain-specific, which require application domain knowledge and employ higher order statistical methods, and computationally expensive and require large train data, which is rare for many applications. Overcoming these issues, this paper proposes a new unsupervised keyphrase extraction technique. The proposed unsupervised keyphrase extraction technique, named TeKET or Tree-based Keyphrase Extraction Technique , is a domain-independent technique that employs limited statistical knowledge and requires no train data. This technique also introduces a new variant of a binary tree, called KeyPhrase Extraction ( KePhEx ) tree, to extract final keyphrases from candidate keyphrases. In addition, a measure, called Cohesiveness Index or CI , is derived which denotes a given node’s degree of cohesiveness with respect to the root. The CI is used in flexibly extracting final keyphrases from the KePhEx tree and is co-utilized in the ranking process. The effectiveness of the proposed technique and its domain and language independence are experimentally evaluated using available benchmark corpora, namely SemEval-2010 (a scientific articles dataset), Theses100 (a thesis dataset), and a German Research Article dataset, respectively. The acquired results are compared with other relevant unsupervised techniques belonging to both statistical and graph-based techniques. The obtained results demonstrate the improved performance of the proposed technique over other compared techniques in terms of precision, recall, and F1 scores.
Extracted statement — Dataset name: SemEval-2010
Research field: Information Science
Title: Fast and accurate entity recognition with iterated dilated convolutions
Abstract: Today when many practitioners run basic NLP on the entire web and large-volume traffic, faster methods are paramount to saving time and energy costs. Recent advances in GPU hardware have led to the emergence of bi-directional LSTMs as a standard method for obtaining per-token vector representations serving as input to labeling tasks such as NER (often followed by prediction in a linear-chain CRF). Though expressive and accurate, these models fail to fully exploit GPU parallelism, limiting their computational efficiency. This paper proposes a faster alternative to Bi-LSTMs for NER: Iterated Dilated Convolutional Neural Networks (ID-CNNs), which have better capacity than traditional CNNs for large context and structured prediction. Unlike LSTMs whose sequential processing on sentences of length N requires O(N) time even in the face of parallelism, ID-CNNs permit fixed-depth convolutions to run in parallel across entire documents. We describe a distinct combination of network structure, parameter sharing and training procedures that enable dramatic 14-20x test-time speedups while retaining accuracy comparable to the Bi-LSTM-CRF. Moreover, ID-CNNs trained to aggregate context from the entire document are more accurate than Bi-LSTM-CRFs while attaining 8x faster test time speeds.
Extracted statement — has research problem: NER
Research field: Information Science
Title: Using DevOps Principles to Continuously Monitor RDF Data Quality
Abstract: One approach to continuously achieve a certain data quality level is to use an integration pipeline that continuously checks and monitors the quality of a data set according to defined metrics. This approach is inspired by Continuous Integration pipelines, that have been introduced in the area of software development and DevOps to perform continuous source code checks. By investigating in possible tools to use and discussing the specific requirements for RDF data sets, an integration pipeline is derived that joins current approaches of the areas of software-development and semantic-web as well as reuses existing tools. As these tools have not been built explicitly for CI usage, we evaluate their usability and propose possible workarounds and improvements. Furthermore, a real-world usage scenario is discussed, outlining the benefit of the usage of such a pipeline.
Extracted statement — has research problem: RDF
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 — has research problem: DBPedia
Research field: Information Systems, Process and Knowledge Management
Title: SoMeSci- A 5 Star Open Data Gold Standard Knowledge Graph of Software Mentions in Scientific Articles
Abstract: Knowledge about software used in scientific investigations is important for several reasons, for instance, to enable an understanding of provenance and methods involved in data handling. However, software is usually not formally cited, but rather mentioned informally within the scholarly description of the investigation, raising the need for automatic information extraction and disambiguation. Given the lack of reliable ground truth data, we present SoMeSci-Software Mentions in Science-a gold standard knowledge graph of software mentions in scientific articles. It contains high quality annotations (IRR: K=.82) of 3756 software mentions in 1367 PubMed Central articles. Besides the plain mention of the software, we also provide relation labels for additional information, such as the version, the developer, a URL or citations. Moreover, we distinguish between different types, such as application, plugin or programming environment, as well as different types of mentions, such as usage or creation. To the best of our knowledge, SoMeSci is the most comprehensive corpus about software mentions in scientific articles, providing training samples for Named Entity Recognition, Relation Extraction, Entity Disambiguation, and Entity Linking. Finally, we sketch potential use cases and provide baseline results.
Extracted statement — Relation types: URL
Research field: Life Sciences
Title: Estimative of real number of infections by COVID-19 in Brazil and possible scenarios
Abstract: Abstract This paper attempts to provide methods to estimate the real scenario of the novel coronavirus pandemic crisis on Brazil and the states of Sao Paulo, Pernambuco, Espirito Santo, Amazonas and Distrito Federal. By the use of a SEIRD mathematical model with age division, we predict the infection and death curve, stating the peak date for Brazil and these states. We also carry out a prediction for the ICU demand on these states for a visualization of the size of a possible collapse on the local health system. By the end, we establish some future scenarios including the stopping of social isolation and the introduction of vaccines and efficient medicine against the virus.
Extracted statement — Used models: SEIRD
Research field: Life Sciences
Title: Supplementation with vitamin D in the COVID-19 pandemic?
Abstract: Abstract The coronavirus disease 2019 (COVID-19) pandemic was declared a public health emergency of international concern by the World Health Organization. COVID-19 has high transmissibility and could result in acute lung injury in a fraction of patients. By counterbalancing the activity of the renin-angiotensin system, angiotensin-converting enzyme 2, which is the fusion receptor of the virus, plays a protective role against the development of complications of this viral infection. Vitamin D can induce the expression of angiotensin-converting enzyme 2 and regulate the immune system through different mechanisms. Epidemiologic studies of the relationship between vitamin D and various respiratory infections were reviewed and, here, the postulated mechanisms and clinical data supporting the protective role of vitamin D against COVID-19–mediated complications are discussed.
Extracted statement — has research problem: COVID-19