Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Systems and Integrative Physiology
Title: Pilot Study to Estimate "Difficult" Area in e-Learning Material by Physiological Measurements
Abstract: To improve designs of e-learning materials, it is necessary to know which word or figure a learner felt "difficult" in the materials. In this pilot study, we measured electroencephalography (EEG) and eye gaze data of learners and analyzed to estimate which area they had difficulty to learn. The developed system realized simultaneous measurements of physiological data and subjective evaluations during learning. Using this system, we observed specific EEG activity in difficult pages. Integrating of eye gaze and EEG measurements raised a possibility to determine where a learner felt "difficult" in a page of learning materials. From these results, we could suggest that the multimodal measurements of EEG and eye gaze would lead to effective improvement of learning materials. For future study, more data collection using various materials and learners with different backgrounds is necessary. This study could lead to establishing a method to improve e-learning materials based on learners' mental states.
Extracted statement — Data: electroencephalography (EEG) and eye gaze data
Research field: Systems and Integrative Physiology
Title: Pilot Study to Estimate "Difficult" Area in e-Learning Material by Physiological Measurements
Abstract: To improve designs of e-learning materials, it is necessary to know which word or figure a learner felt "difficult" in the materials. In this pilot study, we measured electroencephalography (EEG) and eye gaze data of learners and analyzed to estimate which area they had difficulty to learn. The developed system realized simultaneous measurements of physiological data and subjective evaluations during learning. Using this system, we observed specific EEG activity in difficult pages. Integrating of eye gaze and EEG measurements raised a possibility to determine where a learner felt "difficult" in a page of learning materials. From these results, we could suggest that the multimodal measurements of EEG and eye gaze would lead to effective improvement of learning materials. For future study, more data collection using various materials and learners with different backgrounds is necessary. This study could lead to establishing a method to improve e-learning materials based on learners' mental states.
Extracted statement — Material: various materials and learners with different backgrounds
Research field: Theory, Knowledge and Science
Title: Knowledge Graphs in Manufacturing and Production: A Systematic Literature Review
Abstract: Knowledge graphs in manufacturing and production aim to make production lines more efficient and flexible with higher quality output. This makes knowledge graphs attractive for companies to reach Industry 4.0 goals. However, existing research in the field is quite preliminary, and more research effort on analyzing how knowledge graphs can be applied in the field of manufacturing and production is needed. Therefore, we have conducted a systematic literature review as an attempt to characterize the state-of-the-art in this field, i.e., by identifying existing research and by identifying gaps and opportunities for further research. We have focused on finding the primary studies in the existing literature, which were classified and analyzed according to four criteria: bibliometric key facts, research type facets, knowledge graph characteristics, and application scenarios. Besides, an evaluation of the primary studies has also been carried out to gain deeper insights in terms of methodology, empirical evidence, and relevance. As a result, we can offer a complete picture of the domain, which includes such interesting aspects as the fact that knowledge fusion is currently the main use case for knowledge graphs, that empirical research and industrial application are still missing to a large extent, that graph embeddings are not fully exploited, and that technical literature is fast-growing but still seems to be far from its peak.
Extracted statement — Has result:  knowledge fusion is currently the main use case for knowledge graphs,
Research field: Theory, Knowledge and Science
Title: A Survey on Knowledge Graphs: Representation, Acquisition and Applications
Abstract: Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction toward cognition and human-level intelligence. In this survey, we provide a comprehensive review of the knowledge graph covering overall research topics about: 1) knowledge graph representation learning; 2) knowledge acquisition and completion; 3) temporal knowledge graph; and 4) knowledge-aware applications and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning are reviewed. We further explore several emerging topics, including metarelational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of data sets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions.
Extracted statement — Has method:  knowledge graph completion, embedding methods, path inference, and logical rule reasoning
Research field: Theory, Knowledge and Science
Title: Virtual Knowledge Graphs: An Overview of Systems and Use Cases.
Abstract: In this paper, we present the virtual knowledge graph (VKG) paradigm for data integration and access, also known in the literature as Ontology-based Data Access. Instead of structuring the integration layer as a collection of relational tables, the VKG paradigm replaces the rigid structure of tables with the flexibility of graphs that are kept virtual and embed domain knowledge. We explain the main notions of this paradigm, its tooling ecosystem and significant use cases in a wide range of applications. Finally, we discuss future research directions.
Extracted statement — Has method: VKG paradigm replaces the rigid structure of tables with the flexibility of graphs
Research field: Virology
Title: Analysis of the epidemic growth of the early 2019-nCoV outbreak using internationally confirmed cases
Abstract: Background: On January 23, 2020, a quarantine was imposed on travel in and out of Wuhan, where the 2019 novel coronavirus (2019-nCoV) outbreak originated from. Previous analyses estimated the basic epidemiological parameters using symptom onset dates of the confirmed cases in Wuhan and outside China. Methods: We obtained information on the 46 coronavirus cases who traveled from Wuhan before January 23 and have been subsequently confirmed in Hong Kong, Japan, Korea, Macau, Singapore, and Taiwan as of February 5, 2020. Most cases have detailed travel history and disease progress. Compared to previous analyses, an important distinction is that we used this data to informatively simulate the infection time of each case using the symptom onset time, previously reported incubation interval, and travel history. We then fitted a simple exponential growth model with adjustment for the January 23 travel ban to the distribution of the simulated infection time. We used a Bayesian analysis with diffuse priors to quantify the uncertainty of the estimated epidemiological parameters. We performed sensitivity analysis to different choices of incubation interval and the hyperparameters in the prior specification. Results: We found that our model provides good fit to the distribution of the infection time. Assuming the travel rate to the selected countries and regions is constant over the study period, we found that the epidemic was doubling in size every 2.9 days (95% credible interval [CrI], 2 days--4.1 days). Using previously reported serial interval for 2019-nCoV, the estimated basic reproduction number is 5.7 (95% CrI, 3.4--9.2). The estimates did not change substantially if we assumed the travel rate doubled in the last 3 days before January 23, when we used previously reported incubation interval for severe acute respiratory syndrome (SARS), or when we changed the hyperparameters in our prior specification. Conclusions: Our estimated epidemiological parameters are higher than an earlier report using confirmed cases in Wuhan. This indicates the 2019-nCoV could have been spreading faster than previous estimates.
Extracted statement — location: Hong Kong, Japan, Korea, Macau, Singapore, and Taiwan
Research field: Work, Economy and Organizations
Title: An investigation of skill requirements for business and data analytics positions: A content analysis of job advertisements
Abstract: Abstract Presently, analytics degree programs exhibit a growing trend to meet a strong market demand. To explore the skill sets required for analytics positions, the authors examined a sample of online job postings related to professions such as business analyst (BA), business intelligence analyst (BIA), data analyst (DA), and data scientist (DS) using content analysis. They present a ranked list of relevant skills belonging to specific skills categories for the studied positions. Also, they conducted a pairwise comparison between DA and DS as well as BA and BIA. Overall, the authors observed that decision making, organization, communication, and structured data management are key to all job categories. The analysis shows that technical skills like statistics and programming skills are in most demand for DAs. The analysis is useful for creating clear definitions with respect to required skills for job categories in the business and data analytics domain and for designing course curricula for this domain.
Extracted statement — Has result: clear definitions with respect to required skills for job categories in the business and data analytics domain
Research field: Work, Economy and Organizations
Title: Skill Requirements in Big Data: A Content Analysis of Job Advertisements
Abstract: ABSTRACT The technology behind big data, although still in its nascent stages, is inspiring many companies to hire data scientists and explore the potential of big data to support strategic initiatives, including developing new products and services. To better understand the skills and knowledge that are highly valued by industry for jobs within big data, this study reports on an analysis of 1216 job advertisements that contained “big data” in the job title. Our results are presented within a conceptual framework of big data skills categories and confirm the multi-faceted nature of big data job skills. Our research also found that many big data job advertisements emphasize developing analytical information systems and that soft skills remain highly valued, in addition to the value placed on emerging hard technological skills.
Extracted statement — Has result: conceptual framework of big data skills categories
Research field: Work, Economy and Organizations
Title: Analyzing Computer Programming Job Trend Using Web Data Mining
Abstract: Today’s rapid changing and competitive environment requires educators to stay abreast of the job market in order to prepare their students for the jobs being demanded. This is more relevant about Information Technology (IT) jobs than others. However, to stay abreast of the market job demands require retrieving, sifting and analyzing large volume of data in order to understand the trends of the job market. Traditional methods of data collection and analysis are not sufficient for this kind of analysis due to the large volume of job data that is generated through the web and elsewhere. Luckily, the field of data mining has emerged to collect and sift through such large data volumes. However, even with data mining, appropriate data collection techniques and analysis need to be followed in order to correctly understand the trend. This paper illustrates our experience with employing mining techniques to understand the trend in IT Technology jobs. Data was collect using data mining techniques over a number of years from an online job agency. The data was then analyzed to reach a conclusion about the trends in the job market. Our experience in this regard along with literature review of the relevant topics is illustrated in this paper.
Extracted statement — result: conclusion about the trends in the job market
Research field: Work, Economy and Organizations
Title: Skill Needs for Early Career Researchers—A Text Mining Approach
Abstract: Research and development activities are one of the main drivers for progress, economic growth and wellbeing in many societies. This article proposes a text mining approach applied to a large amount of data extracted from job vacancies advertisements, aiming to shed light on the main skills and demands that characterize first stage research positions in Europe. Results show that data handling and processing skills are essential for early career researchers, irrespective of their research field. Also, as many analyzed first stage research positions are connected to universities, they include teaching activities to a great extent. Management of time, risks, projects, and resources plays an important part in the job requirements included in the analyzed advertisements. Such information is relevant not only for early career researchers who perform job selection taking into account the match of possessed skills with the required ones, but also for educational institutions that are responsible for skills development of the future R&D professionals.
Extracted statement — Has result: data handling and processing skills are essential for early career researchers, irrespective of their research field
Research field: electrical engineering
Title: Remarkable Improvement in Foldability of Poly‐Si Thin‐Film Transistor on Polyimide Substrate Using Blue Laser Crystallization of Amorphous Si and Comparison with Conventional Poly‐Si Thin‐Film Transistor Used for Foldable Displays
Abstract: Highly robust poly‐Si thin‐film transistor (TFT) on polyimide (PI) substrate using blue laser annealing (BLA) of amorphous silicon (a‐Si) for lateral crystallization is demonstrated. Its foldability is compared with the conventional excimer laser annealing (ELA) poly‐Si TFT on PI used for foldable displays exhibiting field‐effect mobility of 85 cm2 (V s)−1. The BLA poly‐Si TFT on PI exhibits the field‐effect mobility, threshold voltage (VTH), and subthreshold swing of 153 cm2 (V s)−1, −2.7 V, and 0.2 V dec−1, respectively. Most important finding is the excellent foldability of BLA TFT compared with the ELA poly‐Si TFTs on PI substrates. The VTH shift of BLA poly‐Si TFT is ≈0.1 V, which is much smaller than that (≈2 V) of ELA TFT on PI upon 30 000 cycle folding. The defects are generated at the grain boundary region of ELA poly‐Si during folding. However, BLA poly‐Si has no protrusion in the poly‐Si channel and thus no defect generation during folding. This leads to excellent foldability of BLA poly‐Si on PI substrate.
Extracted statement — Film deposition method: Blue laser annealing (BLA) of amorphous silicon
Research field: electrical engineering
Title: Solution-processed high-performance p-channel copper tin sulfide thin-film transistors
Abstract: <p>We introduce a solution-processed copper tin sulfide (CTS) thin film to realize high-performance of thin-film transistors (TFT) by optimizing the CTS precursor solution concentration.</p>
Extracted statement — Material: Copper tin sulfide (CTS) thin film
Research field: Artificial Intelligence
Title: SemEval-2020 Task 5: Counterfactual Recognition
Abstract: We present a counterfactual recognition (CR) task, the shared Task 5 of SemEval-2020. Counterfactuals describe potential outcomes (consequents) produced by actions or circumstances that did not happen or cannot happen and are counter to the facts (antecedent). Counterfactual thinking is an important characteristic of the human cognitive system; it connects antecedents and consequent with causal relations. Our task provides a benchmark for counterfactual recognition in natural language with two subtasks. Subtask-1 aims to determine whether a given sentence is a counterfactual statement or not. Subtask-2 requires the participating systems to extract the antecedent and consequent in a given counterfactual statement. During the SemEval-2020 official evaluation period, we received 27 submissions to Subtask-1 and 11 to Subtask-2. Our data and baseline code are made publicly available at https://zenodo.org/record/3932442. The task website and leaderboard can be found at https://competitions.codalab.org/competitions/21691.
Extracted statement — Online competition: https://competitions.codalab.org/competitions/21691
Research field: Artificial Intelligence
Title: SemEval-2020 Task 4: Commonsense Validation and Explanation
Abstract: In this paper, we present SemEval-2020 Task 4,CommonsenseValidation andExplanation(ComVE), which includes three subtasks, aiming to evaluate whether a system can distinguish anatural language statement thatmakes senseto humans from one that does not, and provide thereasons. Specifically, in our first subtask, the participating systems are required to choose from twonatural language statements of similar wording the one thatmakes senseand the one does not. Thesecond subtask additionally asks a system to select the key reason from three options why a givenstatement does not make sense. In the third subtask, a participating system needs to generate thereason automatically. 39 teams submitted their valid systems to at least one subtask. For SubtaskA and Subtask B, top-performing teams have achieved results closed to human performance.However, for Subtask C, there is still a considerable gap between system and human performance.The dataset used in our task can be found athttps://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation.
Extracted statement — Data repositories: https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation
Research field: Artificial Intelligence
Title: SemEval-2020 Task 5: Counterfactual Recognition
Abstract: We present a counterfactual recognition (CR) task, the shared Task 5 of SemEval-2020. Counterfactuals describe potential outcomes (consequents) produced by actions or circumstances that did not happen or cannot happen and are counter to the facts (antecedent). Counterfactual thinking is an important characteristic of the human cognitive system; it connects antecedents and consequent with causal relations. Our task provides a benchmark for counterfactual recognition in natural language with two subtasks. Subtask-1 aims to determine whether a given sentence is a counterfactual statement or not. Subtask-2 requires the participating systems to extract the antecedent and consequent in a given counterfactual statement. During the SemEval-2020 official evaluation period, we received 27 submissions to Subtask-1 and 11 to Subtask-2. Our data and baseline code are made publicly available at https://zenodo.org/record/3932442. The task website and leaderboard can be found at https://competitions.codalab.org/competitions/21691.
Extracted statement — Data repositories: https://zenodo.org/record/3932442
Research field: Bioinformatics
Title: LINNAEUS: A species name identification system for biomedical literature
Abstract: Abstract Background The task of recognizing and identifying species names in biomedical literature has recently been regarded as critical for a number of applications in text and data mining, including gene name recognition, species-specific document retrieval, and semantic enrichment of biomedical articles. Results In this paper we describe an open-source species name recognition and normalization software system, LINNAEUS, and evaluate its performance relative to several automatically generated biomedical corpora, as well as a novel corpus of full-text documents manually annotated for species mentions. LINNAEUS uses a dictionary-based approach (implemented as an efficient deterministic finite-state automaton) to identify species names and a set of heuristics to resolve ambiguous mentions. When compared against our manually annotated corpus, LINNAEUS performs with 94% recall and 97% precision at the mention level, and 98% recall and 90% precision at the document level. Our system successfully solves the problem of disambiguating uncertain species mentions, with 97% of all mentions in PubMed Central full-text documents resolved to unambiguous NCBI taxonomy identifiers. Conclusions LINNAEUS is an open source, stand-alone software system capable of recognizing and normalizing species name mentions with speed and accuracy, and can therefore be integrated into a range of bioinformatics and text-mining applications. The software and manually annotated corpus can be downloaded freely at <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://linnaeus.sourceforge.net/" ext-link-type="uri">http://linnaeus.sourceforge.net/</jats:ext-link>.
Extracted statement — url: http://linnaeus.sourceforge.net/
Research field: Bioinformatics
Title: MedPost: a part-of-speech tagger for bioMedical text
Abstract: SUMMARY We present a part-of-speech tagger that achieves over 97% accuracy on MEDLINE citations. AVAILABILITY Software, documentation and a corpus of 5700 manually tagged sentences are available at ftp://ftp.ncbi.nlm.nih.gov/pub/lsmith/MedPost/medpost.tar.gz
Extracted statement — url: ftp://ftp.ncbi.nlm.nih.gov/pub/lsmith/MedPost/medpost.tar.gz
Research field: Computational Linguistics
Title: GENETAG: a tagged corpus for gene/protein named entity recognition
Abstract: Abstract Background Named entity recognition (NER) is an important first step for text mining the biomedical literature. Evaluating the performance of biomedical NER systems is impossible without a standardized test corpus. The annotation of such a corpus for gene/protein name NER is a difficult process due to the complexity of gene/protein names. We describe the construction and annotation of GENETAG, a corpus of 20K MEDLINE ® sentences for gene/protein NER. 15K GENETAG sentences were used for the BioCreAtIvE Task 1A Competition. Results To ensure heterogeneity of the corpus, MEDLINE sentences were first scored for term similarity to documents with known gene names, and 10K high- and 10K low-scoring sentences were chosen at random. The original 20K sentences were run through a gene/protein name tagger, and the results were modified manually to reflect a wide definition of gene/protein names subject to a specificity constraint, a rule that required the tagged entities to refer to specific entities. Each sentence in GENETAG was annotated with acceptable alternatives to the gene/protein names it contained, allowing for partial matching with semantic constraints. Semantic constraints are rules requiring the tagged entity to contain its true meaning in the sentence context. Application of these constraints results in a more meaningful measure of the performance of an NER system than unrestricted partial matching. Conclusion The annotation of GENETAG required intricate manual judgments by annotators which hindered tagging consistency. The data were pre-segmented into words, to provide indices supporting comparison of system responses to the "gold standard". However, character-based indices would have been more robust than word-based indices. GENETAG Train, Test and Round1 data and ancillary programs are freely available at <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="ftp://ftp.ncbi.nlm.nih.gov/pub/tanabe/GENETAG.tar.gz" ext-link-type="uri">ftp://ftp.ncbi.nlm.nih.gov/pub/tanabe/GENETAG.tar.gz</jats:ext-link>. A newer version of GENETAG-05, will be released later this year.
Extracted statement — url: ftp://ftp.ncbi.nlm.nih.gov/pub/tanabe/GENETAG.tar.gz