Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Inequality and Stratification
Title: Who is most affected by the Corona crisis? An analysis of changes in stress and well-being in Switzerland
Abstract: ABSTRACT This study analyses the consequences of the Covid-19 crisis on stress and well-being in Switzerland. In particular, we assess whether vulnerable groups in terms of social isolation, increased workload and limited socioeconomic resources are affected more than others. Using longitudinal data from the Swiss Household Panel, including a specific Covid-19 study, we estimate change score models to predict changes in perceived stress and life satisfaction at the end of the semi-lockdown in comparison to before the crisis. We find no general change in life satisfaction and a small decrease in stress. Yet, in line with our expectations, more vulnerable groups in terms of social isolation (young adults, Covid-19 risk group members, individuals without a partner), workload (women) and socioeconomic resources (unemployed and those who experienced a deteriorating financial situation) reported a decrease in life satisfaction. Stress levels decreased most strongly among high earners, workers on short-time work and the highly educated.
Extracted statement — Examined (sub-)group: men
Research field: Inequality and Stratification
Title: Who is most affected by the Corona crisis? An analysis of changes in stress and well-being in Switzerland
Abstract: ABSTRACT This study analyses the consequences of the Covid-19 crisis on stress and well-being in Switzerland. In particular, we assess whether vulnerable groups in terms of social isolation, increased workload and limited socioeconomic resources are affected more than others. Using longitudinal data from the Swiss Household Panel, including a specific Covid-19 study, we estimate change score models to predict changes in perceived stress and life satisfaction at the end of the semi-lockdown in comparison to before the crisis. We find no general change in life satisfaction and a small decrease in stress. Yet, in line with our expectations, more vulnerable groups in terms of social isolation (young adults, Covid-19 risk group members, individuals without a partner), workload (women) and socioeconomic resources (unemployed and those who experienced a deteriorating financial situation) reported a decrease in life satisfaction. Stress levels decreased most strongly among high earners, workers on short-time work and the highly educated.
Extracted statement — Examined (sub-)group: women
Research field: Information Science
Title: Tools for historical corpus research, and a corpus of Latin
Abstract: We present LatinISE, a Latin corpus for the Sketch Engine. LatinISE consists of Latin works comprising a total of 13 million words, covering the time span from the 2 nd century B. C. to the 21 st century A. D. LatinISE is provided with rich metadata mark-up, including author, title, genre, era, date and century, as well as book, section, paragraph and line of verses. We have automatically annotated LatinISE with lemma and part-of-speech information. The annotation enables the users to search the corpus with a number of criteria, ranging from lemma, part-of-speech, context, to subcorpora defined chronologically or by genre. We also illustrate word sketches, one-page summaries of a word’s corpus-based collocational behaviour. Our future plan is to produce word sketches for Latin words by adding richer morphological and syntactic annotation to the corpus.
Extracted statement — Metadata: date
Research field: Information Science
Title: Multi-Agent Systems: A Survey
Abstract: Multi-agent systems (MASs) have received tremendous attention from scholars in different disciplines, including computer science and civil engineering, as a means to solve complex problems by subdividing them into smaller tasks. The individual tasks are allocated to autonomous entities, known as agents. Each agent decides on a proper action to solve the task using multiple inputs, e.g., history of actions, interactions with its neighboring agents, and its goal. The MAS has found multiple applications, including modeling complex systems, smart grids, and computer networks. Despite their wide applicability, there are still a number of challenges faced by MAS, including coordination between agents, security, and task allocation. This survey provides a comprehensive discussion of all aspects of MAS, starting from definitions, features, applications, challenges, and communications to evaluation. A classification on MAS applications and challenges is provided along with references for further studies. We expect this paper to serve as an insightful and comprehensive resource on the MAS for researchers and practitioners in the area.
Extracted statement — consists of: Agent
Research field: Information Science
Title: NERA: Named Entity Recognition for Arabic
Abstract: Name identification has been worked on quite intensively for the past few years, and has been incorporated into several products revolving around natural language processing tasks. Many researchers have attacked the name identification problem in a variety of languages, but only a few limited research efforts have focused on named entity recognition for Arabic script. This is due to the lack of resources for Arabic named entities and the limited amount of progress made in Arabic natural language processing in general. In this article, we present the results of our attempt at the recognition and extraction of the 10 most important categories of named entities in Arabic script: the person name, location, company, date, time, price, measurement, phone number, ISBN, and file name. We developed the system Named Entity Recognition for Arabic (NERA) using a rulebased approach. The resources created are: a Whitelist representing a dictionary of names, and a grammar, in the form of regular expressions, which are responsible for recognizing the named entities. A filtration mechanism is used that serves two different purposes: (a) revision of the results from a named entity extractor by using metadata, in terms of a Blacklist or rejecter, about ill-formed named entities and (b) disambiguation of identical or overlapping textual matches returned by different name entity extractors to get the correct choice. In NERA, we addressed major challenges posed by NER in the Arabic language arising due to the complexity of the language, peculiarities in the Arabic orthographic system, nonstandardization of the written text, ambiguity, and lack of resources. NERA has been effectively evaluated using our own tagged corpus; it achieved satisfactory results in terms of precision, recall, and F-measure.
Extracted statement — Language/domain: Arabic
Research field: Information Science
Title: RENAR: A rule-based arabic named entity recognition system
Abstract: Named entity recognition has served many natural language processing tasks such as information retrieval, machine translation, and question answering systems. Many researchers have addressed the name identification issue in a variety of languages and recently some research efforts have started to focus on named entity recognition for the Arabic language. We present a working Arabic information extraction (IE) system that is used to analyze large volumes of news texts every day to extract the named entity (NE) types person, organization, location, date, and number, as well as quotations (direct reported speech) by and about people. The named entity recognition (NER) system was not developed for Arabic, but instead a multilingual NER system was adapted to also cover Arabic. The Semitic language Arabic substantially differs from the Indo-European and Finno-Ugric languages currently covered. This article thus describes what Arabic language-specific resources had to be developed and what changes needed to be made to the rule set in order to be applicable to the Arabic language. The achieved evaluation results are generally satisfactory, but could be improved for certain entity types.
Extracted statement — Language/domain: Arabic
Research field: Information Science
Title: Arabic Named Entity Recognition: A Feature-Driven Study 
Abstract: The named entity recognition task aims at identifying and classifying named entities within an open-domain text. This task has been garnering significant attention recently as it has been shown to help improve the performance of many natural language processing applications. In this paper, we investigate the impact of using different sets of features in three discriminative machine learning frameworks, namely, support vector machines, maximum entropy and conditional random fields for the task of named entity recognition. Our language of interest is Arabic. We explore lexical, contextual and morphological features and nine data-sets of different genres and annotations. We measure the impact of the different features in isolation and incrementally combine them in order to evaluate the robustness to noise of each approach. We achieve the highest performance using a combination of 15 features in conditional random fields using broadcast news data (Fbeta = 1=83.34).
Extracted statement — Language/domain: Arabic
Research field: Information Science
Title: Open access, readership, citations: a randomized controlled trial of scientific journal publishing
Abstract: Does free access to journal articles result in greater diffusion of scientific knowledge? Using a randomized controlled trial of open access publishing, involving 36 participating journals in the sciences, social sciences, and humanities, we report on the effects of free access on article downloads and citations. Articles placed in the open access condition (n=712) received significantly more downloads and reached a broader audience within the first year, yet were cited no more frequently, nor earlier, than subscription‐access control articles (n=2533) within 3 yr. These results may be explained by social stratification, a process that concentrates scientific authors at a small number of elite research universities with excellent access to the scientific literature. The real beneficiaries of open access publishing may not be the research community but communities of practice that consume, but rarely contribute to, the corpus of literature.—Davis, P. M. Open access, readership, citations: a randomized controlled trial of scientific journal publishing. FASEB J. 25, 2129‐2134 (2011). www.fasebj.org
Extracted statement — open_access_medium: articles
Research field: Information Science
Title: Attracting new users or business as usual? A case study of converting academic subscription-based journals to open access
Abstract: Abstract This paper studies a selection of 11 Norwegian journals in the humanities and social sciences and their conversion from subscription to open access, a move heavily incentivized by governmental mandates and open access policies. By investigating the journals’ visiting logs in the period 2014–2019, the study finds that a conversion to open access induces higher visiting numbers; all journals in the study had a significant increase, which can be attributed to the conversion. Converting a journal had no spillover in terms of increased visits to previously published articles still behind the paywall in the same journals. Visits from previously subscribing Norwegian higher education institutions did not account for the increase in visits, indicating that the increase must be accounted for by visitors from other sectors. The results could be relevant for policymakers concerning the effects of strict policies targeting economically vulnerable national journals, and could further inform journal owners and editors on the effects of converting to open access.
Extracted statement — open_access_medium: articles
Research field: Information Science
Title: Tools for historical corpus research, and a corpus of Latin
Abstract: We present LatinISE, a Latin corpus for the Sketch Engine. LatinISE consists of Latin works comprising a total of 13 million words, covering the time span from the 2 nd century B. C. to the 21 st century A. D. LatinISE is provided with rich metadata mark-up, including author, title, genre, era, date and century, as well as book, section, paragraph and line of verses. We have automatically annotated LatinISE with lemma and part-of-speech information. The annotation enables the users to search the corpus with a number of criteria, ranging from lemma, part-of-speech, context, to subcorpora defined chronologically or by genre. We also illustrate word sketches, one-page summaries of a word’s corpus-based collocational behaviour. Our future plan is to produce word sketches for Latin words by adding richer morphological and syntactic annotation to the corpus.
Extracted statement — Metadata: author
Research field: Information Science
Title: Spanbert: Improving pre-training by representing and predicting spans
Abstract: We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the span boundary representations to predict the entire content of the masked span, without relying on the individual token representations within it. SpanBERT consistently outperforms BERT and our better-tuned baselines, with substantial gains on span selection tasks such as question answering and coreference resolution. In particular, with the same training data and model size as BERT large , our single model obtains 94.6% and 88.7% F1 on SQuAD 1.1 and 2.0 respectively. We also achieve a new state of the art on the OntoNotes coreference resolution task (79.6% F1), strong performance on the TACRED relation extraction benchmark, and even gains on GLUE. 1
Extracted statement — Has: Baselines
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 — Domain: Biodiversity
Research field: Information Science
Title: AUPress: A Comparison of an Open Access University Press with Traditional Presses
Abstract: This study is a comparison of AUPress with three other traditional (non-open access) Canadian university presses. The analysis is based on the rankings that are correlated with book sales on Amazon.com and Amazon.ca. Statistical methods include the sampling of the sales ranking of randomly selected books from each press. The results of one-way ANOVA analyses show that there is no significant difference in the ranking of printed books sold by AUPress in comparison with traditional university presses. However, AUPress, can demonstrate a significantly larger readership for its books as evidenced by the number of downloads of the open electronic versions.
Extracted statement — open_access_medium: books
Research field: Information Science
Title: The profits of free books: an experiment to measure the impact of open access publishing
Abstract: This article describes an experiment to measure the impact of open access (OA) publishing of academic books. During a period of nine months, three sets of 100 books were disseminated through an institutional repository, the Google Book Search program, or both channels. A fourth set of 100 books was used as control group. OA publishing enhances discovery and online consultation. Within the context of the experiment, no relation could be found between OA publishing and citation rates. Contrary to expectations, OA publishing does not stimulate or diminish sales figures. The Google Book Search program is superior to the repository.
Extracted statement — open_access_medium: books
Research field: Information Science
Title: Tools for historical corpus research, and a corpus of Latin
Abstract: We present LatinISE, a Latin corpus for the Sketch Engine. LatinISE consists of Latin works comprising a total of 13 million words, covering the time span from the 2 nd century B. C. to the 21 st century A. D. LatinISE is provided with rich metadata mark-up, including author, title, genre, era, date and century, as well as book, section, paragraph and line of verses. We have automatically annotated LatinISE with lemma and part-of-speech information. The annotation enables the users to search the corpus with a number of criteria, ranging from lemma, part-of-speech, context, to subcorpora defined chronologically or by genre. We also illustrate word sketches, one-page summaries of a word’s corpus-based collocational behaviour. Our future plan is to produce word sketches for Latin words by adding richer morphological and syntactic annotation to the corpus.
Extracted statement — Metadata: century
Research field: Information Science
Title: Scalable SPARQL querying of large RDF graphs
Abstract: The generation of RDF data has accelerated to the point where many data sets need to be partitioned across multiple machines in order to achieve reasonable performance when querying the data. Although tremendous progress has been made in the Semantic Web community for achieving high performance data management on a single node, current solutions that allow the data to be partitioned across multiple machines are highly inefficient. In this paper, we introduce a scalable RDF data management system that is up to three orders of magnitude more efficient than popular multi-node RDF data management systems. In so doing, we introduce techniques for (1) leveraging state-of-the-art single node RDF-store technology (2) partitioning the data across nodes in a manner that helps accelerate query processing through locality optimizations and (3) decomposing SPARQL queries into high performance fragments that take advantage of how data is partitioned in a cluster.
Extracted statement — Material: cluster
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: community
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 — Personalisation features: courses