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 — Material: data
Research field: Information Science
Title: Open Science meets Food Modelling: Introducing the Food Modelling Journal (FMJ)
Abstract: <jats:p>This Editorial describes the rationale, focus, scope and technology behind the newly launched, open access, innovative Food Modelling Journal (FMJ). The Journal is designed to publish those outputs of the research cycle that usually precede the publication of the research article, but have their own value and re-usability potential. Such outputs are methods, models, software and data. The Food Modelling Journal is launched by the AGINFRA+ community and is integrated with the AGINFRA+ Virtual Research Environment (VRE) to facilitate and streamline the authoring, peer review and publication of the manuscripts via the ARPHA Publishing Platform.</jats:p>
Extracted statement — Material: data
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: data
Research field: Information Science
Title: The Pattern of Patterns: What is a pattern in conceptual modeling?
Abstract: It has been proven that using structured methods to represent the domain reduces human errors in the process of creating models and also in the process of using them. Using modeling patterns is a proven structural method in this regard. A pattern is a generalizable reusable solution to a design problem. Positive effects of using patterns were demonstrated in several experimental studies and explained using theories. However, detailed knowledge about how properties of patterns lead to increased performance in writing and reading conceptual models is currently lacking. This paper proposes a theoretical framework to characterize the properties of ontology-driven conceptual model patterns. The development of such framework is the first step in investigating the effects of pattern properties and devising rules to compose patterns based on well-understood properties.
Extracted statement — Material: domain
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: e-learning
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: era
Research field: Information Science
Title: Expanding horizons in historical linguistics with the 400-million word Corpus of Historical American English
Abstract: The Corpus of Historical American English (COHA) contains 400 million words in more than 100,000 texts which date from the 1810s to the 2000s. The corpus contains texts from fiction, popular magazines, newspapers and non-fiction books, and is balanced by genre from decade to decade. It has been carefully lemmatised and tagged for part-of-speech, and uses the same architecture as the Corpus of Contemporary American English (COCA), BYU-BNC, the TIME Corpus and other corpora. COHA allows for a wide range of research on changes in lexis, morphology, syntax, semantics, and American culture and society (as viewed through language change), in ways that are probably not possible with any text archive (e.g., Google Books) or any other corpus of historical American English.
Extracted statement — Corpus genres: fiction
Research field: Information Science
Title: The Pattern of Patterns: What is a pattern in conceptual modeling?
Abstract: It has been proven that using structured methods to represent the domain reduces human errors in the process of creating models and also in the process of using them. Using modeling patterns is a proven structural method in this regard. A pattern is a generalizable reusable solution to a design problem. Positive effects of using patterns were demonstrated in several experimental studies and explained using theories. However, detailed knowledge about how properties of patterns lead to increased performance in writing and reading conceptual models is currently lacking. This paper proposes a theoretical framework to characterize the properties of ontology-driven conceptual model patterns. The development of such framework is the first step in investigating the effects of pattern properties and devising rules to compose patterns based on well-understood properties.
Extracted statement — Material: framework
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: genre
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 — Uses metric: Inputs
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 — Entity type: Instruments
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 — Languages: Latin
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 — Dataset name: LatinISE
Research field: Information Science
Title: Data Curation in the OpenAIRE Scholarly Communication Infrastructure
Abstract: OpenAIRE is the European Union initiative for an Open Access Infrastructure for Research in support of open scholarly communication and access to the research output of European funded projects and open access content from a network of institutional and disciplinary repositories. This article outlines the curation activities conducted in the OpenAIRE infrastructure, which employs a multi-level, multi-targeted approach: the publication and implementation of interoperability guidelines to assist in the local data curation processes, the data curation due to the integration of heterogeneous sources supporting different types of data, the inference of links to accomplish the publication research contextualization and data enrichment, and the end-user metadata curation that allows users to edit the attributes and provide links among the entities.
Extracted statement — Content: Metadata
Research field: Information Science
Title: Description and validation of a new automated surveillance system for Clostridium difficile in Denmark
Abstract: SUMMARY The surveillance of Clostridium difficile (CD) in Denmark consists of laboratory based data from Departments of Clinical Microbiology (DCMs) sent to the National Registry of Enteric Pathogens (NREP). We validated a new surveillance system for CD based on the Danish Microbiology Database (MiBa). MiBa automatically collects microbiological test results from all Danish DCMs. We built an algorithm to identify positive test results for CD recorded in MiBa. A CD case was defined as a person with a positive culture for CD or PCR detection of toxin A and/or B and/or binary toxin. We compared CD cases identified through the MiBa-based surveillance with those reported to NREP and locally in five DCMs representing different Danish regions. During 2010–2014, NREP reported 13 896 CD cases, and the MiBa-based surveillance 21 252 CD cases. There was a 99·9% concordance between the local datasets and the MiBa-based surveillance. Surveillance based on MiBa was superior to the current surveillance system, and the findings show that the number of CD cases in Denmark hitherto has been under-reported. There were only minor differences between local data and the MiBa-based surveillance, showing the completeness and validity of CD data in MiBa. This nationwide electronic system can greatly strengthen surveillance and research in various applications.
Extracted statement — Epidemiological surveillance software: MiBa
Research field: Information Science
Title: Improving national surveillance of Lyme neuroborreliosis in Denmark through electronic reporting of specific antibody index testing from 2010 to 2012
Abstract: Our aim was to evaluate the results of automated surveillance of Lyme neuroborreliosis (LNB) in Denmark using the national microbiology database (MiBa), and to describe the epidemiology of laboratory-confirmed LNB at a national level. MiBa-based surveillance includes electronic transfer of laboratory results, in contrast to the statutory surveillance based on manually processed notifications. Antibody index (AI) testing is the recommend laboratory test to support the diagnosis of LNB in Denmark. In the period from 2010 to 2012, 217 clinical cases of LNB were notified to the statutory surveillance system, while 533 cases were reported AI positive by the MiBa system. Thirty-five unconfirmed cases (29 AI-negative and 6 not tested) were notified, but not captured by MiBa. Using MiBa, the number of reported cases was increased almost 2.5 times. Furthermore, the reporting was timelier (median lag time: 6 vs 58 days). Average annual incidence of AI-confirmed LNB in Denmark was 3.2/100,000 population and incidences stratified by municipality ranged from none to above 10/100,000. This is the first study reporting nationwide incidence of LNB using objective laboratory criteria. Laboratory-based surveillance with electronic data-transfer was more accurate, complete and timely compared to the surveillance based on manually processed notifications. We propose using AI test results for LNB surveillance instead of clinical reporting.
Extracted statement — Epidemiological surveillance software: MiBa
Research field: Information Science
Title: BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Abstract: Abstract Motivation Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora. Results We introduce BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora. With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT significantly outperforms them on the following three representative biomedical text mining tasks: biomedical named entity recognition (0.62% F1 score improvement), biomedical relation extraction (2.80% F1 score improvement) and biomedical question answering (12.24% MRR improvement). Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts. Availability and implementation We make the pre-trained weights of BioBERT freely available at https://github.com/naver/biobert-pretrained, and the source code for fine-tuning BioBERT available at https://github.com/dmis-lab/biobert.
Extracted statement — contains: Model
Research field: Information Science
Title: Semantic relation classification via bidirectional LSTM networks with entity-aware attention using latent entity typing
Abstract: Classifying semantic relations between entity pairs in sentences is an important task in natural language processing (NLP). Most previous models applied to relation classification rely on high-level lexical and syntactic features obtained by NLP tools such as WordNet, the dependency parser, part-of-speech (POS) tagger, and named entity recognizers (NER). In addition, state-of-the-art neural models based on attention mechanisms do not fully utilize information related to the entity, which may be the most crucial feature for relation classification. To address these issues, we propose a novel end-to-end recurrent neural model that incorporates an entity-aware attention mechanism with a latent entity typing (LET) method. Our model not only effectively utilizes entities and their latent types as features, but also builds word representations by applying self-attention based on symmetrical similarity of a sentence itself. Moreover, the model is interpretable by visualizing applied attention mechanisms. Experimental results obtained with the SemEval-2010 Task 8 dataset, which is one of the most popular relation classification tasks, demonstrate that our model outperforms existing state-of-the-art models without any high-level features.
Extracted statement — Has: Model
Research field: Information Science
Title: Graph Convolution over Pruned Dependency Trees Improves Relation Extraction
Abstract: Dependency trees help relation extraction models capture long-range relations between words. However, existing dependency-based models either neglect crucial information (e.g., negation) by pruning the dependency trees too aggressively, or are computationally inefficient because it is difficult to parallelize over different tree structures. We propose an extension of graph convolutional networks that is tailored for relation extraction, which pools information over arbitrary dependency structures efficiently in parallel. To incorporate relevant information while maximally removing irrelevant content, we further apply a novel pruning strategy to the input trees by keeping words immediately around the shortest path between the two entities among which a relation might hold. The resulting model achieves state-of-the-art performance on the large-scale TACRED dataset, outperforming existing sequence and dependency-based neural models. We also show through detailed analysis that this model has complementary strengths to sequence models, and combining them further improves the state of the art.
Extracted statement — Has: Model