Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Information Science
Title: Named entity recognition for Nepali text using support vector machines
Abstract: Named Entity Recognition aims to identify and to classify rigid designators in text such as proper names, biological species, and temporal expressions into some predefined categories. There has been growing interest in this field of research since the early 1990s. Named Entity Recognition has a vital role in different fields of natural language processing such as Machine Translation, Information Extraction, Question Answering System and various other fields. In this paper, Named Entity Recognition for Nepali text, based on the Support Vector Machine (SVM) is presented which is one of machine learning approaches for the classification task. A set of features are extracted from training data set. Accuracy and efficiency of SVM classifier are analyzed in three different sizes of training data set. Recognition systems are tested with ten datasets for Nepali text. The strength of this work is the efficient feature extraction and the comprehensive recognition techniques. The Support Vector Machine based Named Entity Recognition is limited to use a certain set of features and it uses a small dictionary which affects its performance. The learning performance of recognition system is observed. It is found that system can learn well from the small set of training data and increase the rate of learning on the increment of training size.
Extracted statement — Language/domain: Nepali
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: newspapers
Research field: Information Science
Title: Towards Improving the Quality of Knowledge Graphs with Data-driven Ontology Patterns and SHACL
Abstract: As Linked Data available on the Web continue to grow, understanding their structure and assessing their quality remains a challenging task making such the bottleneck for their reuse. ABSTAT is an online semantic profiling tool which helps data consumers in better understanding of the data by extracting data-driven ontology patterns and statistics about the data. The SHACL Shapes Constraint Language helps users capturing quality issues in the data by means of co straints. In this paper we propose a methodology to improve the quality of different versions of the data by means of SHACL constraints learned from the semantic profiles produced by ABSTAT.
Extracted statement — input: ontology
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 — keywords: ontology
Research field: Information Science
Title: Ontology-based E-learning Content Recommender System for Addressing the Pure Cold-start Problem
Abstract: E-learning recommender systems are gaining significance nowadays due to its ability to enhance the learning experience by providing tailor-made services based on learner preferences. A Personalized Learning Environment (PLE) that automatically adapts to learner characteristics such as learning styles and knowledge level can recommend appropriate learning resources that would favor the learning process and improve learning outcomes. The pure cold-start problem is a relevant issue in PLEs, which arises due to the lack of prior information about the new learner in the PLE to create appropriate recommendations. This article introduces a semantic framework based on ontology to address the pure cold-start problem in content recommenders. The ontology encapsulates the domain knowledge about the learners as well as Learning Objects (LOs). The semantic model that we built has been experimented with different combinations of the key learner parameters such as learning style, knowledge level, and background knowledge. The proposed framework utilizes these parameters to build natural learner groups from the learner ontology using SPARQL queries. The ontology holds 480 learners’ data, 468 annotated learning objects with 5,600 learner ratings. A multivariate k-means clustering algorithm, an unsupervised machine learning technique for grouping similar data, is used to evaluate the learner similarity computation accuracy. The learner satisfaction achieved with the proposed model is measured based on the ratings given by the 40 participants of the experiments. From the evaluation perspective, it is evident that 79% of the learners are satisfied with the recommendations generated by the proposed model in pure cold-start condition.
Extracted statement — keywords: ontology
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 — Database: OpenAIRE
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 — Database: OpenBiodiv
Research field: Information Science
Title: Developing open source, self-contained disease surveillance software applications for use in resource-limited settings
Abstract: Abstract Background Emerging public health threats often originate in resource-limited countries. In recognition of this fact, the World Health Organization issued revised International Health Regulations in 2005, which call for significantly increased reporting and response capabilities for all signatory nations. Electronic biosurveillance systems can improve the timeliness of public health data collection, aid in the early detection of and response to disease outbreaks, and enhance situational awareness. Methods As components of its Suite for Automated Global bioSurveillance (SAGES) program, The Johns Hopkins University Applied Physics Laboratory developed two open-source, electronic biosurveillance systems for use in resource-limited settings. OpenESSENCE provides web-based data entry, analysis, and reporting. ESSENCE Desktop Edition provides similar capabilities for settings without internet access. Both systems may be configured to collect data using locally available cell phone technologies. Results ESSENCE Desktop Edition has been deployed for two years in the Republic of the Philippines. Local health clinics have rapidly adopted the new technology to provide daily reporting, thus eliminating the two-to-three week data lag of the previous paper-based system. Conclusions OpenESSENCE and ESSENCE Desktop Edition are two open-source software products with the capability of significantly improving disease surveillance in a wide range of resource-limited settings. These products, and other emerging surveillance technologies, can assist resource-limited countries compliance with the revised International Health Regulations.
Extracted statement — Epidemiological surveillance software: OpenESSENCE
Research field: Information Science
Title: The transition of
            ARVO
            journals to open access
Abstract: In January 2016, the three journals of the Association for Research in Vision and Ophthalmology (ARVO) transitioned to gold open access. Increased author charges were introduced to partially offset the loss of subscription revenue. Submissions to the two established journals initially dropped by almost 15% but have now stabilized. The transition has not impacted acceptance rates and impact factors, and article pageviews and downloads may have increased as a result of open access.
Extracted statement — research_field_investigated: Ophthalmology
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: personalization
Research field: Information Science
Title: Epidemiologic Surveillance in Developing Countries
Abstract: developed countries in many ways. Most people are poorer, less educated, more likely to die at a young age, and less knowledgeable about factors that cause, prevent, or cure disease. Biological and physical hazards are more common, which results in greater incidence, disability, and death. Although disease is common, both the people and government have much fewer resources for prevention or medical care. Many efficacious drugs arc too expensive and not readily available for those in greatest need. Salaries are so low that government physicians or nurses must work after-hours in private clinics to feed, clothe, and educate their families. The establishment and maintenance of an epidemiological surveillance system in such an environment requires a differ­ ent orientation from that found in wealthier nations. The scarcity of resources is a dominant concern. Salaried time spent gathering data is lost to service activities, such as treating gastrointestinal problems or preventing childhood diseases. As a result, components in a surveillance system must be justified, as are purchases of examination tables or radiographic equipment. A costly, extensive surveillance system may cause more harm than good. In this article 1 will define epidemiologic surveillance. 1 also will describe the various components of a surveillance program, show how microcomputers and existing software can be used to increase effectiveness, and illustrate how
Extracted statement — Epidemiological surveillance users: Physician
Research field: Information Science
Title: The Pattern of Patterns: What is a pattern in conceptual modeling?
Abstract: It has been proven that using structured methods to represent the domain reduces human errors in the process of creating models and also in the process of using them. Using modeling patterns is a proven structural method in this regard. A pattern is a generalizable reusable solution to a design problem. Positive effects of using patterns were demonstrated in several experimental studies and explained using theories. However, detailed knowledge about how properties of patterns lead to increased performance in writing and reading conceptual models is currently lacking. This paper proposes a theoretical framework to characterize the properties of ontology-driven conceptual model patterns. The development of such framework is the first step in investigating the effects of pattern properties and devising rules to compose patterns based on well-understood properties.
Extracted statement — Data: properties
Research field: Information Science
Title: Named entity recognition for Punjabi language text summarization
Abstract: Entity Recognition (NER) is used to locate and classify atomic elements in text into predetermined classes such as the names of persons, organizations, locations, concepts etc. NER is used in many applications like text summarization, text classification, question answering and machine translation systems etc. For English a lot of work has already done in field of NER, where capitalization is a major clue for rules, whereas Indian Languages do not have such feature. This makes the task difficult for Indian languages. This paper explains the Named Entity Recognition System for Punjabi language text summarization. A Condition based approach has been used for developing NER system for Punjabi language. Various rules have been developed like prefix rule, suffix rule, propername rule, middlename rule and lastname rule. For implementing NER, various resources in Punjabi, have been developed like a list of prefix names, a list of suffix names, a list of proper names, middle names and last names. The Precision, Recall and F-Score for condition based NER approach are 89.32%, 83.4% and 86.25% respectively.
Extracted statement — Language/domain: Punjabi
Research field: Information Science
Title: Named entity recognition in query
Abstract: This paper addresses the problem of Named Entity Recognition in Query (NERQ), which involves detection of the named entity in a given query and classification of the named entity into predefined classes. NERQ is potentially useful in many applications in web search. The paper proposes taking a probabilistic approach to the task using query log data and Latent Dirichlet Allocation. We consider contexts of a named entity (i.e., the remainders of the named entity in queries) as words of a document, and classes of the named entity as topics. The topic model is constructed by a novel and general learning method referred to as WS-LDA (Weakly Supervised Latent Dirichlet Allocation), which employs weakly supervised learning (rather than unsupervised learning) using partially labeled seed entities. Experimental results show that the proposed method based on WS-LDA can accurately perform NERQ, and outperform the baseline methods.
Extracted statement — Language/domain: Queries
Research field: Information Science
Title: Estimating relative depth in single images via rankboost
Abstract: In this paper, we present a novel approach to estimate the relative depth of regions in monocular images. There are several contributions. First, the task of monocular depth estimation is considered as a learning-to-rank problem which offers several advantages compared to regression approaches. Second, monocular depth clues of human perception are modeled in a systematic manner. Third, we show that these depth clues can be modeled and integrated appropriately in a Rankboost framework. For this purpose, a space-efficient version of Rankboost is derived that makes it applicable to rank a large number of objects, as posed by the given problem. Finally, the monocular depth clues are combined with results from a deep learning approach. Experimental results show that the error rate is reduced by adding the monocular features while outperforming state-of-the-art systems.
Extracted statement — Has approach: RankBoost
Research field: Information Science
Title: ORCID: a system to uniquely identify researchers
Abstract: The Open Researcher & Contributor ID (ORCID) registry presents a unique opportunity to solve the problem of author name ambiguity. At its core the value of the ORCID registry is that it crosses disciplines, organizations, and countries, linking ORCID with both existing identifier schemes as well as publications and other research activities. By supporting linkages across multiple datasets – clinical trials, publications, patents, datasets – such a registry becomes a switchboard for researchers and publishers alike in managing the dissemination of research findings. We describe use cases for embedding ORCID identifiers in manuscript submission workflows, prior work searches, manuscript citations, and repository deposition. We make recommendations for storing and displaying ORCID identifiers in publication metadata to include ORCID identifiers, with CrossRef integration as a specific example. Finally, we provide an overview of ORCID membership and integration tools and resources.
Extracted statement — Entity type: Researchers
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: Results
Research field: Information Science
Title: Attention Guided Graph Convolutional Networks for Relation Extraction
Abstract: Dependency trees convey rich structural information that is proven useful for extracting relations among entities in text. However, how to effectively make use of relevant information while ignoring irrelevant information from the dependency trees remains a challenging research question. Existing approaches employing rule based hard-pruning strategies for selecting relevant partial dependency structures may not always yield optimal results. In this work, we propose Attention Guided Graph Convolutional Networks (AGGCNs), a novel model which directly takes full dependency trees as inputs. Our model can be understood as a soft-pruning approach that automatically learns how to selectively attend to the relevant sub-structures useful for the relation extraction task. Extensive results on various tasks including cross-sentence n-ary relation extraction and large-scale sentence-level relation extraction show that our model is able to better leverage the structural information of the full dependency trees, giving significantly better results than previous approaches.
Extracted statement — Has: results