Structure Of Knowledge Base Research Articles

KEBLM: Knowledge-Enhanced Biomedical Language Models

Pretrained language models (PLMs) have demonstrated strong performance on many natural language processing (NLP) tasks. Despite their great success, these PLMs are typically pretrained only on unstructured free texts without leveraging existing structured knowledge bases that are readily available for many domains, especially scientific domains. As a result, these PLMs may not achieve satisfactory performance on knowledge-intensive tasks such as biomedical NLP. Comprehending a complex biomedical document without domain-specific knowledge is challenging, even for humans. Inspired by this observation, we propose a general framework for incorporating various types of domain knowledge from multiple sources into biomedical PLMs.We encode domain knowledge using lightweight adapter modules, bottleneck feed-forward networks that are inserted into different locations of a backbone PLM. For each knowledge source of interest, we pretrain an adapter module to capture the knowledge in a self-supervised way. We design a wide range of self-supervised objectives to accommodate diverse types of knowledge, ranging from entity relations to description sentences.Once a set of pretrained adapters is available, we employ fusion layers to combine the knowledge encoded within these adapters for downstream tasks. Each fusion layer is a parameterized mixer of the available trained adapters that can identify and activate the most useful adapters for a given input. Our method diverges from prior work by including a knowledge consolidation phase, during which we teach the fusion layers to effectively combine knowledge from both the original PLM and newly-acquired external knowledge using a large collection of unannotated texts. After the consolidation phase, the complete knowledge-enhanced model can be fine-tuned for any downstream task of interest to achieve optimal performance.Extensive experiments on many biomedical NLP datasets show that our proposed framework consistently improves the performance of the underlying PLMs on various downstream tasks such as natural language inference, question answering, and entity linking. These results demonstrate the benefits of using multiple sources of external knowledge to enhance PLMs and the effectiveness of the framework for incorporating knowledge into PLMs. While primarily focused on the biomedical domain in this work, our framework is highly adaptable and can be easily applied to other domains, such as the bioenergy sector.

Enhancing Answer Selection via Ad-Hoc Knowledge Extraction from Unstructured Web Texts

Answer selection aims to identify the most relevant answers to a given question from a set of candidates. It is the fundamental component of intelligent question answering system. To improve performance, it gradually becomes an effective strategy to integrate external structured knowledge bases (KBs) into the answer selection model. Due to expensive cost of construction and maintenance of such KBs, these models are suffering from domain barriers and information incompleteness. In this paper, we propose a two-stage extraction–comprehension answer selection model, which can extract ad-hoc knowledge from unstructured web texts to enhance the performance of answer selection. For the extraction, two types of snippets are extracted from unstructured web pages and utilized as the source of ad-hoc knowledge. For the comprehension, a selective attention mechanism is employed to extract and integrate ad-hoc knowledge from multiple text snippets obtained in the first stage, which can enrich the representation of question–answer pairs and more accurately identify the correct answers. By incorporating ad-hoc knowledge extracted from both types of snippets, the proposed model achieves state-of-the-art results on two public available benchmark datasets. In particular, on WikiQA, in terms of the two evaluation metrics (mean average precision and mean reciprocal rank), it achieves 9.9[Formula: see text] and 8.4[Formula: see text] higher than the previous non-pretraining-based models, and 3.4[Formula: see text] and 3.2[Formula: see text] higher than the pretraining-based models.

Knowledge Mapping of Opioids and Immunomodulation: A Bibliometric Analysis (2000-2022).

Increasing evidence indicates that opioids markedly affect the immune system. However, there are few studies on opioids and immunomodulation using bibliometric analysis. We aimed to provide a comprehensive overview of the research status and trends of the influence of opioids on immunomodulation using a bibliometric approach. Articles related to opioids and immunomodulation published from 2000 to 2022 were obtained from the Science Citation Index Expanded of the Web of Science Core Collection by searching keywords related to opioids and immunomodulation. Bibliometric analyses and visualizations were conducted using the CiteSpace and VOSviewer software programs. From 2000 to 2022, a total of 3242 research articles on opioids and immunomodulation were published in 1126 academic journals by 16,555 authors in 3368 institutions from 102 countries/regions. A majority of publications were from the US and China, and the University of Minnesota System and Chinese Academy of Sciences were the most active institutions. Tsong-long Hwang had published the most papers, while Sabita Roy had the most cocitations. The Journal of Ethnopharmacology published the most papers on opioids and immunomodulation, the Journal of Immunology was the top cocited journal, and the major area of these publications were molecular, biological, and genetic. The top three keywords were "expression", "activation", and "inflammation." The number of studies on opioids and immunomodulation has increased sharply all over the world in the last two decades. This is the first bibliometric study to comprehensively summarize the collaboration network in this field. It will help scholars to understand not only the basic knowledge structure but also potential collaborations, research trend topics, and hot directions.

Üniversite Öğrencilerinin Birinci Yıl Deneyimleri Üzerine Yapılan Araştırmaların Bibliyometrik Analizi

The purpose of the current study is to systematically review the literature on the first-year college student experience. The study used Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) to review the studies. The review included 161 studies from 2000 to 2019 within the Scopus database. Results indicated that almost half of the studies were conducted between 2015 and 2019 such that there is an increase in the number of studies. Most productive scholars were from Nordic Countries on this topic while the US, Australia, and UK published a high number of articles. Surprisingly, there were more qualitative studies than quantitative ones. Intellectual structure of knowledge base of research underlined the importance of first-year in college, transition to higher education, and student retention. Academic and social integration was the most frequently studied topic. The study broadens our understanding of the structure and processes of the first-year experience.

Research on liquid biopsy for cancer: A bibliometric analysis

Background: In recent years, liquid biopsy has shown great potential for improving cancer diagnosis and treatment. This study aimed to explore the trends and prospects in liquid biopsy for cancer from a bibliometric perspective.Methods: Reviews and articles on liquid biopsy and cancer were collected from the Web of Science Core Collection (WoSCC). Key bibliometric characteristics were analyzed using CiteSpace. Co-occurrence analysis of keywords and co-citation analysis of references was performed.Results: A total of 6331 publications from 11 years of scientific research were retrieved. Ninety-five countries and 7004 institutions in liquid biopsy and cancer contributed. The United States (US) and China published the most articles. The institution with the most published articles was the University of Texas MD Anderson Cancer Center. The most published journals were Cancer and Frontiers in Oncology. “Bettegowda (2014)" was the most cited reference with the highest burst strength in the last decade. Cluster analysis revealed that the recent hot topics were “circulating tumor cells,” “cancer,” and “exosomes."Conclusions: This bibliometric analysis maps the basic knowledge structure of the field of liquid biopsy for cancer. The field is entering a phase of rapid development. The hot spots identified in this study deserve further investigation.

Structuring Knowledge Bases with AI and Machine Learning

In today's world, it is no longer enough for a company to have a better product or service than its competitors to survive and grow. A customer-obsessed attitude is required for businesses to survive and grow. As we all know, competitors can typically quickly duplicate any new market position and even do it better than the organization that started the idea. The more business knowledge your team members have about the products and services your consumers use, the more successful your company will be. You, as an organization, should be swift in responding to your customers' demands to react to the competitive changes in the market. One of the key tools needed for a quick response to this effect is the Knowledge Base System (KBS). This tool can be an internal tool for your employees or an external tool for your customers. This will support the decision-making process, information sharing, products, services, etc. Most organizations have this tool but are not well structured. There is no single correct way to build a knowledge base, but there are multiple methods, each with its own set of advantages and quirks. However, if you follow some basic guidelines, you can be sure that your customers or employees will not get lost in the process. The most basic content format in the knowledge base is an article with text. However, it can include screenshots, photos, videos, audio, and infographics. We can further implement a knowledge-based system with artificial intelligence (AI), which gives room for more productivity in an organization. One thing that constitutes or destroys your knowledge base is its structure. Just like a dictionary won't serve its purpose unless it’s organized alphabetically, a cluttered or disorganized knowledge base will confuse your customers and your employees rather than lead them to a solution. You can convert knowledge base articles into FAQs, product manuals, troubleshooting guides, etc. A knowledge-based system might be a game changer for your organization if you want to make your clients happy. In this article, I will walk through the objectives, scope, strategy, and all you should keep in mind when you're building a knowledge base system for your organization. Keywords: Artificial intelligence (AI), knowledge, information, decision-making, and organizational DOI: 10.7176/IKM/13-1-03 Publication date: January 31 st 2023

Immune-related adverse events: A bibliometric analysis.

Despite providing clinical benefit, immune checkpoint inhibitors (ICIs) can cause immune-related adverse events (irAEs) in a number of patients. This study explored the development pattern in irAEs research from a bibliometric perspective. We obtained articles and reviews related to irAEs from the Web of Science Core Collection (WoSCC) (retrieved on September 13, 2022). Using the R package "Bibliometrix", the main bibliometric features were calculated, and a three-filed plot was generated to show the relationship between authors, institutions, and topics. VOSviewer was used for co-authorship and keyword co-occurrence analysis and visualization. CiteSpace was used to detect burst references and keywords. A total of 3995 publications on irAEs were included. The United States (US), Japan, and China had the highest publications. The Journal for ImmunoTherapy of Cancer had the highest number of publications. In addition to "immune-related adverse events", "immune checkpoint inhibitors", "immunotherapy", and "nivolumab" were the most frequently used keywords. A bibliometric analysis of 17 years of irAEs research was conducted to map a basic knowledge structure including countries, institutions, authors, journals, and publications. The findings provided a comprehensive perspective on the broad future of this research area.

The Development and Evolution of Digital Leadership: A Bibliometric Mapping Approach-Based Study

The inevitable digitalization of workplaces in the present era, generally as a result of technological developments, has caused a paradigm shift, along with new innovative business models and business behaviors, which has required leaders to possess certain digital skills for sustainable corporate performance. Hence, studies on digital leadership have attracted the attention of academics and practitioners worldwide, with many studies having been conducted on the topic. However, a comprehensive analysis of the intellectual architecture, knowledge structure, and thematic evolution of the digital leadership field of research using science mapping tools has yet to be conducted. The current study, therefore, aimed at reviewing the intellectual structure and evolution of the digital leadership field through a bibliometric and science-mapping analysis. This study used digital leadership as an umbrella term comprising leadership styles such as e-leadership, virtual leadership, technology leadership, and leadership 4.0, which have similar meanings and can be used interchangeably. With this purpose, bibliometric performance and science mapping analysis was performed on articles related to the research field that were retrieved from the Scopus database using SciMAT software (version 1.1.04). The results of the study revealed that the scope of digital leadership research is gradually expanding and diversifying and that publication output is increasing steadily. In addition, period-based analysis showed that the technology management theme during the first period, the virtual teams and technology themes during the second period, and the COVID-19, virtual reality, and digital technologies themes during the third period emerged as the motor themes and formed the focus of research in this field. Thematic evolution analysis showed that virtual leadership during the first and second periods, virtual teams during the second period, e-leadership and technology during the second and third periods, and digital leadership, COVID-19, and virtual reality during the third period, along with technology leadership in all three periods were all noteworthy as well-developed research themes. These findings enable a better understanding of the research field of digital leadership and provide a reference for future research by revealing the conceptual structure and thematic evolution of the digital leadership knowledge base.

TEACHERS’ DIGITAL COMPETENCE: BIBLIOMETRIC ANALYSIS OF THE PUBLICATIONS OF THE WEB OF SCIENCE SCIENTOMETRIC DATABASE

With the Online Emergency Remote Teaching (OERT) practices emerged during the outbreak of the pandemic, teachers’ digital competence (TDC) has gained growing attention in educational ICT research realm. In view of this context, the present review study aimed at illuminating the current state of TDCs literature by identifying the volume, growth trajectory, geographical distribution of TDC research. It also aimed at mapping highly influential TDC scholars, documents, and journals. Retrieved from the educational research category in the Clarivate Analytics Web of Science (WoS) core collection database, the metadata of 406 articles were analyzed by employing bibliometric performance and science mapping techniques in VOSwiever 1.6. The timeframe for the study was the last two decades (from 2002 to 2021). Findings illustrated that there has been a growing increase in the number of studies focusing on TDCs. This increase is more evident in the Covid-19 pandemic period, particularly in the last two years. More specifically, more than half of all studies were published in the years 2020 and 2021. Findings also illustrated that there is a dominance of Spanish scholars and organizations in TDC research since 2 out of every 3 studies were carried out by researchers affiliated to Spanish Universities. Additionally, co-citation analysis purported the intellectual structure of TDC knowledge base by identifying the most influential authors and documents. Finally, co-occurrence analysis revealed the concept analysis topical foci of TDC research. These topics are concentrated on “teachers’ digital competence”, “higher education studies”, teacher training programs”, and “ICT in education”. As a result, based on the findings of the study some recommendations were proposed that will contribute into the ICT research community by reflecting the intellectual structure of existing TDC research, thus highlighting the future research direction.

Nutrition and sarcopenia: Current knowledge domain and emerging trends.

Non-pharmacological management like nutrient supplements has shown positive impacts on muscle mass and strength, which has burgeoned clinical and research interest internationally. The aim of this study was to analyze the current knowledge domain and emerging trends of nutrition-related research in sarcopenia and provide implications for future research and strategies to prevent or manage sarcopenia in the context of aging societies. Nutrition- and sarcopenia-related research were obtained from the Web of Science Core Collection (WoSCC) database from its inception to April 1, 2022. Performance analysis, science mapping, and thematic clustering were performed by using the software VOSviewer and R package "bibliometrix." Bibliometric analysis (BA) guideline was applied in this study. A total of 8,110 publications were extracted and only 7,510 (92.60%) were selected for final analysis. The production trend in nutrition and sarcopenia research was promising, and 1,357 journals, 107 countries, 6,668 institutions, and 31,289 authors were identified in this field till 2021. Stable cooperation networks have formed in the field, but they are mostly divided by region and research topics. Health and sarcopenia, metabolism and nutrition, nutrition and exercise, body compositions, and physical performance were the main search themes. This study provides health providers and scholars mapped out a comprehensive basic knowledge structure in the research in the field of nutrition and sarcopenia over the past 30 years. This study could help them quickly grasp research hotspots and choose future research projects.

Development of knowledge base structure for gout diagnosis

Gout belongs to the group of microcrystalline arthritides and is a systemic tophus manifested by inflammation of various tissues caused by deposition of monosodium urate crystals. Verification of gout diagnosis in Russia takes on average four to eight years from the beginning of the disease. This period is sufficient for the development of various complications (for example, gouty nephropathy) and disability of patient, and, therefore, it is necessary to revise the existing strategy for the diagnosis of this disease. One of the options for improving the provision of medical care can be attributed to the creation of a clinical decision support system (CDSS), which is based on the knowledge of experts, formalized as a knowledge base (KB).Aim. To develop a KB structure for CDSS for gout diagnosis.Material and Methods. Clinical information for gout diagnosis, presented as a nomenclature of medical concepts and logical schemes, which were collected on the basis of federal clinical recommendations, various literature sources and expert knowledge were used as materials. The ontological method was used as a method of knowledge structuring. Combined semantic network and frames were used as the methods of representation.Results. While developing KB structure, a combination of two methods of knowledge representation including semantic network and frames was used. An ontological approach was used in terms of knowledge structuring. The structure was built on the clinical knowledge collected in cooperation with experts in gout diagnosis. Compared with similar developments of diagnosing diseases based on knowledge engineering methods, the main feature of developed KB structure was the use of a separate type “Syndrome” concept as an aggregator accepted in medicine, significantly reducing the volume of KB for diseases.Conclusion. The KB structure was developed comprising the use of seven types of concepts and 11 types of relationships. The structure involved the use of ontological approach and combination of two models of knowledge representation, namely: a semantic network and a frame model.

OncoPubMiner: a platform for mining oncology publications

Updated and expert-quality knowledge bases are fundamental to biomedical research. A knowledge base established with human participation and subject to multiple inspections is needed to support clinical decision making, especially in the growing field of precision oncology. The number of original publications in this field has risen dramatically with the advances in technology and the evolution of in-depth research. Consequently, the issue of how to gather and mine these articles accurately and efficiently now requires close consideration. In this study, we present OncoPubMiner (https://oncopubminer.chosenmedinfo.com), a free and powerful system that combines text mining, data structure customisation, publication search with online reading and project-centred and team-based data collection to form a one-stop 'keyword in-knowledge out' oncology publication mining platform. The platform was constructed by integrating all open-access abstracts from PubMed and full-text articles from PubMed Central, and it is updated daily. OncoPubMiner makes obtaining precision oncology knowledge from scientific articles straightforward and will assist researchers in efficiently developing structured knowledge base systems and bring us closer to achieving precision oncology goals.

Research Status, Hotspots, and Evolutionary Trends of Intelligent Education from the Perspective of Knowledge Graph

Intelligent education research has become a research hotspot in recent years. The Citespace software that operates a graph visualization function was used to clarify the current situation, hot spots, and evolutionary trends of intelligent education research development; the authors, institutions, and countries engaged in intelligent education research, as well as the basic knowledge structure, main keywords, citation clustering, dual-map overlay of journals and citation emergence of intelligent education research. The results show that the annual number of publications in the field has shown an upward trend since 2010, with strong communication among research institutions and countries, but weak communication among researchers. Among them, the United States is the center of the global collaborative network of intelligent education research. The basic knowledge structure of intelligence education research is mainly composed of Classroom Management, Evaluation Index, 5G Network, and Big Data Analytics. The dual-map overlay analysis of journals shows that the core areas of intelligence education are increasing, and the analysis of keywords and cited literature shows that Intelligence Tutoring System, AI system, Students and Education, Model, and System are high-frequency words with high-intensity burstness. In addition, research on intelligent education is characterized by multi-country, multi-field, and multi-disciplinary integration, and the adoption of Big Data, Distance Education Technology and Artificial Intelligence Technology to provide scientific support for teaching and learning will become the key research content in this field in the future.

Global research trends in MERS-CoV: A comprehensive bibliometric analysis from 2012 to 2021.

BackgroundThe Middle East respiratory syndrome coronavirus (MERS-CoV) was first reported in Saudi Arabia in 2012. So far, the cases of MERS-CoV have been reported in 27 countries. The virus causes severe health complications, resulting high mortality.AimThe current study aimed to evaluate the global research trends and key bibliometric indices in MERS-CoV research from 2012 to 2021.MethodsA retrospective bibliometric and visualized study was conducted. The Science Citation Index Expanded Edition of Web of Science Core Collection database was utilized to retrieve published scientific literature on MERS-CoV. The retrieved publications were assessed for a number of bibliometric attributes. The data were imported into HistCiteTM and VOSviewer software to calculate the citations count and perform the visualization mapping, respectively. In addition, countries or regions collaboration, keywords analysis, and trend topics in MERS-CoV were assessed using the Bibliometrix: An R-tool.ResultsA total of 1,587 publications, published in 499 journals, authored by 6,506 authors from 88 countries or regions were included in the final analysis. Majority of these publications were published as research article (n = 1,143). Globally, these publications received 70,143 citations. The most frequent year of publication was 2016 (n = 253), while the most cited year was 2014 (11,517 citations). The most prolific author was Memish ZA (n = 94), while the most published journal was Emerging Infectious Diseases (n = 80). The United States of America (USA) (n = 520) and Saudi Arabia (n = 432) were the most influential and largest contributors to the MERS-CoV publications. The extensively studied research area was infectious diseases. The most frequently used author keywords other than search keywords were Saudi Arabia, SARS-CoV-2, COVID-19, epidemiology, transmission, spike protein, vaccine, outbreak, camel, and pneumonia.ConclusionThis study provides an insight into MERS-CoV-related research for scientific community (researchers, academicians) to understand and expand the basic knowledge structure, potential collaborations, and research trend topics. This study can also be useful for policy makers. After the emergence of MERS-CoV, a significant increase in scientific production was observed in the next 4 years (2013–2016). In 2021, the trend topics in MERS-CoV-related research were COVID-19, clinical characteristics, and cytokine storm. Saudi Arabia had the strongest collaboration with the USA, while the USA had the highest collaboration with China.

Использование онтологических шаблонов содержания при построении баз знаний для технического обслуживания и ремонта авиационной техники

The article presents a method for creating knowledge base prototypes based on ontological design patterns and decision tables. The method contains the following steps: building an ontology of the subject area based on the ontological template of the content; formation of a production knowledge base based on the transformation of elements of the ontology of the subject area; filling the rule base using decision tables, as well as their interpretation and debugging. An ontological content template has been developed that describes the maintenance and repair process in accordance with current standards. The transformation of the ontology into a production model and the generation of decision tables were carried out using a programming system for production knowledge bases. In order to involve non-programming end users in the process of filling knowledge base structures with specific rules, a spreadsheet editor was used. A meaningful assessment of the obtained knowledge base was carried out using the author's shell program. The method and template proposed in the paper were tested in solving the problem of finding the causes of malfunctions and forming a sequenceof maintenance and repair of the power supply system of the Sukhoi Superjet 100 aircraft.

An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA

Knowledge-based visual question answering (VQA) involves answering questions that require external knowledge not present in the image. Existing methods first retrieve knowledge from external resources, then reason over the selected knowledge, the input image, and question for answer prediction. However, this two-step approach could lead to mismatches that potentially limit the VQA performance. For example, the retrieved knowledge might be noisy and irrelevant to the question, and the re-embedded knowledge features during reasoning might deviate from their original meanings in the knowledge base (KB). To address this challenge, we propose PICa, a simple yet effective method that Prompts GPT3 via the use of Image Captions, for knowledge-based VQA. Inspired by GPT-3’s power in knowledge retrieval and question answering, instead of using structured KBs as in previous work, we treat GPT-3 as an implicit and unstructured KB that can jointly acquire and process relevant knowledge. Specifically, we first convert the image into captions (or tags) that GPT-3 can understand, then adapt GPT-3 to solve the VQA task in a few-shot manner by just providing a few in-context VQA examples. We further boost performance by carefully investigating: (i) what text formats best describe the image content, and (ii) how in-context examples can be better selected and used. PICa unlocks the first use of GPT-3 for multimodal tasks. By using only 16 examples, PICa surpasses the supervised state of the art by an absolute +8.6 points on the OK-VQA dataset. We also benchmark PICa on VQAv2, where PICa also shows a decent few-shot performance.

Dialogue System Augmented with Commonsense Knowledge

Building an open-domain dialog system is a challenging task in current research. In order to successfully maintain a conversation with human, a dialog system must develop many qualities: being engaging, empathetic, show a unique personality and having general knowledge about the world. Prior research has shown that it is possible to develop such chat-bot system that combines these features, but this work explores this problem further. Most state-of-theart dialogue systems are guided by unstructured knowledge such as Wikipedia articles, but there is a lack of research on how structured knowledge bases can be used for open-domain dialogue generation. This work proposes usage of structured knowledge base ConceptNet for knowledge-grounded dialogue generation. Novel knowledge extraction algorithm is proposed which is then used to incorporate knowledge into existing dialogue datasets. Current state-of-theart model BlenderBot is finetuned on new datasets which shows improvement in novelty of utterances generated by the model.

A Model for the Intelligent Supervision of Production for Industry 4.0

Nowadays, the automation of production processes requires qualified engineering staff; unfortunately, such personnel are in very short supply in all EU countries. This article proposes a concept for a model for the intelligent supervision of production for Industry 4.0., the implementation of which will allow the demand for highly skilled engineering staff to be reduced within a company. An analysis of the literature, dedicated to manufacturing enterprises, regarding the intelligent supervision of production systems, is carried out in the article. It follows, therefore, that the Industry 4.0 concept assumes that mechanisms will be implemented, in production resources, in order to enable preventive measures to be taken, vis-à-vis breakdowns, failures and disruptions to the operation of devices. The need to develop a model for the intelligent supervision of production systems, in the face of challenges within the concept of Industry 4.0, is proposed. This model includes the following elements: (1) a configurator allowing devices to be selected for measuring production parameters, (2) a database for registering production system parameters, (3) Convolutional Neural Networks (CNN), as the prediction algorithm and (4) a knowledge-based structure, including operating procedures and good practices for preventing emergency situations, threats and excessive energy consumption in production systems. The usefulness of this model for predictive maintenance, safety and energy efficiency, vis-à-vis the use of production resources, as well as in support of middle management for decisions taken by employees, is described.

Knowledge structure and emerging trends on osteonecrosis of the femoral head: a bibliometric and visualized study

BackgroundOsteonecrosis of the femoral head (ONFH) is a common disabling disease with considerable social and economic impacts. Although extensive studies related to ONFH have been conducted in recent years, a specific bibliometric analysis on this topic has not yet been performed. Our study attempted to summarize the comprehensive knowledge map, development landscape, and future directions of ONFH research with the bibliometric approach.MethodsAll publications concerning ONFH published from 2001 to 2020 were identified from Web of Science Core Collection. Key bibliometric indicators were calculated and evaluated using CiteSpace, VOSviewer, and the online bibliometric analysis platform.ResultsA total of 2594 publications were included. Our analysis revealed a significant exponential growth trend in the annual number of publications over the past 20 years (R2 = 0.9663). China, the USA, and Japan were the major contributors both from the quality and quantity points of view. Correlation analysis indicated that there was a high positive correlation between the number of publications and gross domestic product (r = 0.774), and a moderate positive correlation between publications and demographic factor (r = 0.673). All keywords were categorized into four clusters including Cluster 1 (etiology and risk factors study); Cluster 2 (basic research and stem cell therapy); cluster 3 (hip-preserving study); and Cluster 4 (hip replacement study). Stem cell therapy-related research has been recognized as an important research hotspot in this field. Several topics including exosomes, autophagy, biomarkers, osteogenic differentiation, microRNAs, steroid-induced osteonecrosis, mesenchymal stem cells, double-blind, early-stage osteonecrosis, and asymptomatic osteonecrosis were considered as research focuses in the near future.ConclusionOver the past two decades, increasing attention has been paid to global ONFH-related research. Our bibliometric findings provide valuable information for researchers to understand the basic knowledge structure, identify the current research hotspots, potential collaborators, and future research frontiers in this field.

Building structured Knowledge Base for trustable NLP application systems by Resource by Collaborative Construction scheme

Wikipedia is an exhaustive resource that contains too much information for any one human to completely absorb. Computers, on the other hand, are able to trawl through information at a rapid pace. However, as Wikipedia is written in such a way that it is clear for people to read, it is not possible for a machine to easily utilise and manipulate this information. Dr Satoshi Sekine is based within the Language Information Access Technology Team at the RIKEN Center for Advanced Intelligent Project, Japan. He is the team leader of a project called SHINRA that is centred around a Resource by Collaborative Contribution (RbCC) scheme and seeks to build a structured knowledge base that combines Wikipedia and an Extended Named Entity (ENE). The RbCC scheme enables the team to work together to construct a resource to advance the field of deep learning. ENE is a hierarchy that is divided into name, time and numerical expressions and the team found that, generally speaking, any question on any specific matter fits within one of these three categories and means that information can potentially be understood by machines engaged in deep learning. The researchers worked to assign appropriate concept class labels and then used references to existing online thesaurus entries and ontology sites to find information that matched the hierarchy. Ultimately, Sekine and the team want to build a structured Wikipedia and to do this categorisation, attribute extraction and attribute value linking will need to be performed.