Structure Of Knowledge Base Research Articles

Методологическое обоснование содержания, структуры и программной реализации базы геопространственных знаний территориальных образований

The article deals with substantiating the contents, structure and software implementation of the geospatial knowledge base. The importance and relevance of forming its infrastructure and the corresponding informational base on the territory in modern geoinformatics and cartography are noted. The requirements for the contents, formal and logical representation of the mentioned base, its structure and connections of the elements at the logical level are given. The authors consider the software implementation of the processes of searching and issuing geospatial knowledge in a visualized form at the request of untrained users in the field of geoinformatics and cartography. The requisitions and information on the special original software, which allows observing the contents of the geospatial knowledge base, are shown. The effectiveness of visualizing the geospatial knowledge base through interactive configuration of software elements is substantiated, based on the metadata principle proposed by the authors, two formalisms and an algorithm for creating corresponding services for geospatial knowledge and other related data. These formalisms of the client’s parameters and server software applications are described, a variant of the geospatial knowledge base structure is given, and the requirements for the necessary software for the effective solution of the task are formulated. The authors` solution is considered in the form of a complex of multicomponent software, used at working with geodata and creating their own interactive maps by poorly trained non-professional users

Examining the ability of big data analytics to investigate financial reporting quality: a comprehensive bibliometric analysis

Purpose This study aims to examine the ability of big data analytics (BDA) to investigate financial reporting quality (FRQ), identify the knowledge base and conceptual structure of this research field and explore BDA techniques used over time. Design/methodology/approach This study uses a comprehensive bibliometric analysis approach (performance analysis and science mapping) using software packages, including Biblioshiny and VOSviewer. Multiple analyses are conducted, including authors, sources, keywords, co-citations, thematic evolution and trend topic analysis. Findings This study reveals that the intellectual structure of using BDA in investigating FRQ encompasses three clusters. These clusters include applying data mining to detect financial reporting fraud (FRF), using machine learning (ML) to examine FRQ and detecting earnings management as a measure of FRQ. Additionally, the results demonstrate that ML and DM algorithms are the most effective techniques for investigating FRQ by providing various prediction and detection models of FRF and EM. Moreover, BDA offers text mining techniques to detect managerial fraud in narrative reports. The findings indicate that artificial intelligence, deep learning and ML are currently trending methods and are expected to continue in the coming years. Originality/value To the best of the authors’ knowledge, this study is the first to provide a comprehensive analysis of the current state of the use of BDA in investigating FRQ.

Enhancing knowledge graphs with microdata and LLMs: the case of Schema.org and Wikidata in touristic information

PurposeKnowledge graphs (KGs) are structured knowledge bases that represent real-world entities and are used in a variety of applications. Many of them are created and curated from a combination of automated and manual processes. Microdata embedded in Web pages for purposes of facilitating indexing and search engine optimization are a potential source to augment KGs under some assumptions of complementarity and quality that have not been thoroughly explored to date. In that direction, this paper aims to report results on a study that evaluates the potential of using microdata extracted from the Web to augment the large, open and manually curated Wikidata KG for the domain of touristic information. As large corpora of Web text is currently being leveraged via large language models (LLMs), these are used to compare the effectiveness of the microdata enhancement method.Design/methodology/approachThe Schema.org taxonomy was used as the source to determine the annotation types to be collected. Here, the authors focused on tourism-related pages as a case study, selecting the relevant Schema.org concepts as point of departure. The large CommonCrawl resource was used to select those annotations from a large recent sample of the World Wide Web. The extracted annotations were processed and matched with Wikidata to estimate the degree to which microdata produced for SEO might become a valuable resource to complement KGs or vice versa. The Web pages themselves can also serve as a context to produce additional metadata elements using them as context in pipelines of an existing LLMs. That way, both the annotations and the contents itself can be used as sources.FindingsThe samples extracted revealed a concentration of metadata annotations in only a few of the relevant Schema.org attributes and also revealed the possible influence of authoring tools in a significant fraction of microdata produced. The analysis of the overlapping of attributes in the sample with those of Wikidata showed the potential of the technique, limited by the disbalance of the presence of attributes. The combination of those with the use of LLMs to produce additional annotations demonstrates the feasibility of the approach in the population of existing Wikidata locations. However, in both cases, the effectiveness appears to be lower in the cases of less content in the KG, which are arguably the most relevant when considering the scenario of an automated population approach.Originality/valueThe research reports novel empirical findings on the way touristic annotations with a SEO orientation are being produced in the wild and provides an assessment of their potential to complement KGs, or reuse information from those graphs. It also provides insights on the potential of using LLMs for the task.

Research hot spots and trends in endocrine-related adverse events caused by immune checkpoint inhibitors: a bibliometric analysis and visualization research.

In recent years, with the widespread use of immune checkpoint inhibitors (ICIs) in cancer treatment, the toxicity associated with immunotherapy of ICIs has attracted more attention from scholars. Endocrine toxicity is the most likely immune-related adverse events (irAEs) and is often irreversible, posing a significant clinical treatment challenge. In this study, bibliometric methods were used to analyze relevant literature in screening endocrine-related adverse events caused by ICIs in the Web of Science core collection database (WoSCC) and to summarize the status, research hot spots, and future trends in this field. 321 countries, 297 institutions, 365 authors, and 305 journals had published 671 English documents on endocrine adverse reactions of ICIs as of 1 December, 2022. The United States, Japan, and China were the top three countries with the most publications. The University of Texas MD Anderson Cancer Center, Harvard Medical School, and Memorial Sloan Kettering Cancer Center were the top three research institutions in terms of publication output. F Stephen Hodi, from the Dana-Farber Cancer Institute in the United States, contributed the largest number of publications. Frontiers in Oncology, which was the most widely distributed publication in the field. The main keywords or clusters identified that current research hotspots include the management of endocrine-related adverse events, hypophysitis, thyroid dysfunction, type I diabetes mellitus, and the impact of endocrine adverse events on survival of patients in this field. The basic knowledge structure of the field of endocrine-related adverse events of ICIs, including publication trends, authors, institutions, countries, keywords, journals and publications, and cited documents, was visually analyzed in this bibliometric analysis. The research results comprehensively demonstrated the hot spots and future trends in the research field, as well as its broad prospects, thus providing a reference for the researchers.

K-PathVQA: Knowledge-Aware Multimodal Representation for Pathology Visual Question Answering.

Pathology imaging is routinely used to detect the underlying effects and causes of diseases or injuries. Pathology visual question answering (PathVQA) aims to enable computers to answer questions about clinical visual findings from pathology images. Prior work on PathVQA has focused on directly analyzing the image content using conventional pretrained encoders without utilizing relevant external information when the image content is inadequate. In this paper, we present a knowledge-driven PathVQA (K-PathVQA), which uses a medical knowledge graph (KG) from a complementary external structured knowledge base to infer answers for the PathVQA task. K-PathVQA improves the question representation with external medical knowledge and then aggregates vision, language, and knowledge embeddings to learn a joint knowledge-image-question representation. Our experiments using a publicly available PathVQA dataset showed that our K-PathVQA outperformed the best baseline method with an increase of 4.15% in accuracy for the overall task, an increase of 4.40% in open-ended question type and an absolute increase of 1.03% in closed-ended question types. Ablation testing shows the impact of each of the contributions. Generalizability of the method is demonstrated with a separate medical VQA dataset.

Knowledge Base of Algorithmic and Software Base for Intellectualization of Underground Water Supply Facilities

This article examines the importance of the knowledge base in the development of an intelligent algorithmic and software complex of underground water intake facilities. When developing the computerized knowledge base of underground water intake facilities, the management knowledge base, the classification of knowledge bases, the use of conventional knowledge bases for creating expert systems and storing information about the facility will be studied. A knowledge base structure will be developed when creating an intelligent algorithmic and software complex of underground water intake facilities. When organizing the process of forming underground water intake facilities, the development of a knowledge base for the watertight properties of single-layer moisture layers will be studied. During the formation of underground water intake facilities, knowledge is calculated to solve the issues of determining the watertightness of single-layer earthworks.

The conceptual structure and thematic evolution of the digital leadership knowledge base

The aim of this study is to analyze the Conceptual Structure and Thematic Evolution of the Digital Leadership Knowledge Base. The qualitative method with a literature study approach was employed for this analysis. A positive growth trend towards research on the topic of digital leadership was identified. The topic of digital leadership has been a widely discussed and prominent subject among scientists from 2015 to 2023. The peak occurred in 2022, marking the highest number of published documents on digital leadership. Meanwhile, Germany and Indonesia stand out as the two countries with the highest number of global publications on digital leadership. Despite this, the most influential research on the topic of digital leadership does not originate from these two countries. Marc K. Peter, Corin Kraft, and Johan Lindeque from the United Kingdom emerged as the leading and most influential scientists in the field of digital leadership research. Utilizing this bibliographic methodology is of paramount importance for uncovering research trends related to authors, countries, institutions, and subtopics associated with the primary focus of a study.

Worldwide research trends on tumor burden and immunotherapy: a bibliometric analysis.

Various immunotherapy has been greatly applied to comprehensive treatment of malignant cancer under different degrees of tumor burden. Scientific researchers have gained considerable progress in the relationship between immunotherapy and tumor burden in recent years. This review aimed to explore the prospect and developing trends in the field of tumor burden and immunotherapy from a bibliometric perspective. Articles about tumor burden and immunotherapy were collected from the Web of Science Core Collection (WoSCC) (retrieved on 3 January 2023). The R package 'Bibliometrix' analyzed the primary bibliometric features and created a three-filed plot to display the relationship between institutions, countries, and keywords. VOSviewer was used for co-authorship analysis, co-occurrence analysis, and their visualization. And CiteSpace calculated the citation burst references and keywords. A total of 1030 publications were retrieved from 35 years of scientific researches. The United States (US) and China published the most articles. The most productive journals were Cancer Immunology Immunotherapy and Journal for ImmunoTherapy of Cancer . The top one institution of the highest output was University of Texas MD Anderson Cancer Center. The hot keywords of strong citation burst strength in recent years were 'nivolumab', 'tumor microenvironment', and 'immune checkpoint inhibitor'. The most popular tumor type is melanoma. This bibliometric analysis mapped a basic knowledge structure. The field of tumor burden and immunotherapy is entering a rapid growing stage and keeping it value for future research.

Research trends in insurance risk from 2000–2022: A bibliometric analysis of the literature

This paper aims to document and synthesize research trends in the domain of “insurance risk” over the past 20 years through bibliometric analysis (Hallinger, 2019) of 894 Scopus keyword-based reviews. Publications on insurance risk predominately originate from the United States (U.S.) and China. The most co-cited papers over the past 20 years were published in Insurance: Mathematics and Economics. The journal co-citation analysis (JCA) map identified three main journal fields: finance and risk management, mathematics and statistics, and actuarial science. The authors’ co citation map reveals the intellectual structure of the insurance risk knowledge base, resulting in three leading “schools of thought”: risk management, mathematical and model, and actuarial science. Gerber, H. U. and Tang, Q. are the top scholars in their schools of thought. Recent efforts have focused on processes and technology, as gathering and analyzing a large volume of data requires artificial intelligence-based (AI-based) technologies to support efficient data-driven decisions (Tournas & Bowman, 2021). This helps in developing a robust and faster process for revenue and profit strategies. Considering the structure of the intellectual themes could be beneficial as part of insurance risk businesses and their strategic decisions for future achievements and further improvements.

Chronic kidney diseases and inflammation research: a bibliometric analysis.

Chronic kidney diseases (CKD) is a severe public health problem. This study aimed to explore the field of inflammation-related research in CKD from a bibliometric perspective. Relevant literature published between 2004 and 2023 were searched from the Web of Science database. The bibliometric analysis were performed to summarize countries, institutions, authors, journals and keywords using VOSviewer and CiteSpace. A total of 9,287 publications on CKD and inflammation were included. Publications were mainly from the United States, China, Italy, Germany, and Japan. The findings revealed that the United States had the highest number of publications in this field, followed by China. There is strong collaboration between the two countries. The most productive institutions included the University of California system and the US Department of Veterans Affairs. Research hotspots primarily focused on inflammation mechanisms, biomarkers, and interventions. This study revealed the basic knowledge structure and provided a comprehensive insight into the research field of CKD and inflammation through bibliometric methods. Future studies should focus on early diagnosis, prevention, and treatment strategies of CKD, and explore more inflammation associated biomarkers and therapeutic targets for CKD.

Artificial intelligence applications in bone fractures: A bibliometric and science mapping analysis.

Bone fractures are a common medical issue worldwide, causing a serious economic burden on society. In recent years, the application of artificial intelligence (AI) in the field of fracture has developed rapidly, especially in fracture diagnosis, where AI has shown significant capabilities comparable to those of professional orthopedic surgeons. This study aimed to review the development process and applications of AI in the field of fracture using bibliometric analysis, while analyzing the research hotspots and future trends in the field. Studies on AI and fracture were retrieved from the Web of Science Core Collections since 1990, a retrospective bibliometric and visualized study of the filtered data was conducted through CiteSpace and Bibliometrix R package. A total of 1063 publications were included in the analysis, with the annual publication rapidly growing since 2017. China had the most publications, and the United States had the most citations. Technical University of Munich, Germany, had the most publications. Doornberg JN was the most productive author. Most research in this field was published in Scientific Reports. Doi K's 2007 review in Computerized Medical Imaging and Graphics was the most influential paper. AI application in fracture has achieved outstanding results and will continue to progress. In this study, we used a bibliometric analysis to assist researchers in understanding the basic knowledge structure, research hotspots, and future trends in this field, to further promote the development of AI applications in fracture.

Technological externalities and wages: new evidence from Italian NUTS 3 regions

Abstract In this paper, we investigate the relationship between local wages and the internal structure of the regional knowledge base. The purpose is to assess if the workers’ compensations are related to the peculiarities of the knowledge base of the regions in which they supply their labor services. The test of this hypothesis is based on the assessment of the impact of related vis-à-vis unrelated knowledge variety on cross-regional wage differentials. The empirical analysis is carried out by exploiting patent data and a unique employer–employee administrative dataset. First, using OECD-PATREG data on patent filing, we build information entropy indexes proxying the variety of NUTS 3 regions’ knowledge bases, and the decomposition in the related and unrelated component. Second, we assess the impact of these indexes on wages based on administrative data from the Italian National Institute of Social Security. Our results suggest that workers employed in regions with a heterogenous knowledge structure earn positive wage premia, while related variety has a negative effect on compensation levels.

Hybrid 5G Positioning Systems with Applications to Mobile Robot Technology

A new concept of 5G/INS integration of artificial intelligence-based systems is proposed, namely the Recurrent Neural Networkbased Fuzzy Inference System (RNNFIS). The 5G system with fuzzy logic was used as a reference during its availability. Data from the 5G system and the inertial navigation system (INS) are used to build a structured knowledge base consisting of the behavior of the INS system in some special scenarios of robotic technology. Using the same data, the proposed fuzzy system is trained to obtain corrected navigation data. In the absence of 5G system information, the system will only utilize data from the INS system and blur correction algorithms to perform its tasks.

Biophysical Characterization of p51 and p66 Monomers of HIV-1 Reverse Transcriptase with Their Inhibitors.

Human immunodeficiency virus (HIV)-1 reverse transcriptase (HIV-1 RT) is responsible for the transcription of viral RNA genomes into DNA genomes and has become an important target for the treatment of acquired immune deficiency syndrome (AIDS). This study used biophysical techniques to characterize the HIV-1 RT structure, monomer forms, and the non-nucleoside reverse transcriptase inhibitors (NNRTIs) bound forms. Inactive p66W401A and p51W401A were selected as models to study the HIV-1 RT monomer structures. Nuclear magnetic resonance (NMR) spectroscopy revealed that the unliganded forms of p66W401A protein and p51W401A protein had similar conformation to each other in solution. The complexes of p66W401A or p51W401A with inhibitors showed similar conformations to p66 in the RT heterodimer bound to the NNRTIs. Furthermore, the results of paramagnetic relaxation enhancement (PRE)-assisted NMR revealed that the unliganded forms of the p66W401A and p51W401A conformations were different from the unliganded heterodimer, characterized by a greater distance between the fingers and thumb subdomains. Small-angle X-ray scattering (SAXS) experiments confirmed that p66W401A and p51W401A can bind with inhibitors, similar to the p66/p51 heterodimer. The findings of this study increase the structural knowledge base of HIV-1 RT monomers, which may be helpful in the future design of potent viral inhibitors.

Object and attribute recognition for product image with self-supervised learning

Accurate class and attribute recognition is the critical technique to convert the unstructured product image data into structured knowledge base, which provides strong support for product design in the future. However, objects of different classes sharing similar attribute may vary a lot in visual appearances, making it challenging to accurately recognize the objects and their attributes. Different from the traditional multi-label image recognition, the attribute of the object, as high-level semantic information, is not corresponding to certain regions of the object and requires to learn more fine-grained features to represent the latent high level semantic information for the attribute across different object categories. Therefore, a self-supervised method called Deconstruction and Reconstruction Learning Network (DRLN) is proposed to solve the above problems in this paper. The DRLN tries to learn more fine-grained and local feature of the input product image by a self-supervised task, which deconstructs the input product images by randomly shuffling their local regions and further reconstructs the features of corresponding deconstructed images. Besides, the proposed model is optimized in an end-to-end manner by learning from multiple tasks, i.e., the multi-label classification task, the adversarial discrimination task, and the location alignment task. Experimental results demonstrate that the proposed method outperforms the state-of-the-arts for multi-label learning problems on both our product image dataset and another public available attribute recognition dataset. To facilitate future research in this field, all the datasets and codes are directly available online.11https://github.com/Yong-DAI/DRLN.

Quantity Knowledge Extraction and Search

Vinh Thinh Ho is an applied scientist at Amazon Development Center, working in Alexa AI-NLU team. He completed his PhD at Max Planck Institute for Informatics, under the supervision of Prof. Gerhard Weikum. His research broadly covers the area of semantic web, with main focuses on knowledge bases, quantity search, information extraction and retrieval, rule mining and NLP. This research conducted by Vinh Thinh Ho developed new methods for the extraction and search of quantity knowledge over web contents. Quantities are more than mere numbers. They represent measurements of various entities in the world, such as the heights of buildings, the timings of athletes, the energy efficiency of car models, or the energy production of power plants, all expressed in numbers with associated units. While modern search engines effectively support entity-centric searches and question answering, they struggle when the queries involve quantity filters, such as searching for athletes who ran 200m under 20 seconds or companies with quarterly revenue above $2 Billion. These systems fail to understand the quantities, including the condition (less than, above, etc.), the unit of interest (seconds, dollar, etc.), and the context of the quantity (200m race, quarterly revenue, etc.). QA systems based on structured knowledge bases also fail as quantities are poorly covered by state-of-the-art KBs. We developed new methods to overcome these limitations and advance the state-of-the-art on quantity knowledge extraction and search.

Determinants of digital wallet adoption and super app: A review and research model

Abstract As many companies are transforming through digitalisation to increase efficiency and effectiveness of business processes, financial technology firms innovated the digital wallet as an omnichannel payment to facilitate the digital payments. This review aims to explore the conceptual structure of the digital wallet knowledge base in business and management literature and the determinants that influence the consumer adoption decision of a digital wallet. A bibliometric approach is used to analyse 156 Scopus-indexed documents in this study field. Author co-citation reveals four key schools of thought: digital marketing and consumer behaviour, management and business analytics, mobile commerce and technology, and information and communication technology. The temporal overlay keyword cooccurrence map shows the topical focus changes of research from mobile payment technology, business uses, and determinants of digital wallet adoption. The top cited empirical documents also show that perceived usefulness, perceived ease of use, trust or perceived ability, perceived risk, and perceived value are consumers’ key considerations in choosing digital wallets for payments. This review seeks to contribute to both scientific knowledge and the model of research practice that could provide insights into digital wallet payment. The comprehensive research framework proposes the facilitators and inhibitors which employ the status quo bias theory and presents the mediating effects of the unified theory of acceptance and use of technology constructs on behavioural intention to use a digital wallet, positive word of mouth, and user satisfaction. Four additional determinants augment the existing theories, including perceived trust, perceived security and privacy, promotion, and super app for novelty.

TOXOPLASMOSIS AND SCHIZOPHRENIA PUBLICATION ACTIVITY IN 1991-2023: A WEB OF SCIENCE-BASED BIBLIOMETRIC ANALYSIS

Schizophrenia is a complicated mental condition marked by signs of psychosis, often leading to social and occupational decline. Schizophrenia has a complex etiology that is a result of the interplay between environmental factors (such as infectious illnesses) and a person's genetic disposition. Recent epidemiological research indicates that Toxoplasma gondii may cause schizophrenia. T. gondii-related studies in schizophrenia were the focus of this bibliometric analysis study. The Web of Science’s electronic database was used to find T. gondii-related studies in schizophrenia. Co-authorship analyses of countries and keyword co-occurrence analyses were carried out using the VOSviewer software. In this bibliometric study, the research topic of T. gondii-related publications in schizophrenia during the last 33 years has been laid out, with the nations, organizations, authors, journals, main keywords, citations, and the top documents contributing to the basic knowledge structure. The significant increase in yearly publications implies that this research area has acquired importance on a global scale, with the United States having the most articles. The main organizations and researchers working on T. gondii studies on schizophrenia have been identified by this study. The outcomes give a thorough overview of this study field's terrain.

Process Defects Knowledge Modeling in Laser Powder Bed Fusion Additive Manufacturing: An Ontological Framework

Comprehensive knowledge of the laser powder bed fusion (LPBF) process defects, their causal factors, and relationships can enable proactive prevention and/or mitigation of those defects to ensure the production of high-quality products. However, this knowledge is scattered in a plethora of research articles, and there is a need for a formal and structured knowledge base to document the LPBF process defects knowledge and model the complex network of relationships among those defects and their causal factors. In response, this paper proposes an ontological framework to systematically structure and represent the knowledge of LPBF defects in a sustainable, reusable, and extensible way. In doing so, we first conducted a detailed literature review and analysis of current LPBF defect surveys to systematically develop a consistent and comprehensive classification of defects and potential causal factors. Then, to effectively represent the gathered knowledge, the ontological framework was designed to: (1) organize and formalize knowledge on LPBF defects and the causal factors, (2) model the complex network of causal links and cascading effects among the defects and causal factors, (3) enable easy querying of the stored knowledge, and (4) include ontological entities that are suitable for extension and reuse. A prototype LPBF defects ontology was developed in Web Ontology Language (OWL)/Resource Description Framework (RDF) formalism using the Protégé tool to effectively realize those design requirements. The developed ontology covers thirty-one unique defects and knowledge of their causal factors, including defect-to-defect causal relationships and hierarchical categorization under four major categories of high-level defect classes. Similarly, forty-five unique causal factors were categorized under twelve major categories. The proposed ontological framework and knowledge model offer a pathway to (1) provide a comprehensive knowledge base on LPBF defects for in-depth tutoring and training of novice researchers and practitioners; (2) help investigators to identify root causes of detected defects for informed corrective action; (3) guide process planning tasks from a defect control perspective; and (4) support future application and reuse of the knowledge. This study also provides several examples to illustrate the modeling of cascading effects in causal relationships, discovering knowledge using an ontology reasoner, visualizing the complex network of causal links using OntoGraph, and retrieving stored knowledge by answering competency questions using SPARQL queries. In the future, the reasoning capabilities of our proposed ontology can also be leveraged to develop expert systems for optimizing the AM workflow and quantitatively predict and diagnose LPBF defects.© 2023 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved.This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)Peer-review under responsibility of the Scientific Committee of the NAMRI/SME.

FORMATION OF THE KNOWLEDGE BASE OF DIGITAL LIBRARIES BASED ON SEMANTIC MODELS OF AUTHORITY FILES

This article is devoted to the analysis of methods for creating a knowledge base of a digital library based on the semantic model of authority files. The article also describes the practical aspects of creating an electronic library legal encyclopedia based on the semantic model of authority files. Creation of a semantic network of authority files significantly optimizes the quality and speed of searching for resources in electronic libraries and creates the basis for intellectualization of information retrieval. The article examines in detail the existing approaches to creating a knowledge base of authority files, including files based on semantic models, and forming a knowledge base structure. The main purpose of the study is to create an improved model and develop software for creating a knowledge base of an electronic library of semantic authority files for various subject areas. In the article the theoretical research methods (method of comparison and critical thinking) and empirical research methods (data collection and document analysis) have been applied. Used object-oriented programming, intelligent information retrieval, reverse indexing capabilities. The field of jurisprudence was chosen for the application and approbation of the created model and software. A knowledge base has been created that studies frequently used specialized terms and semantic relationships between them.