Creation Of Knowledge Base Research Articles

Deriving comprehensive literature trends on multi-omics analysis studies in autism spectrum disorder using literature mining pipeline

Autism spectrum disorder (ASD) is characterized by highly heterogenous abnormalities in functional brain connectivity affecting social behavior. There is a significant progress in understanding the molecular and genetic basis of ASD in the last decade using multi-omics approach. Mining this large volume of biomedical literature for insights requires considerable amount of manual intervention for curation. Machine learning and artificial intelligence fields are advancing toward simplifying data mining from unstructured text data. Here, we demonstrate our literature mining pipeline to accelerate data to insights. Using topic modeling and generative AI techniques, we present a pipeline that can classify scientific literature into thematic clusters and can help in a wide array of applications such as knowledgebase creation, conversational virtual assistant, and summarization. Employing our pipeline, we explored the ASD literature, specifically around multi-omics studies to understand the molecular interplay underlying autism brain.

An Exploration of IFLA LRM for Literature Data Representation

The digital humanities have witnessed a clear development in recent years due partly to their adoption of Semantic Web and linked data technologies and the creation of knowledge bases. In this work, we target the creation of an ontology and knowledge base for literature data representation based on the IFLA Library Reference Model (LRM). IFLA LRM is the main model for book-related data, allowing for a fine representation of the various layers that constitute a book. However, by design, it doesn’t deal with some aspects usually available in literature databases, such as information about authors, literary awards or book themes. As a result, LRM requires some extensions to be able to represent ancillary data. Another challenge is the querying of IFLA LRM knowledge bases, with a performance cost that comes with the fine-grained expressivity of the LRM model, which creates longer and therefore typically slower SPARQL queries. In this work, we propose an extension to the IFLA LRM ontology called IFLA LRM* that targets these limitations including a connection to the vocabulary Schema.org and to the taxonomies Thema and Dewey Decimal, and the representation of literary awards. We also present a practical case study on using our extended model to create a Quebec literature knowledge base, discussing the interest of our extensions.

Increasing the explainability and trustiness of Wang-Mendel fuzzy system for classification problems

Fuzzy systems are the perfect systems for knowledge representation-the fuzzy rules on which they are based can be interpreted and explained during the presentation of the system’s results. In this paper, a new method for fuzzy system optimization is proposed in which the fuzzy rules are built with the Wang–Mendel approach. The proposed method allows taking into account a new additional criterion during optimization - trustiness, which is aimed at obtaining better trust in the created knowledge base. In addition, a new criterion of explainability and explainable interface are proposed, which are aimed at presenting fuzzy rules in an explainable form. The proposed approach was tested in many variants and compared to methods from the literature. The results of the simulations showed that not only the accuracy of the classification was improved, but also the resulting rules had less significant conflicts, and the explanation of the classification was more transparent. Such significant results confirm the effectiveness of the proposed method-it not only allowed for the simultaneous improvement of the three mentioned elements without any trade-offs compared to known methods but also provided further development potential in this direction.

The Notion of Accountability in the Public Sector

Purpose: The purpose of this presentation is to investigate the various views, perceptions and attitudes of people with regards to the notion of accountability within the public sector with the hope of suggesting, recommending and creating knowledge base that can be utilized to enhance best accountability practices in the public sector. Players in the public sector accountability roles should be helped to take an active role in shaping the notion of accountability for public effectiveness and efficiency. The current employment accountability system is top-down and excludes players at the bottom to contribute to the formulation of accountability parameters and standards. The notion stems from the assumption that individuals in organizations are held to a certain standard of excellence, hence responsible for their actions and be answerable for the results of their actions. Implementers at the receiving end just receive parcels of finished products for implementation .This creates platforms for those in management positions to take advantage of their counter parts and abuse them in effecting accountability as a tool for control and appraisal .The current practice has opened up avenues for practice of unprofessional and the unethical practices to prevail in work places .Thus , the presentation came up with mitigatory measures to shift the current perception of accountability to a user-friendly approach that will promote unity among players and ensure quality work and service delivery in work places if adopted Material and Methods: This presentation used the qualitative survey methodology to gather data on, ‘The Notion of Accountability in Public Sector. The interpretivist approach was taken on body in this presentation. This philosophy of qualitative survey was selected for use in this presentation as it is powerful when it comes to individual in-depth interviews or questionnaires to collect analyze and interpret data on what people see, believe hear or think about the notion of accountability in the public sector. The methodology clearly reports on the meaning, perceptions, characteristics and descriptions of phenomena. Actually it was chosen for use due to its open endedness and exploratory nature which are basic fundamentals of depth research. Questionnaires were chosen for their ability to solicit definitive information without allowing chances of data ambiguity. Conversely, the interviews were taken on board in this presentation due to their upper hand in gathering qualitative thick descriptive data hence providing the researcher with the much needed data to warranty research finding generalization. The approach allowed random sampling from the study population leading to data gathering, presentation, analysis and discussions. Findings: The research findings revealed a wide range of scenario coming out of the notion of accountability in the public sector. The major finding emanates from the theory x of the trait theories where those in management view employees as naturally lazy and unwilling to do their jobs or task assignment. As such management and public in general expect employees to account for their performance so that they can be appraised where work outcomes are positive to pre-determined goals and be punished where performance fall short of set expectations and goals. Concerns and perceptions of those at the bottom who happens to account to top management are often neglected and not taken seriously leaving those who are supposed to account feeling worthless and not owning the entire process of accountability. Some of the tasks given to employees to carry out are not feasible but the door for grievance and challenge handling is closed since there is a one way information flow system which is predominantly top-down. The long chain of bureaucracy is a barrier to information communication which should be two way. Those in lower levels of management lack clarity of some specific issues but are compelled to ensure task accomplishment by those in top management who have no time and mandate to receive instruction from people at the bottom lines of the accountability chain. Implications to Theory, Practice and Policy: The presentation recommended best accountability practices where people at the lower level of accountability are offered opportunities to contribute to the construction and formulation of accountability guidelines so that they do not become strangers in implementation of tasks meant for them so as to account. This can be achieved through making reviews on basic assumptions under pinning decision making in areas of accountability within the public sector.

Creation of a Medical Knowledge Base for Unify the Development of Clinical Decision Support Systems Based on the National Metathesaurus

Background: The rapid growth in the volume of medical data, the extensive possibilities of information technology, the transfer of medical document flow to electronic format generates a high demand for the introduction of information and reference assistance tools and clinical decision support systems (CDSS). Work on the creation of CDSS currently combines the expert activities of doctors with the work of information technology specialists, mathematical statisticians, data scientists, knowledge engineers. Most of the developments involving the formation of knowledge bases are created in isolation, without the use of universal approaches that allow combining various solutions. At the heart of any medical knowledge base (MKB) there is a thesaurus, which is a systematized dictionary of terms that helps to standardize terminology, which makes it possible to speed up the search and exchange of information. It includes concept terms and relationships between them, as well as synonyms and various attributes. Aims — creation of a national medical metathesaurus, built on the ontological principle and the development of MKB based on it. Methods. International systematized dictionary of medical terms UMLS (Unified Medical Language System); clinical recommendations for 22 groups of nosologies; reference books of the federal portal of normative reference information of the Ministry of Health of the Russian Federation; electronic medical records – 330 thousand (dataset MIMIC-IV); abstracts of PubMed publications-28 million. Semantic analyzers SemRep (Semantic Repository) and MetaMap were used; methods for evaluating lexical similarity, connectivity, contextual combinability of entities in a subgraph, and mathematical statistics. Results. The first version of the Unified National Medical Nomenclature (UNMN) has been created. It is proved that ontological models are an effective way of presenting structured information. Components of information search engines have been created. Analytical tools for working with metathesaurus have been developed. Conclusions. On the basis of the UNMN and the created tools, it is possible to automate the formation of a clinical picture of the disease (knowledge base) and single-platform development of the CDSS.

Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES): a method for populating knowledge bases using zero-shot learning.

Creating knowledge bases and ontologies is a time consuming task that relies on manual curation. AI/NLP approaches can assist expert curators in populating these knowledge bases, but current approaches rely on extensive training data, and are not able to populate arbitrarily complex nested knowledge schemas. Here we present Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES), a Knowledge Extraction approach that relies on the ability of Large Language Models (LLMs) to perform zero-shot learning and general-purpose query answering from flexible prompts and return information conforming to a specified schema. Given a detailed, user-defined knowledge schema and an input text, SPIRES recursively performs prompt interrogation against an LLM to obtain a set of responses matching the provided schema. SPIRES uses existing ontologies and vocabularies to provide identifiers for matched elements. We present examples of applying SPIRES in different domains, including extraction of food recipes, multi-species cellular signaling pathways, disease treatments, multi-step drug mechanisms, and chemical to disease relationships. Current SPIRES accuracy is comparable to the mid-range of existing Relation Extraction methods, but greatly surpasses an LLM's native capability of grounding entities with unique identifiers. SPIRES has the advantage of easy customization, flexibility, and, crucially, the ability to perform new tasks in the absence of any new training data. This method supports a general strategy of leveraging the language interpreting capabilities of LLMs to assemble knowledge bases, assisting manual knowledge curation and acquisition while supporting validation with publicly-available databases and ontologies external to the LLM. SPIRES is available as part of the open source OntoGPT package: https://github.com/monarch-initiative/ontogpt.

@llegra: a chatbot for Vallader

AbstractExtinct and endangered languages have been preserved primarily through audio conservation and the collection and digitization of scripts and have been promoted through targeted language acquisition efforts. Another possibility would be to build conversational agents like chatbots or voice assistants that can master these languages. This would provide an artificial, active conversational partner which has knowledge of the vocabulary and grammar and allows one to learn with it in a different way. The chatbot, @llegra, with which one can communicate in the Rhaeto-Romanic idiom Vallader was developed in 2023 based on GPT-4. It can process and output text and has voice output. It was additionally equipped with a manually created knowledge base. After laying the conceptual groundwork, this paper presents the preparation and implementation of the project. In addition, it summarizes the tests that native speakers conducted with the chatbot. A critical discussion elaborates advantages and disadvantages. @llegra could be a new tool for teaching and learning Vallader in a memorable and entertaining way through dialog. It not only masters the idiom, but also has extensive knowledge about the Lower Engadine, that is, the area where Vallader is spoken. In conclusion, it is argued that conversational agents are an innovative approach to promoting and preserving languages.

Deploying a Building Information Modelling (BIM)-Based Construction Safety Risk Library for Industry: Lessons Learned and Future Directions

A continuing need to improve health, safety and wellbeing in construction has led to multiple research projects and technological innovations. One such innovation is the Safety Risk Library: a repository of data that functions in BIM environments to assist designers and contractors in identifying health and safety risk scenarios and offer suitable validated treatments to mitigate their effects. This paper reports on the deployment of this library in several construction projects across the United Kingdom and reviews expert and practitioner opinions of such digital solutions for improving health and safety in the future. This paper makes several contributions. The description of an effective process for knowledge base creation, including the data extraction workflow, the anonymization of data and the definition of communication channels aligned to project working practices, is instructive for innovation developers, providing informative guidance through lessons learned. The discussion of expert and practitioner opinions of the functional knowledge base to improve health and safety performance could inform further technological developments in the field and provide empirical insights for developers. Additionally, the alignment of the Safety Risk Library to existing industry standards (PAS1192:6) for better sharing and use of structured health and safety information illustrates how digital solutions can connect directly with industry standards to facilitate improvements to working practices whilst also changing perceptions of how risks may be visualised, understood and actioned by duty holders engaged in construction projects.

InstructNet: A novel approach for multi-label instruction classification through advanced deep learning.

People use search engines for various topics and items, from daily essentials to more aspirational and specialized objects. Therefore, search engines have taken over as people's preferred resource. The "How To" prefix has become familiar and widely used in various search styles to find solutions to particular problems. This search allows people to find sequential instructions by providing detailed guidelines to accomplish specific tasks. Categorizing instructional text is also essential for task-oriented learning and creating knowledge bases. This study uses the "How To" articles to determine the multi-label instruction category. We have brought this work with a dataset comprising 11,121 observations from wikiHow, where each record has multiple categories. To find out the multi-label category meticulously, we employ some transformer-based deep neural architectures, such as Generalized Autoregressive Pretraining for Language Understanding (XLNet), Bidirectional Encoder Representation from Transformers (BERT), etc. In our multi-label instruction classification process, we have reckoned our proposed architectures using accuracy and macro f1-score as the performance metrics. This thorough evaluation showed us much about our strategy's strengths and drawbacks. Specifically, our implementation of the XLNet architecture has demonstrated unprecedented performance, achieving an accuracy of 97.30% and micro and macro average scores of 89.02% and 93%, a noteworthy accomplishment in multi-label classification. This high level of accuracy and macro average score is a testament to the effectiveness of the XLNet architecture in our proposed 'InstructNet' approach. By employing a multi-level strategy in our evaluation process, we have gained a more comprehensive knowledge of the effectiveness of our proposed architectures and identified areas for forthcoming improvement and refinement.

FEATURES OF THE WORK ORGANIZATION OF INSURANCE COMPANIES OF UKRAINE DURING THE PERIOD OF ARMED INVASION

The article is devoted to the definition of the main aspects of the organization of work of insurance companies of Ukraine during the period of armed invasion. The purpose of the article is to determine the effects on the work of insurance companies in the modern conditions of the armed invasion of Russian aggressors and to provide proposals regarding the priority areas of insurance management. The object of the research is the processes of organization and management of the work of insurance companies. The subject of the study is the ways of optimizing the work of insurers in conditions of external and internal negative influences. The research used general scientific and special research methods: comparative analysis, scientific abstraction and generalization of the scientific experience of modern theoretical studies, a system-complex approach. When conducting an analysis of the dynamics of the number of insurance organizations and gross insurance payments and premiums for the period 2017-2022, it was found that fluctuations in their volumes also occurred in pre-war times. The main reasons that made it possible to effectively reduce the number of insurance service providers without a significant loss of the client base, while simultaneously increasing the guarantee requirements for insurers, were identified. It has been proven that even in the pre-war period, conditions were created for strengthening requirements for corporate governance, introducing a civilized procedure for withdrawing insolvent insurers from the market, and implementing Solvency II. It is noted that Russia's sudden insidious attack caused a wave of violations and disruptions in the work of almost all domestic business entities. revealed that during 2022, the activities of insurance companies and other participants of the financial market of Ukraine took place in the conditions of the maximum limit of destabilization, and the volume of services fell significantly compared to the pre-war period. The structure of assets and liabilities of life insurers and risk insurers in 2022 was studied, and the largest insurance premiums and payments by region of Ukraine as of January 1, 2023 were identified.According to the results of the research conducted by Mrthe development and further development within the framework of state programs and on a unified conceptual basis of the methodology of managing insurance companies in conditions of war, the creation and expansion of a modern intellectual knowledge base of this subject area are recognized as priority areas of insurance management. It was emphasized that measures to ensure the organization of the work of insurance companies during the armed invasion should be based on advanced management techniques using the latest technologies.

The Adoption and Implementation of Artificial Intelligence Chatbots in Public Organizations: Evidence from U.S. State Governments

Although the use of artificial intelligence (AI) chatbots in public organizations has increased in recent years, three crucial gaps remain unresolved. First, little empirical evidence has been produced to examine the deployment of chatbots in government contexts. Second, existing research does not distinguish clearly between the drivers of adoption and the determinants of success and, therefore, between the stages of adoption and implementation. Third, most current research does not use a multidimensional perspective to understand the adoption and implementation of AI in government organizations. Our study addresses these gaps by exploring the following question: what determinants facilitate or impede the adoption and implementation of chatbots in the public sector? We answer this question by analyzing 22 state agencies across the U.S.A. that use chatbots. Our analysis identifies ease of use and relative advantage of chatbots, leadership and innovative culture, external shock, and individual past experiences as the main drivers of the decisions to adopt chatbots. Further, it shows that different types of determinants (such as knowledge-base creation and maintenance, technology skills and system crashes, human and financial resources, cross-agency interaction and communication, confidentiality and safety rules and regulations, and citizens’ expectations, and the COVID-19 crisis) impact differently the adoption and implementation processes and, therefore, determine the success of chatbots in a different manner. Future research could focus on the interaction among different types of determinants for both adoption and implementation, as well as on the role of specific stakeholders, such as IT vendors.

INTELLIGENT APPROACH TO LOCAL AND GENERAL PHYSIOTHERAPY DEVICES

This paper discusses the usage of intelligent approach to a physiotherapy. Physiotherapy as a branch of medicine has existed for a long time, but its methods of application remain about the same as they were a hundred years ago. This causes a number of problems, in particular a sceptical attitude towards physiotherapy itself. The use of traditional techniques in this field has a major influence on its attitude. Even though even traditional methods of treating patients with physiotherapy show good results, people are increasingly turning to medication. However, these methods are not directed at the individual patient, with his or her personal problems, and can be detrimental to the patient, let alone the treatment. Every patient, irrespective of gender, race or age, has his or her own individuality. Physiotherapists now prescribe treatment to patients on the basis of their findings and personal experience. But this is not always the right approach, especially if we're talking about the severely or chronically ill patients.But we are lucky to live in the era of computer progress, when machines can not only help in determining the correct diagnosis, but also autonomously make decisions about the patient's treatment. With this modern approach to health care, it is even possible to automate certain areas. A global collection of data on patients, with their different illnesses and experiences, will help. By processing this data, we can create knowledge bases, with different patterns, so that the patient's treatment plan will be as appropriate and accurate as possible. This approach is already being used in other areas of medicine, for instance for the recognition of X-ray images. Using fuzzy logic, machine learning and artificial intelligence algorithms, data from databases can be used to create predefined patient treatment patterns. This can help in determining the diagnosis and prescribing the right treatment in cases where doctors take a long time to decide on the right approach. Keywords: Physiotherapy devices, Fuzzy logic, Machine learning, Artificial Intelligence, Data bases, Biofeedback.

PubExN: An Automated PubMed Bulk Article Extractor with Affiliation Normalization Package.

Biomedical article extraction is the preliminary step for every biomedical application. These applications are helpful in finding the gene, disease, chemical, drugs, protein entities. Finding entities relation such as gene-gene entities, drug-disease interaction, and chemical protein relation the PubExN can be helpful for these types of biomedical applications. In most cases, domain experts do this extraction process on their own. Human interference makes this process time-consuming and there is a high probability, that documents can be missed during the extraction process. To get rid of these complicated processes a python package is introduced to automate the process of bulk extraction from the PubMed database. The extraction process covers all the citation information with the associated abstract. The batch approach is used to extract the bulk extraction. The motivation for the development of PubExN was to provide flexibility for the extraction process of biomedical article's text data from NCBI's PubMed database. Basically, NCBI's PubMed database article contains the article id or can say PubMed-id (PMID), the title of the article, abstract, authors information, etc. This package will benefit many biomedical texts mining research including biomedical named entity recognition, biomedical relation extraction, literature discovery, knowledgebase creation, and various biomedical Natural Language Processing (NLP) tasks. In addition, it could be used in the author name disambiguation problems and new drug discoveries. This package will help save time and extra effort for the extraction and normalization process of PubMed articles.

Knowledge Base Reuse With Frame Representation In Artificial Intelligence Applications

The creation and upkeep of knowledge bases have emerged as the primary issue in artificial intelligence development. The knowledge domain is expanding, and application interoperability is urgently demanded. To accommodate this modification, the knowledge base, which serves as the foundation of the artificial intelligence architecture, must be updated. XML (extensible markup language), RDF (resources description framework), DAML (DARPA agent markup language), OIL (ontology inference layer), and OKBC are just a few of the standards that have been introduced as a result of the development of the internet (Open Knowledge-Base Connectivity). One type of knowledge representation that can store information and facts about a subject is frame-based representation. The OKBC, which can help with knowledge base interoperability, supports this approach. In this work, the main issues covered are the comprehension and fundamentals of knowledge base creation with frame representation. To better understand how frame representation functions as a reusable component, a case study from a news publication were used as an example.

Becoming an Occupation? A Research Agenda Into the Professionalization of the Sport for Development and Peace Sector

Sport for Development and Peace (SDP) has transformed from what some termed a “social movement” to an institutionalized sector with numerous organizations and practitioners involved, resulting in trends that point toward SDP becoming a recognized category of work through professional training. The purpose of this paper is to utilize theories of professions and institutional isomorphism to advance the significance and importance of thinking about SDP as a profession. Three emerging trends that point to the professionalization of SDP are reviewed: (a) increasing opportunities to attain SDP certifications and training, (b) the growing number of SDP-specific academic degrees, and (c) the creation of a SDP knowledge base, particularly in relation to monitoring and evaluation. To conclude, theoretical and practical implications of the professionalization of SDP are discussed and a research agenda is outlined for future research on the continued institutionalization, and professionalization, of the SDP sector.

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.

Hierarchical Fuzzy-Logic Expert Systems for Medical Diagnostics

Unlike numerous publications on the application of fuzzy logic in medicine, where the main attention was paid to the medical features of the problem, this work for the first time formulates the most effective areas and general methodological aspects of constructing such systems. The structure, methods of creating knowledge bases and the sequence of decision-making based on them by a specialist doctor are presented. The types of membership functions that are advisable to use to represent the symptoms of diseases are proposed. In fuzzy-logical conclusions, not only the values of the characteristic functions of the logical terms of individual symptoms are used, but also complex arithmetic functions of their values.

Artificial Intelligence-Virtual Trainer: Innovative Didactics Aimed at Personalized Training Needs

First, the benefits of AI-based training for business development are presented. Then, the theoretical literature on various stages from traditional training to AI-based training is analyzed. Finally, we analyze AI applications in the training process and managerial response. This study presents a framework for the application of artificial intelligence (AI) technology to training and managerial challenges. AI tools can be applied to the training process, including knowledge management (KM), needs analysis, training organization, and results feedback. AI-based training transforms organizations into knowledge organizations that can meet the demands of personalized training and improve learning quality. AI tools bring about a shift in the training phases of knowledge base creation, needs surveys, the organization of training, and feedback on results.

Knowledge base creation for nitrogen fertilization towards crops

The use of intelligent technologies for the systematization of agrotechnological knowledge, by creating a specialized software system for storing and presenting knowledge in the field of crop production, represents one of the most promising areas for improving the efficiency of agricultural production planning. The paper deals with the relevant issues of creating a knowledge base for information support system of the operator’s activities on the mineral fertilizer application. The increased requirements for modern agriculture to be resource-saving and environmentally friendly stem from the development of this direction. The software LogicGem, an easy-to-use tool for creating, editing, checking and compiling decision table logic, was used to build a knowledge base.

Using Wikipedia's Big Data for creation of Knowledge Bases

Wikipedia is among the most prominent and comprehensive sources of information available on the WWW. However, its unstructured form impedes direct interpretation by machines. Knowledge Base (KB) creation is a line of research that enables interpretation of Wikipedia's concealed knowledge by machines. In light of the efficacy of KBs for the storage and efficient retrieval of semantic information required for powering several IT applications such Question-Answering System, many large-scale knowledge bases have been developed. These KBs have employed different approaches for data curation and storage. The retrieval mechanism facilitated by these KBs is also different. Further, they differ in their depth and breadth of knowledge. This paper endeavours to explicate the process of KB creation using Wikipedia and compare the prominent KBs developed using the big data of Wikipedia.