Construction and Application of Knowledge Bases

Abstract. This study was conducted to understand the state of research on applications of Knowledge-based View in franchise systems. First, we used SALSA (Search, Appraisal, Synthesis, and Analysis), a simple systematic data search method, to obtain 61 sample papers. Second, the citations of authors and publications were analyzed using the bibliometric method to understand the authors and the publications that had the most impact as well as the trend of current studies in the field of knowledge-based theory application in franchise systems. The results showed that the journals that had the most publications on the topic were Entrepreneurship Theory and Practice and Journal of Business Research; the most cited author was S.A. Shane, who had an average rate of 1.016 citations per article, and the most cited study was a paper published by Darr, Argote, & Epple ( 1995 ), which was cited by 18 of the 61 sample papers (18/61, 29.51%). Third, we categorized the topic of knowledge-based theory application in franchise systems into six categories, i.e., knowledge transfer, knowledge creation, knowledge sharing, knowledge application, organizational learning, and knowledge exchange, to provide references for future studies. Keywords. Knowledge-based view; Franchising; Bibliometrics. JEL. L10.

This chapter discusses the role that research should play in the current discussions about the role of technology in educational development, with particular reference to small developing states. It shows that often decisions about technology use in education are far from being based on evidence and suggests that this is due partly to the absence of a rigorous knowledge base but also of an appropriate research agenda. No doubt, research has a role to play investigating further the role that technology solutions play in improving the quality of education. But the right research questions must be asked. This chapter addresses two questions. First, it looks at what is currently known and the limitations of the existing knowledge base about how technology can contribute to improving learning – recognizing the paradox that developing countries, which make comparatively bigger efforts in this domain, lag also in terms of knowledge base. The question is therefore whether research has missed the point and failed to build a comprehensive, coherent and useful knowledge base. Second, the chapter considers both what elements are missing and how the important research challenges required to assemble them could be met.

Present and future commercial applications of biohydrometallurgy

V&V and UQ are the primary means to assess the accuracy and reliability of M&S and, hence, to establish confidence in M&S. Though other industries are establishing standards and requirements for the performance of V&V and UQ, at present, the nuclear industry has not established such standards or requirements. However, the nuclear industry is beginning to recognize that such standards are needed and that the resources needed to support V&V and UQ will be very significant. In fact, no single organization has sufficient resources or expertise required to organize, conduct and maintain a comprehensive V&V and UQ program. What is needed is a systematic and standardized approach to establish and provide V&V and UQ resources at a national or even international level, with a consortium of partners from government, academia and industry. Specifically, what is needed is a structured and cost-effective knowledge base that collects, evaluates and stores verification and validation data, and shows how it can be used to perform V&V and UQ, leveraging collaboration and sharing of resources to support existing engineering and licensing procedures as well as science-based V&V and UQ processes. The Nuclear Energy Knowledge base for Advanced Modeling and Simulation (NE-KAMS) is being developed at the Idaho National Laboratory in conjunction with Bettis Laboratory, Sandia National Laboratories, Argonne National Laboratory, Utah State University and others with the objective of establishing a comprehensive and web-accessible knowledge base to provide V&V and UQ resources for M&S for nuclear reactor design, analysis and licensing. The knowledge base will serve as an important resource for technical exchange and collaboration that will enable credible and reliable computational models and simulations for application to nuclear power. NE-KAMS will serve as a valuable resource for the nuclear industry, academia, the national laboratories, the U.S. Nuclear Regulatory Commission (NRC) and the public and will help ensure the safe, economical and reliable operation of existing and future nuclear reactors.

This article focuses on the need for destinations to create a knowledge-based system to assist with crisis and disaster management. After laying out the unique needs of the tourism industry for knowledge when disaster strikes, the article describes a knowledge framework designed to assist in such situations. This framework suggests three separate knowledge bases to deal with the three stages of a disaster. Knowledge Base 1, the precrisis stage, deals with knowledge retrieval and storage; Knowledge Base 2, the stage during the crisis, deals with knowledge processing; and Knowledge Base 3, the postcrisis stage, deals with knowledge dissemination. The contents of each knowledge base are identified in detail and a recommendation for public sector leadership in implementation of such a system is discussed.

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.

This dissertation, an historical case study, investigates how the Bell Telephone System blended knowledge of depreciation and probability theory both to develop a managerial accounting policy and to serve as a means for controlling debate before government regulators in the early twentieth century. This research shows that by combining statistics and accounting, the Bell System created a system for estimating capital cost expiration that was firmly grounded in mathematical science. The firm developed methodologies that used averaging techniques as a way to determine trends in asset life that were obscured by random fluctuations in actual retirements. The resultant smoothing of annual expenses also helped reduce the perception of risk, thus providing an economic benefit to the firm. At the same time, complexity derived from the application of probability theory gave the telephone company significant advantages in regulatory debates with adversaries trained primarily in accounting and law. In addition, the advanced mathematics also functioned as a knowledge barrier which inhibited potential encroachments by regulators on corporate prerogatives. This study extends the body of literature on managerial accounting by documenting an early use of statistical tools to understand firm resources. This study also expands our understanding of the acquisition and use of knowledge within the firm. Some of the most interesting findings look at the contextual nature of knowledge. Thus, in the pro-business atmosphere of the 1920’s, the firm was left alone to develop its knowledge base and design applications that were in its own best interests. With the crisis of the Depression, the relationship between the firm and business changed, and regulators demanded examination of the equity issues related to AT&T’s depreciation practices. This resulted in changes to the rate base calculations, but the basic depreciation policies of the Bell System remained unchanged.%%%%

A comprehensive and machine-understandable cancer drug-side effect (drug-SE) relationship knowledge base is important for in silico cancer drug target discovery, drug repurposing, and toxicity predication, and for personalized risk-benefit decisions by cancer patients. While US Food and Drug Administration (FDA) drug labels capture well-known cancer drug SE information, much cancer drug SE knowledge remains buried the published biomedical literature. We present a relationship extraction approach to extract cancer drug-SE pairs from the literature. We used 21,354,075 MEDLINE records as the text corpus. We extracted drug-SE co-occurrence pairs using a cancer drug lexicon and a clean SE lexicon that we created. We then developed two filtering approaches to remove drug-disease treatment pairs and subsequently a ranking scheme to further prioritize filtered pairs. Finally, we analyzed relationships among SEs, gene targets, and indications. We extracted 56,602 cancer drug-SE pairs. The filtering algorithms improved the precision of extracted pairs from 0.252 at baseline to 0.426, representing a 69% improvement in precision with no decrease in recall. The ranking algorithm further prioritized filtered pairs and achieved a precision of 0.778 for top-ranked pairs. We showed that cancer drugs that share SEs tend to have overlapping gene targets and overlapping indications. The relationship extraction approach is effective in extracting many cancer drug-SE pairs from the literature. This unique knowledge base, when combined with existing cancer drug SE knowledge, can facilitate drug target discovery, drug repurposing, and toxicity prediction.

Services and prevention: pharmacoepidemiology of antidepressant use

Nursing excellence is usually defined in terms of having and applying more and more knowledge, especially from nursing science--the more nurses know, the better their practice. This conceptualization of nursing practice has similarities with the ancient Greek mode of reasoning called techne but cannot adequately deal with the ambiguities of everyday nursing. Nursing excellence does occur, however, with phronetic, ontological practice in which a nurse's morals, habits, and dispositions guide practice. Of course, nurses need a comprehensive knowledge and skill base, but phronetic nurses negotiate the "rough ground" of nursing practice because ontological dispositions are guiding practice rather than simply applying generalizable and communal knowledge from nursing science. Techne-ical practice leads to competent nursing, but only phronetic practice results in nursing excellence. Included in this article is a description of techne-ical practice, its limitations for nursing excellence, and rationale for adopting a phronetic conceptualization of nursing practice.

The transition to "intellectual" agriculture is the main vector of modernization of the agricultural sector of the economy. It is based on integrated automation and robotization of production, the use of automated decision-making systems. This is inevitably accompanied by a significant increase in data flow from sensors, monitoring systems, meteorological stations, drones, satellites and other external systems. Farm management has the opportunity to use various online applications for accurate recommendations and making various kinds of management decisions. In this regard, the most effective use of cloud information technologies, allowing implementing the most complex information and technical level of automation systems for management of agricultural technologies. The purpose of this work is to test the approach to creating expert management decision support systems (DSS) through the knowledge base (KB), formed in the cloud information system. For this, we consider an example of constructing a DSS for choosing the optimal date for preparing forage from perennial grasses. A complete theoretical and algorithmic database of the analytical DSS implemented in the data processing center of the cloud information system is given. On its basis, a KB is formed for a variety of different decision-making conditions. This knowledge base is transmitted to the local DSS. To make decisions about the optimal dates for the preparation of the local DSS, two variants of algorithms are used. The first option is based on management models, and the second uses the pattern recognition method. The approbation of the algorithms was carried out according to the BZ from 50 cases. According to the results of testing, the method of pattern recognition proved to be more accurate, which provides a more flexible adjustment of the situation on the local DSS to a similar situation in the KB. The considered technique can be extended to other crops.

Epidemic outbreaks on sea-going vessels pose a significant health and safety risk, particularly in isolated maritime environments lacking professional medical staff. The COVID-19 pandemic exposed major deficiencies in the preparedness and response protocols of maritime operations, where vessels became de facto quarantine units without access to structured medical decision support. This study presents the development and evaluation of DESSEV, a decision support system designed to assist maritime personnel in identifying and managing infectious disease outbreaks onboard vessels. A comprehensive non-SQL knowledge base was constructed using international epidemiological guidelines (WHO, CDC, ECDC) and medical literature, encompassing 22 infectious diseases and 35 symptoms categorized into 8 clinical domains. To support probabilistic diagnostics, artificial patient profiles were generated reflecting real-world symptom distributions. Three predictive models—Decision Tree, Naive Bayes, and Random Forest were trained. Their performances were assessed through cross-validation and random sampling techniques. Evaluation on a holdout test set of real patient cases showed that the Random Forest model achieved superior performance across all The Random Forest algorithm was thus selected as the core prediction engine for the DESSEV application. While not a substitute for professional medical care, DESSEV improves situational awareness and supports early risk mitigation actions aboard vessels. Future work will focus on integrating real-time health telemetry and user feedback to further refine diagnostic accuracy and usability.

In September, 1996, Brown University School of Medicine inaugurated a new competency-based curriculum, known as MD2000, which defines a comprehensive set of competency requirements that all graduates are expected to attain. The medical students entering in 1996 and thereafter are required to demonstrate mastery in nine abilities as well as a comprehensive knowledge base as a requirement for graduation. Faculty use performance-based methods to determine if students have attained competence. We describe in this article the reasons why we developed the new curriculum, how we planned and structured it, and the significance we anticipate the curricular innovation will have on medical education.

Product-related information can be found in various data sources and formats across the product lifecycle. Effectively exploiting this information requires the federation of these sources, the extraction of implicit information, and the efficient access to this comprehensive knowledge base. Existing solutions for product information management (PIM) are usually restricted to structured information, but most of the business-critical information resides in unstructured documents. We present a generic architecture for federating heterogeneous information from various sources, including the Internet of Things, and argue how this process benefits from using semantic representations. A reference implementation tailor-made to business users is explained and evaluated. We also discuss several issues we experienced that we believe to be valuable for researchers and implementers of semantic information systems, as well as the information retrieval community.

The foregoing analysis has delved into the reasoning behind conceptual change in the evolving jurist understanding of the purposes of law in China's transition to a 'socialist market'. The analysis and interpretation of new legal thinking is presumed to be useful to the informed understanding of Chinese progress towards rule-of-law making. It accepts the study of the conceptual basis for contemporary legal reform as a legitimate area of academic inquiry; and it disputes impatient assumption that because not all of the new substance of law has been consistently operationalized in practice, the issues of contemporary jurisprudence and related human rights legislation are dubious. On the contrary, a correct and comprehensive knowledge base is necessary to a critically balanced external understanding as to how best to support the positive development of human rights and the rule of law in the complicated circumstances of China's current marketplace.