Knowledge Storage Research Articles

A Study on Automated Problem Troubleshooting in Cloud Environments with Rule Induction and Verification

In a vast majority of cases, remediation of IT issues encoded into domain-specific or user-defined alerts occurring in cloud environments and customer ecosystems suffers from accurate recommendations, which could be supplied in a timely manner for recovery of performance degradations. This is hard to realize by furnishing those abnormality definitions with appropriate expert knowledge, which varies from one environment to another. At the same time, in many support cases, the reported problems under Global Support Services (GSS) or Site Reliability Engineering (SRE) treatment ultimately go down to the product teams, making them waste costly development hours on investigating self-monitoring metrics of our solutions. Therefore, the lack of a systematic approach to adopting AI Ops significantly impacts the mean-time-to-resolution (MTTR) rates of problems/alerts. This would imply building, maintaining, and continuously improving/annotating a data store of insights on which ML models are trained and generalized across the whole customer base and corporate cloud services. Our ongoing study aligns with this vision and validates an approach that learns the alert resolution patterns in such a global setting and explains them using interpretable AI methodologies. The knowledge store of causative rules is then applied to predicting potential sources of the application degradation reflected in an active alert instance. In this communication, we share our experiences with a prototype solution and up-to-date analysis demonstrating how root conditions are discovered accurately for a specific type of problem. It is validated against the historical data of resolutions performed by heavy manual development efforts. We also offer experts a Dempster–Shafer theory-based rule verification framework as a what-if analysis tool to test their hypotheses about the underlying environment.

Efficiency Improvisation of Large-Scale Knowledge Systems in Feature Determination using Proposed HVGAN Architecture

Knowledge Management (KM) is crucial for efficient information retrieval and forms the backbone of implementing an Information System (IS). Efficiently extracting meaningful trends from headlines aids in visualising demographic situations and addressing emergency handling needs. Understanding Information Resource Management (IRM) and Knowledge Resource Management (KRM) concepts benefits end users. This proposed KRM concept organises knowledge, ensuring global resource control through a neural decision framework. Traditional methods like Structural Equation Modelling (SEM) have higher computational steps, whereas neural network strategies in KM reduce steps and improve data prediction accuracy. The paucity of input data is a common challenge, which can be overcome by using Generative Adversarial Network (GAN) architecture for probabilistic generative processes. This research evaluates acquisition, spreading, vulnerability, and application using GAN-based knowledge systems that mitigate data scarcity through generative methods. The proposed Hierarchical Vanilla Generative Adversarial Network (HVGAN) utilises gradient functions and hierarchical computation algorithms to achieve desired knowledge extraction with lower time complexity and higher accuracy. Implementation improves efficiency in accessing stored knowledge, yielding an AUC score of 96% compared to other GAN architectures.

Knowledge Repositories for Managing Knowledge in Learning Organizations

Learning organizations create a phenomenal amount of scholarly knowledge as part of their academic discourse and research activities. This scholarly knowledge must be preserved and made accessible to other members. The absence of tools and strategies to facilitate the storage and accessibility of knowledge and information resources poses lots of challenges for the growth of learning organizations, particularly, learning organizations in Ghana. This study, therefore, sought to assess the deployment of knowledge repositories in learning organizations toward effective knowledge creation, use, sharing, retention, and retrieval. The study employed the triangulation design and online survey research. The triangulation helped in the collection of quantitative data followed by qualitative instruments (interviews) to find answers to pertinent questions and issues that were insufficiently addressed in the questionnaire responses. Nine (9) learning organizations in Ghana participated in this study. The study established that knowledge repositories and technology played critical roles in managing knowledge in learning organizations. However, the knowledge repositories were not user-friendly and fully utilized or accessible for knowledge management practices at the learning organizations. Also, the absence of fully integrated ICT Tools and Infrastructure inhibited the effective promotion of knowledge management initiatives at the learning organizations. The study concludes by developing a knowledge repository architecture for knowledge management in learning organizations.

The Biological Basis of the Symbolic: Exploring the Implications of the Co‐Evolution of Language, Cognition and Sociality for Management Studies

AbstractIn this essay, we approach the question of what it means for something to be symbolic in a different way from the usual answers rooted in philosophy, sociology or anthropology: we argue that the symbolic is, first and foremost, rooted in human biology and human evolution. We discuss how the development of the capability to create and share symbols was a key moment in human evolution that underpins our capability to communicate and store knowledge through language, to think abstractly about problems, and to live and work together effectively in large groups. It also underpins the unique ecological niche – the cognitive niche – that Homo sapiens construct using our capability to create and share symbols. We go on to explore some of the implications of an evolutionary understanding of the symbolic for management and organization research.

Mathematical modeling of food production technological processes in a computer training complex for operator education

A computer training complex (CTC) structure has been designed for training food production operators, comprising interconnected subsystems generating output parameters. This CTC structure enables the configuration and storage of knowledge on specific technological processes, facilitating the customization of exercises by instructors. Individualized exercises are generated for students to perform in a virtual production environment, utilizing computer programs, tablets, smartphones, or simulated production equipment, including VR/AR technologies. During exercises, student actions are automatically recorded, and a quantitative assessment is provided based on standards, forming a comprehensive evaluation. A mathematical model, represented by a Markov transition graph, captures various states of the virtual production environment during simulated processes at the CTC. This model serves as the basis for constructing specific technological process models in the food industry, tailored to CTC hardware, software, and training methodologies. The methodology presented streamlines the development of training courses for CTC exercises, enhancing personnel knowledge and skills in food production. By utilizing these models, methods, and algorithms, CTCs can be tailored for training operators in diverse food production sectors. The approach outlined simplifies course creation, ensuring personnel development at the desired proficiency level through exercise quality assessment algorithms.

Knowledge Graph-based Diversity Analysis of Supplier Holographic Portraits

Abstract Fully understand the development of suppliers in order to make better supplier selection. This paper is based on the knowledge graph, through the knowledge updating of the knowledge graph combined with the Transformer model for knowledge extraction of supplier entity relationship, forming the ternary semantic information of supplier entity relationship. Then, based on the big data platform for the construction of supplier holographic portrait and knowledge storage, through information integration, analysis and other links to identify the supplier attributes for label definition. Taking cell phone product suppliers as an example, we use Python technology to obtain relevant data and validate the specific role of supplier holographic portrait in terms of the supplier’s comprehensive strength, behavioral prediction, transaction closeness, and comprehensive evaluation. The results show that: the correlation between the comprehensive strength of suppliers and the amount of winning bids is strong, and its R2 test result is 0.5924, and it can realize the behavioral prediction of suppliers in the supply chain. Supplier H offers a range of cell phone products in 2022, which is 17.62%<unk>21.17% higher than the benchmark market price. The holographic portrait of suppliers based on a knowledge graph combined with a big data platform can meet the need to carry out an all-around analysis of suppliers and provide more accurate support for diversified decision-making on the demand side.

Role of Knowledge Management Processes on achieving Strategic Superiority - An Exploratory study of the opinions of a sample of National Security Service Officers

Purpose of current research is to test effect of knowledge management processes in their dimensions (knowledge diagnosis, knowledge acquisition, knowledge generation, knowledge storage, knowledge distribution, knowledge application) in improving strategic superiority in their dimensions (area of influence, psychological formation, competitive pressure) in national security apparatus. Due to importance of two variables investigated for agency from its future perspective and interest of research community and its sample in various administrative phenomena aimed at improving its strategic superiority from a sovereign orientation as a goal that should be viewed from multi-faceted perspectives, and on this basis, a research problem was embodied in its main question (Was the National Security Service able to invest in management operations? Knowledge in improving its strategic superiority?), so that this question would be the starting point for a research goal embodied in choosing exploratory descriptive analytical method as it is the most appropriate in this research direction, so that a questionnaire for research could be prepared from solid scientific standards adopted by previous studies and some expert professors and arbitrators made appropriate modifications to it, after knowing the community. and research included (650) of its leaders, represented by (General Director, Assistant General Director, Directorate Director, Department Director, Division Director), as the research sample was a stratified random (236) from the leadership community, to be collected and analyzed through the statistical program (AMOS V.25, SPSS. V.28) and through appropriate statistical methods (normal distribution tests, testing the scale’s validity, stability and confirmation after exploring it, mean, standard deviation, relative importance, coefficient of relative variation, Pearson correlation coefficient, multiple regression), so that practical analysis can be achieved through the data obtained. researcher drew from the sample answers to most prominent result embodied in National Security Agency’s focus on improving its strategic superiority well, which prompted it to adopt knowledge management processes to advance that superiority, especially since it adopted knowledge diagnosis, knowledge distribution, and knowledge application mainly for that purpose, in addition to His adoption of strategies aimed at improving the national security apparatus from the level of the sphere of influence by adopting the distribution of knowledge, the diagnosis of knowledge, the application of knowledge, and the acquisition of knowledge, which led him to adopt the diagnosis of knowledge and the application of knowledge in improving psychological formation, in addition to his resort to storing knowledge, distributing knowledge, and applying knowledge when it improves the level of competitive pressure.

Research on Digital Book Resource Recommendation Algorithm Based on Knowledge Graph

Knowledge graph based search is becoming increasingly close to users' reading habits, providing a more user-friendly interactive experience. Integrating knowledge graphs as auxiliary information into recommendation algorithms can model and integrate semantic information of learning resources, providing more diverse personalized recommendation results and better alleviating the problem of information overload. In order to solve the problem of readers searching for suitable digital book resources and improve reading efficiency, the first step is to study the construction of knowledge graphs, including core tasks such as knowledge extraction, knowledge representation, and knowledge storage; Then, a mathematical model is constructed, based on the optimized graph convolutional network layer and the quantification algorithm of knowledge point centrality, to construct a universal computational framework for attention mechanism; Finally, simulation experiments were conducted to compare and analyze different models through evaluation indicators, in order to verify the advantages of the model constructed in this paper. The experimental results show that the research method proposed in this paper is superior to conventional methods such as FM, CKE, DNN, NFM, CFKG, KGAT, and KGCN.

A New Neural Dynamic Learning Framework for Discrete-Time Strict-Feedback Systems: Internal Interaction-Based Weight Adaptive Laws.

This article investigates internal interaction-based dynamic learning control (LC) for uncertain discrete-time strict-feedback systems. On the basis of predict technology, the original system is converted into a common n -step-ahead input-output predict model. The predict model causes every estimated neural weight to converge to n different constants using the existing control framework. To solve such a problem, the predict model is further decomposed into n one-step-ahead subsystems, which can be viewed as n independent agents. Subsequently, the distributed cooperative weight adaptive laws are designed by introducing an undirected and connected interconnection topology among subsystems. By constructing the variable relationship between the subsystems and the n -step-ahead predict model, a new internal weight interaction-based neural dynamic LC framework is proposed for the whole closed-loop system, in which estimated weights at different times share their weight knowledge. The proposed framework ensures the ultimately uniform boundedness of the closed-loop system and achieves the excellent control performance. By combining the consensus theory and a cooperative persistent excitation condition, every estimated weight along the neural input orbit is verified to exponentially converge to a close vicinity of a unique ideal constant, rather than n different constants. Consequently, the developed LC framework facilitates constant weights storage, saves the knowledge storage space, and improves the robustness of knowledge utilization. These characteristics are verified by simulation results.

Knowledge Management Perspective of Generative Artificial Intelligence

In this editorial, revisiting Alavi and Leidner (2001) as a conceptual lens, we consider the organizational implications of Generative Artificial Intelligence (GenAI) from a knowledge management (KM) perspective. We examine how GenAI impact the processes of knowledge creation, storage, transfer, and application, highlighting both the opportunities and challenges this technology presents. In knowledge creation, GenAI enhances information? processing and cognitive functions, fostering individual and organizational learning. However, it also introduces risks like AI bias and reduced human socialization, potentially marginalizing junior knowledge workers. For knowledge storage and retrieval, GenAI’s ability to quickly access vast knowledge bases significantly changes employee interactions with KM systems. This raises questions about balancing human-derived tacit knowledge with AI-generated explicit knowledge. The paper also explores GenAI’s role in knowledge transfer, particularly in training and cultivating a learning culture. Challenges include an over-reliance on AI and risks in disseminating sensitive information. In terms of knowledge application, GenAI is seen as a tool to boost productivity and innovation, but issues like knowledge misapplication, intellectual property, and ethical considerations are critical. Conclusively, the paper argues for a balanced approach to integrating GenAI into KM processes. It advocates for harmonizing GenAI’s capabilities with human insights to effectively manage knowledge in contemporary organizations, ensuring both technological advances and ethical responsibility.

Принципы динамики детской языковой картины мира

Introduction. In line with the anthropocentric approach, the desire to create a model of an individual linguistic worldview becomes possible thanks to concepts as basic structures for accumulating knowledge about the world. Such a result can be achieved by studying the children's language picture of the world, which will explain the structure of the language picture of the adult world, and not vice versa, as it seemed before. Materials and Methods. The research is based on the analysis of texts of a junior schoolboy using methods of observation, comparison, generalization of analysis and synthesis, interpretation of the data obtained empirically. Analysis. The works of Russian researchers describe fragments of the linguistic picture of the world of people of different professions who speak different languages. This article presents the author's point of view on the model of a fragment of the children's language picture of the world of a junior schoolboy. Predicates denoting mental activity as one of the numerous and leading groups of verbs in speech at this age are taken as a nuclear sphere of analysis. The principles of the dynamics of the image of the world, reflected in the language of the child, are revealed. The ontological principle is that the assimilation of the native language occurs through the assimilation of various models of structural schemes of sentences and their variants. The cognitive principle – proposition is a special stacked structure of knowledge storage, which is implemented and fixed by the structural scheme of the sentence. The communicative principle – speech acts expressed by the structural scheme of the sentence, implement the intentions of a native speaker. Thus, the structural schemes of sentences are the signifiers of syntactic concepts, and intentions and propositions are their signified. In a word, the syntactically represented concept controls the content of the sentence's structural scheme and the type of speech act, highlighting its individual cognitive features. Results. We have studied the connections of the cognitive basis of the concept with semantics and pragmatics, cause-and-effect relationships of actualization of ideas about mental activity by a younger student. Thus, the principles of the dynamics of the linguistic picture of the world form an initial position of objective reality conceptualization.

La carte conceptuelle : un soutien au transfert des apprentissages métalinguistiques

The Concept Map: A Support for Learning Transfer
 Based on the concept mapping technique, our research endeavors to experimentally address the challenges encountered by candidates taking the Baccalaureate exam, specifically related to their ability to employ interdisciplinary knowledge while inferentially processing texts in French as a foreign language (FLE). The study intersects two disciplines: French as a Foreign Language (FLE) and History courses in the first language (L1). Employing a three-phase action research approach—commencing with a pre-test involving a text recall task, followed by collaborative concept map design using the PREP technique (Langer, 1982-1984) as a metacognitive experience, and concluding with a post-test to validate our approach—we aim to surpass the limited capacity of working memory and facilitate in-depth processing of textual information. By incorporating the concept map into our experimental approach, we aim to simulate "reflection on cognition," aligning with the training in internal monitoring—a central goal in any educational institution. Our initiative addresses linguistic challenges arising from the mental transition between the first language (L1) and the second language (L2), with a particular focus on challenges associated with mental translation. Situated within a plurilingual context involving Classical Arabic (L1) and the second language, French (FLF), we seek to visualize the linguistic interactions occurring during information processing through schematic organization and hierarchization. Resulted revealed that the use of concept maps substantially supports strategies related to analogical reasoning and high-level text processing. This approach significantly enhances learners' lexical repertoire by shaping organizational strategies and facilitating the storage of conceptual knowledge, streamlining retrieval processes. While concept maps undeniably bolster language skills, our research goes beyond this acknowledgement. It not only highlights the positive impact of integrating concept maps for language skill improvement but also emphasizes the untapped potential for even greater significance and promise. The instructional intervention has shed light on specific elements educators should consider to optimize outcomes. As a tool for facilitating transfer learning, the concept map has proven significant, albeit not without some negative transfers. Adjusting teaching strategies based on individual student needs and characteristics is essential to harness the full potential of this approach.

ENHANCED PEST INFORMATICS SYSTEM

Pest Management System (PMS) is a computer application in Integrated Pest Management. The term encompasses computer-based storage, retrieval, sharing, and optimal use of pest management data, information, and knowledge for problem solving and decision-making. The pest control industry is continuously experiencing novel technologies and products that will improve the way to manage and prevent pests. The development of an intensive electronic crop pest informatics is highly essential to help bridge these gaps. The aim of this research is to design and develop a voice enhanced Pest Informatics System(PIS). Pest Informatics System was designed and implemented using VModel an extension of the Waterfall model, which offers a means of making the development process more feasible. The tools used for the development of the voice enhanced web-based pest management system are PHP/Laravel, Java Script, HTML and CSS. Result shows that PIS consists of over one hundred pests with their scientific names, descriptions and control measures. It is voice enhanced, therefore can assist visually impaired individuals and aid decision support on farm through listening and not necessarily reading. It is web based which makes it easily accessible, compact, educative and enhance decision making. Conclusively, The need to deliver a first hand information to farmers as at when due is critical to good yield. Access to compact information about pests is of great significance to farmers. Enhanced pest informatics system is a smart tool that creates mobile extension services to farmers using a stress free and friendly approach. User’s evaluation result confirmed that the tool is flexible, user friendly, reliable, efficient and robust.

Green knowledge management to achieve corporate sustainable development

The mining industry significantly impacts the three pillars of sustainable development: the economy, the environment, and society. Therefore, it is essential to incorporate sustainability principles into operational practices. Organizations can accomplish this through knowledge management activities and diverse knowledge resources. A study of 300 employees from two of the largest mining corporations in South Kalimantan, Indonesia, found that four out of five elements of knowledge management—green knowledge acquisition, green knowledge storage, green knowledge application, and green knowledge creation—have a direct impact on the sustainability of businesses. The calculation was determined using Structural Equation Modelling (SEM). However, the study also found that the influence of collectivist cultural norms inhibits the direct effect of green knowledge sharing on corporate sustainable development. The finding suggests that companies operating in collectivist cultures may need to take additional measures to encourage knowledge sharing, such as rewarding employees for sharing their expertise on green initiatives, supportive organizational culture, clear expectations, and opportunities for social interaction.

About a Range of Metaphorical Meanings in the Representation
 of the Phenomenon of «Language» in the Book «Herausmit der Sprache» by A. Thalmayr

The article examines the metaphorical models used by the famous German writer, poet, translator Andreas Thalmayr when describing the German language in his book «Heraus mit der Sprache. Ein bisschen Deutsch für Deutsche, Österreicher, Schweizer und andere Aus- und Inländer». Attention to language as an object of research has increased within the framework of cognitive linguistics, which considers natural human language that reflects all the diversity of the world, on the one hand, as an optimal means of storage, creative interpretation and transmission of human knowledge about the surrounding reality. On the other hand, language is an object of scientific linguistic study. The optimal tool for exploring this most complex phenomenon of human culture is a cognitive metaphor,
 which allows us to comprehend this abstract concept in a more concrete and structured way. In the process of analysis, we identified the following most frequent metaphorical models: language is a person, language is an object of nature,
 language is an object of human activity, language is a treasury, language is a system, language is chaos. The metaphorical representation of language turns out to be associated with various semantic images, which indicates the complexity,
 inconsistency and dynamism of the object of research, the versatility of which can be accounted for by the complexity of its carrier – the man.

Effectiveness of GBL in the Engagement, Motivation, and Satisfaction of 6th Grade Pupils: A Kahoot! Approach

In the modern day, educational demands necessitate student-centered instructional approaches at all levels of educational institutions. Through the integration of digital technology, these frameworks incorporate up-to-date teaching methods, encourage social engagement, and promote cultural values. Gamification in digital technologies assists in establishing an educational environment that is engaging, entertaining, personalized, and constantly available. This educational environment is based on digital learning resources and draws on stored human knowledge, articulating education by removing geographical and temporal boundaries. Kahoot! is a platform that allows the creation of and access to gamified assessment quizzes while integrating technology into the teaching and learning process. In this article, we present the results of a study based on a Kahoot! quiz used in a classroom with 27 6th-grade pupils in a Greek elementary school. Our objective is to determine the effectiveness of game-based learning by using Kahoot! as a supplementary element to traditional teaching methods. With the inclusion of a post-activity questionnaire for overall interest and motivation to learn while using Kahoot! and the Driscoll Questionnaire, we aim to explore the potential benefits of Kahoot! in the learning process, pupil engagement, and assessment. The analysis of the collected data from the questionnaire demonstrated a positive attitude toward Kahoot! as an alternative educational method and learning approach. It also revealed positive feedback on the motivation, enjoyment, sense of autonomy, creativity, and pupil interest in the learning process. In addition, the study indicated a moderate positive effect on pupils’ understanding of the learning content and a slight inclination towards the technology-based, non-traditional approach compared to traditional teaching. Based on the findings, by fostering an encouraging and creative environment, we can improve pupil engagement and overall motivation while promoting autonomous learning through a game-based experience.

توجهات ادارة المعرفة لدى مدرسي المرحلة الاعدادية

This research aims to : The current research aims to identify the level of knowledge management of middle school teachers as well as the statistically significant differences for the variable of knowledge management orientations according to the gender variable (male, female) and specialization (scientific, human). To achieve the research objectives, the researcher proceeded to: 1- Using the descriptive associative approach to reveal the importance of e-learning and knowledge management 2- -Adopting the knowledge management scale prepared by (Al-Abdullah, Al-Gharawi, 2020). The scale may consist of four areas (knowledge generation, knowledge storage, knowledge distribution, knowledge application). The scale included (40) items distributed over four journals. To complement that, the researcher applied the two scales to a sample of (400) teachers and schools in middle schools (boys and girls schools) affiliated to the Center of the General Directorate of Al-Qadisiyah Education, which were selected by the stratified random method according to the proportional method. Search for the following results: 1-Teachers in the preparatory stage have a knowledge management orientation. 2-There are statistically significant differences in knowledge management orientations among middle school teachers according to gender (males, females) in what there are no statistically significant differences in knowledge management orientations according to specialization (scientific, human), as well as the presence of statistically significant differences in knowledge management orientations. To the interaction of sex with the specialty According to the results of the current research, the researcher came up with a number of recommendations and suggestions, including: 1-Reconsidering educational policies related to the mechanisms of applying knowledge management 2-Building a clear strategic plan for the application of knowledge and the mechanisms for acquiring it 3-Holding training courses for middle school teachers on how to apply knowledge management and benefit from it. 4-Conducting studies to reveal the readiness of students to learn through the technologies of the e-learning system

A Knowledge Guided Transfer Strategy for Evolutionary Dynamic Multiobjective Optimization

The key task in dynamic multiobjective optimization problems (DMOPs) is to find Pareto-optima closer to the true one as soon as possible once a new environment occurs. Previous dynamic multiobjective evolutionary algorithms (DMOEAs) normally focus on DMOPs with regular environmental changes, but neglect widespread random one, limiting their applications in real-world fields. To address this issue, a knowledge guided transfer strategy based DMOEA is proposed in the paper. First, knowledge described as a two-tuple is extracted under each historical environment and preserved to a knowledge pool. Redundant knowledge is recognized and adaptively removed so as to guarantee the diversity of the pool. Second, a knowledge matching strategy is developed to re-evaluate the representative of each stored knowledge under a new environment, with the purpose of finding the most valuable one to promote positive knowledge transfer. Third, an improved knowledge transfer mechanism based on subspace alignment is introduced. By integrating it with knowledge reuse mechanism, a hybrid transfer strategy is constructed to adaptively select the most suitable one in terms of the similarity degree of selected knowledge on the current environment, and then generate a new initial population. Experiments on 20 benchmark problems demonstrate that the knowledge guided transfer strategy outperforms five state-of-the-art algorithms, achieving good versatility in solving DMOPs with both regular and random changes.

An investigation into knowledge management adoption and sustainable competitive advantage in an emerging market economy

Purpose Organizations sustain competitiveness by improving product or service quality, performing efficiently or innovating. This paper aims to investigate the relationship between knowledge management (KM) and sustainable competitive advantage (SCA) in business organizations in the Kingdom of Bahrain. The KM initiatives are categorized into knowledge creation, knowledge storage, knowledge transfer and knowledge application. Employees’ attitudes toward workplace knowledge resources are derived from their perceptions of their importance, usefulness and ease of use. Design/methodology/approach This paper adopts a cross-sectional survey design. Data is collected via an electronic questionnaire developed using Google Forms. Purposive sampling used a list of 122 business organizations. Ninety responses were received and taken into consideration for data analysis. Findings Spearman correlation analysis and partial least square structural equation modeling revealed a positive association between KM and SCA. This study reflected a positive association between employees’ attitudes toward knowledge resources and sustaining organizations’ competitive advantages. Originality/value In Bahrain, empirical studies still need to be developed to explore KM in business organizations and investigate its association with SCA. This study aims to fill this gap by examining the relationship between KM and the sustainability of quality, efficiency and innovation-based competitive advantages in business organizations in an emerging economy context.

Retrieval Contrastive Learning for Aspect-Level Sentiment Classification

Aspect-Level Sentiment Classification (ALSC) aims to assign specific sentiments to a sentence toward different aspects, which is one of the crucial challenges in the field of Natural Language Processing (NLP). Despite numerous approaches being proposed and obtaining prominent results, the majority of them focus on leveraging the relationships between the aspect and opinion words in a single instance while ignoring correlations with other instances, which will make models inevitably become trapped in local optima due to the absence of a global viewpoint. Instance representation derived from a single instance, on the one hand, the contained information is insufficient due to the lack of descriptions from other perspectives; on the other hand, its stored knowledge is redundant since the inability to filter extraneous content. To obtain a polished instance representation, we developed a Retrieval Contrastive Learning (RCL) framework to subtly extract intrinsic knowledge across instances. RCL consists of two modules: (a) obtaining retrieval instances by sparse retriever and dense retriever, and (b) extracting and learning the knowledge of the retrieval instances by using Contrastive Learning (CL). To demonstrate the superiority of RCL, five ALSC models are employed to conduct comprehensive experiments on three widely-known benchmarks. Compared with the baselines, ALSC models achieve substantial improvements when trained with RCL. Especially, ABSA-DeBERTa with RCL obtains new state-of-the-art results, which outperform the advanced methods by 0.92%, 0.23%, and 0.47% in terms of Macro F1 gains on Laptops, Restaurants, and Twitter, respectively.