Knowledge Acquisition Framework Research Articles

A Rule-Based Expert System for Academic Advising and Guidance Toward Undergraduate Students

The provision of academic guidance to undergraduate students is a crucial and significant duty for the academic faculty in most prestigious universities. Expert systems are often regarded as one of the most successful areas in the field of artificial intelligence. The system utilizes a rule-based decision engine to assist individuals lacking experience in enhancing their skills. Nevertheless, a significant number of students are required to meet with their advisors to arrange their study schedule. This thesis is driven by the belief that the successful development of an academic advisory expert system will result in an expansion of the range and extent of challenges that students, academic staff, and other academic activities can effectively address, leading to a higher level of achievement in the university learning process. Nevertheless, the effectiveness of an advisory expert system is constrained by the caliber of the acquired knowledge, specifically in the context of academic advisory expert systems. The performance of such systems is primarily determined by the quality of the framework employed for academic expert knowledge acquisition. The objective of this study is to suggest a revision of the current knowledge acquisition framework in order to make it appropriate for implementation in higher education institutions. The proposed modification aims to create a prototype rule-based expert system specifically designed for academic advising of undergraduate students. The system's output also offers a precise and conflict-free course proposal for the undergraduate student. It functions as a mentor, providing undergraduate students with guidance and recommendations for suitable subjects based on their completed courses, prerequisites, and project scope, as defined by the student themselves. According to the empirical findings, the utilization of the proposed model for an undergraduate advisory expert system resulted in a noteworthy enhancement in performance.

Trust but verify? A social epistemology framework of knowledge acquisition and verification practices for fictional entertainment

Abstract Fictional entertainment can serve as a vivid and easily comprehensible source of knowledge, but only if audiences are able to tell its kernel of truth apart from fantasy. In this article, we use the lens of social epistemology to develop a theoretical framework of knowledge acquisition and verification practices for fictional entertainment that integrates various extant lines of work on entertainment education, perceived realism, information processing, credibility assessment, and verification strategies. To flesh out the conceptual model derived from top-down theoretical integration, we use an inductive, bottom-up approach to theory building, assisted by qualitative research. The resulting model describes knowledge acquisition from fiction as an essentially social process characterized by a combination of epistemic trust and epistemic vigilance toward fictional content and sources, in which credibility is assessed via social knowledge sharing and verification practices.

A Sensitivity Analysis for Roles Selection in Hybrid Multi-Criteria Decision Making

The process of retaining leadership succession at higher education institutions (HEI) is crucial since it has entailed the process of selecting the ideal candidate. The goal is to guide universities toward maintaining organizations' excellence in their academic leadership and management (ALM) positions. Due to the lack of established standards for assessing the competency of possible successors at their home institutions, many ALM of Malaysia HEIs is difficult to identify the proper replacement for their posts. This study aims to propose a multi-criteria tacit knowledge acquisition framework (MC-TKAF) for supporting talent development intervention programs in Malaysia HEIs. It will be based on cognitive apprenticeship, socialization, and informal learning theory which is mostly used in acquiring knowledge from expertise to overcome talent bottlenecks among novices. Fuzzy Delphi will be used as the primary methodology in this study to gather agreement regarding the appropriate indicator to measure tacit knowledge competency among ALMs at Malaysian HEIs. There are three phases: Phase 1 involves analysing the current tacit knowledge acquisition (TKA) and identifying the appropriate parameters to build the intended framework. Phase 2 involves using the results of Phase 1 to create a new framework of tacit knowledge acquisition (TKAF) that is appropriate for the HEI environment. Phase 3's final objective is to assess the viability of the Talent Development Intervention Program's (TDIP) Tacit Knowledge Acquisition Framework (TKAF) utilizing the Multi-Criteria Decision Making (MCDM) approach. This paper's goal is to offer the hybrid MCDM approach as a talent performance indicator for the multi-criteria tacit acquisition framework. The final Phase 3 of the study design will essentially be the subject of this paper. The built-in indicators in this document may be utilized as a guide for the HEI sectors to create talent performance metrics that are appropriate for each TKA applied.

A novel failure mode and effect analysis method using a flexible knowledge acquisition framework based on picture fuzzy sets

Failure mode and effect analysis (FMEA) is an effective reliability management tool for identifying potential failures in a system/component that has been widely utilized in various fields by combining fuzzy sets theory. However, the difficulty in assessment expression and acquisition, imprecision in assessment aggregation, and missing relationships among risk factors are prominent challenges in the existing fuzzy FMEA methods. Thus, this work employs the picture fuzzy sets (PFSs) theory to meet these challenges, allowing experts to express assessments more efficiently and accurately. Meanwhile, we propose a novel FMEA method to improve the three essential processes of FMEA, involving the following steps. Firstly, a flexible knowledge acquisition framework (FKAF) is established to simplify the expert evaluation process, allowing experts to express fuzzy information with various forms of non-fuzzy assessments. Then, a strategy-based picture fuzzy conversion (PFC) method is developed to standardize the non-fuzzy values to picture fuzzy numbers (PFNs). Secondly, to improve the assessment aggregation accuracy in uncertain environments, we suggest the picture fuzzy evidential reasoning (PFER) method that extend the existing fuzzy evidential reasoning (FER) methods. Thirdly, to completely describe the parallel and causal relationships among risk factors, four alternative models are established using picture fuzzy Petri nets (PFPNs) and risk priority rankings are determined through inference. Finally, the effectiveness and superiority of the proposed FMEA method are verified through two case studies and one extended experiment, demonstrating that it overcomes the shortcomings of the existing FMEA methods and reduces application costs while ensuring the rationality of rankings.

A Formal Framework for Knowledge Acquisition: Going beyond Machine Learning.

Philosophers frequently define knowledge as justified, true belief. We built a mathematical framework that makes it possible to define learning (increasing number of true beliefs) and knowledge of an agent in precise ways, by phrasing belief in terms of epistemic probabilities, defined from Bayes’ rule. The degree of true belief is quantified by means of active information : a comparison between the degree of belief of the agent and a completely ignorant person. Learning has occurred when either the agent’s strength of belief in a true proposition has increased in comparison with the ignorant person (), or the strength of belief in a false proposition has decreased (). Knowledge additionally requires that learning occurs for the right reason, and in this context we introduce a framework of parallel worlds that correspond to parameters of a statistical model. This makes it possible to interpret learning as a hypothesis test for such a model, whereas knowledge acquisition additionally requires estimation of a true world parameter. Our framework of learning and knowledge acquisition is a hybrid between frequentism and Bayesianism. It can be generalized to a sequential setting, where information and data are updated over time. The theory is illustrated using examples of coin tossing, historical and future events, replication of studies, and causal inference. It can also be used to pinpoint shortcomings of machine learning, where typically learning rather than knowledge acquisition is in focus.

Digital Protection and Utilization of Architectural Heritage Using Knowledge Visualization

Architectural heritage is a lively carrier of historical development, as well as providing a valuable resource for human society. Recently, however, the longevity of architectural heritage has been influenced and even threatened by natural and human factors. Furthermore, the future remains uncertain. Therefore, this study aimed to develop a comprehensive method, namely, digital protection and utilization based on knowledge visualization (DPUKV), to protect and utilize architectural heritage. The most important problem to solve involves transforming digital protection data into a digital inheritance culture. We constructed a technical framework of knowledge acquisition, knowledge reconstruction, knowledge innovation, and knowledge management using qualitative and vertical methods to form hierarchical digital resources of architectural heritage. In particular, the knowledge reconstruction converted tacit knowledge of architectural heritage into explicit knowledge via visualization technology, providing research materials for more scholars. In knowledge innovation, architectural heritage culture was excavated and inherited through knowledge graphs, digital displays, and cultural creation with virtual reality, augmented reality, etc. Furthermore, this method was validated over four years via architectural heritage in China as case studies. In this paper, the buildings of Qinglian Temple in Shanxi, China, are discussed in detail as a case study. The results indicated that this method was effective for preserving architectural heritage and is suitable for other cultural heritage. It is also helpful to improve the public’s protection of and interest in cultural heritage, especially architectural heritage.

How knowledge acquisition creates a competitive edge? A qualitative inquiry from international consultancy alliance

PurposeThe current study attempts to bridge the existing gap related to the role of knowledge acquisition from international alliance partners to improve competitiveness by examining the distinct processes of knowledge acquisition and the challenges confronted in this learning process in order to enhance local and international market performance.Design/methodology/approachFollowing case-study approach based on systematic combining, the study presents a case of knowledge acquisition and learning in the context of an international consultancy alliance between leading Pakistani and Chinese engineering firms using six in-depth interviews of key engineers to explore the dynamic mechanisms for knowledge acquisition and learning from the Chinese firm. Grounded analysis drawn upon the Straussian version of grounded theory (GT) {{Strauss, 1990 #136} Strauss, 1998 #139} is used for data analysis in this research.FindingsIt was found that the processes of explicit and implicit knowledge acquisition from Chinese firms are integrated consultancy working, social and technical adaptability and seeking confirmation about the work done and knowledge/theories and models used in work. However, these processes are quite complex, posing serious challenges for National Engineering Services, Pakistan to acquire the required knowledge, which can be addressed through partners' motivation to share and acquire knowledge, cultural intelligence and friendship and informal association. The study also found that the knowledge acquired from technologically advanced international organizations by the host partner in the international strategic alliance not only provides a competitive edge to the local host in its local market but also builds its capacity to undertake similar projects in other parts of the world, substantially enhancing its market success.Originality/valueAdding up to the current literature that focuses on knowledge acquisition in a parent-subsidiary relationship, the current research proposes a framework for knowledge acquisition in the unique context of international strategic alliances. The research provides managerial guidelines to manage knowledge acquisition for gaining a competitive edge that would be helpful for the managers in the era of growing interdependence among the organizations across the borders.

A reward-learning framework of knowledge acquisition: An integrated account of curiosity, interest, and intrinsic-extrinsic rewards.

Recent years have seen a considerable surge of research on interest-based engagement, examining how and why people are engaged in activities without relying on extrinsic rewards. However, the field of inquiry has been somewhat segregated into three different research traditions which have been developed relatively independently-research on curiosity, interest, and trait curiosity/interest. We identify "long-term development" as a critical factor that links different research traditions, and set out an integrative perspective called the reward-learning framework of knowledge acquisition. This framework takes on the basic premise of existing reward-learning models of information seeking: that knowledge acquisition serves as an inherent reward, which reinforces people's information-seeking behavior through a reward-learning process. Critically, however, the framework reveals how the knowledge-acquisition process is sustained and boosted over a long period of time in real-life settings (i.e., self-boosting effect), allowing us to integrate the different research traditions within reward-learning models. The framework also characterizes the knowledge-acquisition process with three distinct features that are not present in the reward-learning process with extrinsic rewards- (a) selectivity, (b) vulnerability, and (c) under-appreciation. Finally, we discuss implications of the proposed framework regarding the debate over the conceptualization of broad concepts, namely; curiosity, interest, and intrinsic-extrinsic rewards. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

SSC-KA: A Framework of Space Situational Knowledge Acquisition for Status-Cognition of Satellites

With the rapid development of space technology, the environment of the space domain has become more and more complex and changeable, which brought great difficulties in cognition of space domain activity. As space domain awareness (SDA) required, any relevant information and knowledge from various sources are needed as much as possible, while all of those can be sorted and integrated for effective cognition of space objects, including the cognition of their status. This paper proposes a knowledge integration framework (SSC-KA) designed for the cognition of space target and its status. In the framework (SSC-KA), open-source information and data acquired from multi-kinds of sensors are sent into parallel channels, and then processed by the algorithm this paper designed into sequence data as the channels’ output. Furthermore, the rules and four statuses defined in this paper can be judged for the anomaly detection of satellites. Based on the space domain knowledge acquisition framework of SSC-KA, this paper describes a complete abnormal state detection method for satellites step by step through multi-level feature engineering. Therefore, the method is used to analyze four different statuses of satellites in this paper, to verify the validity and feasibility of the application of the method in the cognition of spatial events, thus laying the foundation for the cognition of Space Domain Awareness.

Unsupervised skill transfer learning for autonomous robots using distributed Growing Self Organizing Maps

A persistent challenge in the cognitive development of autonomous robotics is the unsupervised and unstructured nature of skill transfer learning where the Self Organizing Map (SOM) has been used as the enabling technology. The Growing Self-Organizing Map (GSOM) algorithm is an unsupervised, structure-adapting machine learning algorithm conventionally used for data exploration, clustering, visualization, outlier detection and dimensionality reduction. In this paper, we present the design and development of a new distributed algorithm based on the GSOM for unsupervised skill transfer learning in autonomous robotics settings which overcomes the key limitations of the SOM in real-life scenarios. We posit this new algorithm will be directly applicable to skill transfer learning scenarios that require unsupervised, incremental and on-going self-learning of multi-tasks and knowledge transfer. The distributed and scalable properties of the proposed algorithm handle large volumes of data required for unsupervised skill transfer learning, based on data parallelization. It generates multiple maps representing diverse skill knowledge, which are then projected together to a single embedding. The new algorithm is positioned within an autonomous developmental robotics framework for knowledge acquisition and skill transfer learning. This framework was further adapted to three contemporary distributed computing platforms, Hadoop, Spark and Hama. Empirical evaluation of these three adaptations using several benchmark and real-life datasets demonstrates its practical value and computational efficiency for unsupervised skill transfer learning in autonomous robots.

Integration of a geo-ontology-based knowledge model and spatial analysis into emergency response for geologic hazards

Emergency response is an important task in geologic hazard emergency management in China. Rapid emergency response actions can effectively reduce the losses caused by geologic hazards. Emergency plans and emergency command systems play an important role in emergency response. The emergency response still comes with challenges, such as the difficulty of integrating emergency plans with real-time geologic hazard information and the operability of emergency disposal in emergency plans. One possible approach to solving this problem is to model and represent knowledge and to enable the emergency command system to acquire and intelligently provide the required emergency response knowledge. In this paper, we propose a conceptual model of knowledge for geologic hazard emergency response (GHER). The geospatial characteristics of GHER knowledge are represented by the geo-ontology in the modeling process. The ontology enables semantic interoperability of GHER knowledge. A case study is used as an application test to evaluate the framework of GHER knowledge acquisition. In the framework, the GHER knowledge model enables the emergency command system to provide imperative knowledge intelligently for emergency decision-making; knowledge inference enables the emergency command system to provide important implicit knowledge for emergency response; and spatial analysis of geographic information system is applied to visualize and analyze spatial data in the process of emergency response.

Acquisition of missile fault diagnosis knowledge based on incomplete information of flow graph

Aiming at the problem of missing information and incomplete information in missile fault diagnosis, a method of acquiring missile fault diagnosis knowledge based on flow graph is proposed. Firstly, the fault feature information is extracted, and the fault diagnosis knowledge is classified by using the feature relationship to obtain a set of fault diagnosis examples under the same feature relationship. Then, the flow graph of incomplete fault diagnosis is established, and the confidence level and coverage level between each node are expressed. The knowledge acquisition framework of missile fault diagnosis is constructed. Finally, combining with the case analysis, it is verified that the fault diagnosis knowledge acquisition method has a good intuition, which provides an effective reference for missile fault diagnosis.

Enhancing green product and process innovation: Towards an integrative framework of knowledge acquisition and environmental investment

AbstractDespite the increasing interest in green innovation literature, little is known on how and under what conditions firms' knowledge transfer activities affect green innovation. There is lack of research that on how particular organizational capabilities are seen more useful and how it influences on green innovation performance. To address this research gap, we examine a mediation model in which we explore whether a firm's knowledge acquisition capability and investment in environmental management mediate the impact of buyer‐driven knowledge transfer activities on green product innovation and green process innovation. On the basis of an analysis of a sample of 239 manufacturing firms, we find that buyer‐driven knowledge activities have a greater positive impact on green product innovation than green process innovation. Investment in environmental management fully mediates the relationship between buyer‐driven knowledge transfer activities and green process innovation, and knowledge acquisition capability partially mediates the relationship between buyer‐driven knowledge transfer activities and green product innovation. The current study provides evidence that internal competencies and the role of buyers in knowledge transfer are critical for explaining the green product innovation and green process innovation. Our results suggest that buyer involvement pushes firms to develop resource acquisition capability to enhance green product innovation. Our results also highlight the importance of investment in environmental management for overcoming the environmental challenges in the manufacturing firms.

Multi Criteria Tacit Knowledge Acquisition Framework (MC-TKAF) using Fuzzy Delphi Method for supporting Talent Development Intervention Program in Malaysian Higher Education Institution

There are certain qualities and traits needed to be qualified as Academic Leader or Academic Manager. However, there is a lack of study regarding the preparation to develop and prevent the loss of these qualities and traits among the talented academicians. Lacking of this preparation will also lead to certain Academic Leadership Management roles leave vacant without being occupied. Thus, there is a requirement to have an appropriate model to measure the knowledge, skill and experience among potential academicians during Talent Development Intervention program. This paper aims to form criteria based proposed multi criteria tacit knowledge acquisition framework using fuzzy Delphi method in phase 2 of the study. Ten (10) expertise were used to form consensus finding. Result show that elements that are evaluated by the experts with rate of consensus between 50% to 90%. This finding will enable the proposed model to be evaluated using analysis software for model fitness towards tacit knowledge competence of potential Academic Leader or Academic Manager.

Knowledge recommendation for product development using integrated rough set-information entropy correction

New product development is knowledge intensive as it needs the work teams and design engineers located at various locations to constantly share, update, and re-use knowledge. As such, improving the efficiency of acquiring knowledge and coping with the challenge of frequently retrieving related knowledge have become a key factor to managing knowledge in new product development. This paper combines rough set theory and information entropy to establish a new knowledge recommender technique to address the issue of knowledge reuse for new product development. Our method enhances knowledge acquisition and reuse, as it provides a realistic framework for knowledge acquisition and reuse, encompassing the entire process from what the design and work teams need, to recommending what they should have. To validate the proposed approach, we perform experiments on a case study to demonstrate the benefit and performance.

Development of Multi Criteria Tacit Knowledge Acquisition Framework (MC-TKAF) to Support Talent Development Intervention Program in A Malaysian Comprehensive University

In Higher Education Institutions (HEI), the process of retaining leadership succession is critical since it has involved the process in choosing the right person. The purpose is to steer the institutions to sustain organizations’ excellence for academic leadership and management (ALM) position. Many ALM of Malaysia HEIs are struggling to find the right successor to replace their roles as they do not have yet any firm criteria in evaluating the competence among their potential successors in their home institutions. This study aims to propose a multi criteria tacit knowledge acquisition framework (MC-TKAF) for supporting talent development intervention program in Malaysia HEIs. It will be based on cognitive apprenticeship, socialization and informal learning theory which mostly used in acquiring knowledge from expertise to overcome talent bottleneck among novice. The main process of this study will use Fuzzy Delphi among ALM in Malaysian HEIsto get consensus judgement about the right indicator to evaluate tacit knowledge competence. Three phases involved are: Phase 1 is to analyze the existing Tacit Knowledge Acquisition (TKA) by finding the suitable parameters to construct intended framework, Phase 2 is to use the findings in Phase 1 in order to develop a new framework of Tacit Knowledge Acquisition Framework (TKAF) that suits with HEI environment. Finally, Phase 3 is to evaluate the practicality of Tacit Knowledge Acquisition Framework (TKAF) by using Multi Criteria Decision Making (MCDM) approach in supporting Talent Development Intervention Program. The objective of this paper is to propose the multi criteria tacit acquisition framework by using MCDM technique as a talent performance indicator. This paper basically will focus on Phase 1 of the research design. The constructed indicators in this paper could be served as a reference for the HEI industries to establish applicable talent performance indicators according to the properties of each TKA used.

Automated Knowledge Acquisition Framework for Supply Chain Management based on Hybridization of Case based Reasoning and Intelligent Agent

Throughout the past few years, there has been notable research effort directed towards developing automated knowledge acquisition (KA) in order to automate knowledge acquisition in Supply Chain Management (SCM) applications. Several methods utilized for the automation of supply chain management involved Intelligent Agent (IA) and Case-Based Reasoning (CBR). This paper used both approaches to bring about automated knowledge acquisition in order to assist decision-making in SCM applications. With the arrival of a new case, prior cases are retrieved from the database and the potential solutions are laid down. After the completion of acquisition, case and solution outcome are analyzed and evaluated according to function similarity. Finally, after evaluating the new case along with the problem details and the chosen solution, the case is retained in the database for issues that will arise in future applications.

Neural Knowledge Acquisition via Mutual Attention Between Knowledge Graph and Text

We propose a general joint representation learning framework for knowledge acquisition (KA) on two tasks, knowledge graph completion (KGC) and relation extraction (RE) from text. In this framework, we learn representations of knowledge graphs (KGs) and text within a unified parameter sharing semantic space. To achieve better fusion, we propose an effective mutual attention between KGs and text. The reciprocal attention mechanism enables us to highlight important features and perform better KGC and RE. Different from conventional joint models, no complicated linguistic analysis or strict alignments between KGs and text are required to train our models. Experiments on relation extraction and entity link prediction show that models trained under our joint framework are significantly improved in comparison with other baselines. Most existing methods for KGC and RE can be easily integrated into our framework due to its flexible architectures. The source code of this paper can be obtained from https://github.com/thunlp/JointNRE.

Reasoning and Knowledge Acquisition Framework for 5G Network Analytics.

Autonomic self-management is a key challenge for next-generation networks. This paper proposes an automated analysis framework to infer knowledge in 5G networks with the aim to understand the network status and to predict potential situations that might disrupt the network operability. The framework is based on the Endsley situational awareness model, and integrates automated capabilities for metrics discovery, pattern recognition, prediction techniques and rule-based reasoning to infer anomalous situations in the current operational context. Those situations should then be mitigated, either proactive or reactively, by a more complex decision-making process. The framework is driven by a use case methodology, where the network administrator is able to customize the knowledge inference rules and operational parameters. The proposal has also been instantiated to prove its adaptability to a real use case. To this end, a reference network traffic dataset was used to identify suspicious patterns and to predict the behavior of the monitored data volume. The preliminary results suggest a good level of accuracy on the inference of anomalous traffic volumes based on a simple configuration.

Wearable expert system development: definitions, models and challenges for the future

PurposeA wearable expert system (WES) is an expert system designed and implemented to obtain input from and give outputs to wearable devices. Among its distinguishing features are the direct cooperation between domain experts and users, and the interaction with a knowledge maintenance system devoted to dynamically update the knowledge base taking care of the evolving scenario. The paper aims to discuss these issues.Design/methodology/approachThe WES development method is based on the Knowledge Acquisition Framework based on Knowledge Artifact (KAFKA) framework. KAFKA employs multiple knowledge artifacts, each devoted to the acquisition and management of a specific kind of knowledge. The KAFKA framework is introduced from both the conceptual and computational points of view. An example is given which demonstrates the interaction, within this framework, of taxonomies, Bayesian networks and rule-based systems. An experimental assessment of the framework usability is also given.FindingsThe most interesting characteristic of WESs is their capability to evolve over time, due both to the measurement of new values for input variables and to the detection of new input events, that can be used to modify, extend and maintain knowledge bases and to represent domains characterized by variability over time.Originality/valueWES is a new and challenging concept, dealing with the possibility for a user to develop his/her own decision support systems and update them according to new events when they arise from the environment. The system fully supports domain experts and users with no particular skills in knowledge engineering methodologies, to create, maintain and exploit their expert systems, everywhere and when necessary.