European Journal of Information and Knowledge Management

Evaluation of Natural Language Processing Techniques for Information Retrieval

Hope Nabankema — Wed, 27 Mar 2024 00:00:00 +0300

Purpose: The main objective of the study was to investigate the evaluation of Natural Language Processing techniques for information retrieval.

Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.

Findings: The findings reveal that there exists a contextual and methodological gap relating to Natural Language Processing techniques for information retrieval. Preliminary empirical review revealed that NLP methods significantly improved the accuracy and efficiency of information retrieval systems. Through systematic evaluation, various NLP techniques, including tokenization, named entity recognition, semantic parsing, and word embeddings, were found to enhance retrieval performance across diverse datasets and domains. By considering context and user intent, researchers aimed to develop more contextually aware and personalized information retrieval systems. The study emphasized the need for further research to explore hybrid approaches and domain-specific adaptations, ultimately highlighting the transformative potential of NLP in revolutionizing information access and utilization.

Unique Contribution to Theory, Practice and Policy: Information Foraging theory, Relevance theory and Cognitive Load theory may be used to anchor future studies on Natural Language Processing techniques. The study provided recommendations to enhance information retrieval systems. It suggested integrating advanced NLP techniques such as named entity recognition and sentiment analysis to improve query understanding and document relevance. Additionally, the study recommended adopting word embeddings and semantic parsing techniques for better semantic understanding of user queries and documents. It emphasized the importance of domain-specific adaptations and continuous evaluation of NLP techniques to tailor them to specific application domains and keep pace with evolving user needs and technological advancements.

Semantic Technologies in Knowledge Management

Charlie Jones — Wed, 27 Mar 2024 00:00:00 +0300

Purpose: The general objective of this study was to explore semantic technologies in knowledge management.

Findings: The findings reveal that there exists a contextual and methodological gap relating to semantic technologies in knowledge management. Preliminary empirical review revealed that these technologies hold significant potential for enhancing knowledge organization, retrieval, and utilization within organizations. While theoretical benefits were recognized, practical implementation faced challenges such as technical constraints, organizational resistance, and the need for alignment with organizational objectives. Despite these hurdles, the study emphasized the importance of overcoming barriers through comprehensive approaches involving technological solutions, organizational change management, and stakeholder engagement. Overall, the study highlighted the transformative potential of semantic technologies in knowledge management but underscored the necessity of addressing challenges to realize their full benefits.

Unique Contribution to Theory, Practice and Policy: The Social Construction of Technology (SCOT) theory, Actor-Network Theory (ANT) and Resource-Based View (RBV) theory may be used to anchor future studies on semantic technologies. The study made several recommendations to enhance the adoption and utilization of semantic technologies. It suggested developing standardized ontologies, prioritizing user-centric design principles, and providing ongoing training to improve digital literacy among practitioners. Additionally, the study emphasized the importance of governance mechanisms for ensuring data quality, fostering collaborative networks for knowledge sharing, and embracing a culture of experimentation and adaptation. These recommendations aimed to address challenges such as interoperability, usability, and trustworthiness, ultimately enabling organizations to leverage semantic technologies more effectively in their knowledge management practices.

Effectiveness of Recommender Systems in Knowledge Discovery

Kerry Nyachama — Thu, 28 Mar 2024 00:00:00 +0300

Purpose: The general purpose of the study was to investigate the effectiveness of recommender systems in knowledge discovery.

Findings: The findings reveal that there exists a contextual and methodological gap relating to recommender systems in knowledge discovery. The study on the effectiveness of recommender systems in knowledge discovery found that such systems played a pivotal role in facilitating users' exploration of vast information repositories, enabling them to uncover relevant resources and expand their knowledge. It found that recommender systems employing advanced algorithms and personalized techniques demonstrated higher effectiveness in generating relevant recommendations tailored to users' preferences and needs. Additionally, the study highlighted the positive correlation between user engagement metrics and knowledge discovery outcomes, emphasizing the importance of fostering active user participation in the recommendation process. Contextual information was also identified as a crucial factor influencing recommendation effectiveness. Overall, the study underscored the significance of continuous refinement and optimization of recommender system algorithms to enhance knowledge discovery outcomes for users.

Unique Contribution to Theory, Practice and Policy: The Social Learning theory, Information Foraging theory and Cognitive Load theory may be used to anchor future studies on recommender systems in knowledge discovery. The study provided recommendations to enhance the efficacy of such systems. It suggested adopting hybrid recommender systems that combine collaborative and content-based filtering techniques to offer more accurate and diverse recommendations. Additionally, the study emphasized the importance of integrating contextual information into recommendation algorithms to dynamically adjust recommendations based on situational context. Furthermore, it recommended the use of explainable AI techniques to improve transparency and user understanding of recommendation processes. Maximizing user engagement through active participation and feedback was also highlighted as crucial, along with prioritizing recommendation diversity to foster exploration and serendipitous discovery of new knowledge resources.

The Role of Semantic Web Technologies in Improving Knowledge Management Systems

Gracie Shalom — Wed, 27 Mar 2024 00:00:00 +0300

Purpose: The general purpose of this study was to investigate the role of semantic web technologies in improving knowledge management systems.

Findings: The findings reveal that there exists a contextual and methodological gap relating to role of semantic web technologies in improving knowledge management systems. Preliminary empirical review revealed that that semantic web technologies significantly enhanced knowledge management systems across diverse industries, facilitating better organization, integration, and retrieval of knowledge resources. Adoption of these technologies addressed key challenges such as information overload and semantic heterogeneity, leading to increased discoverability, interoperability, and reuse of knowledge assets. However, successful implementation required considerations of factors like organizational readiness and user acceptance, necessitating investments in training, support, and change management strategies. Overall, semantic web technologies improved knowledge management practices and enhanced organizational competitiveness in the knowledge-driven economy.

Unique Contribution to Theory, Practice and Policy: The Social Constructionism theory, Cognitive Load and Diffusion of Innovation may be used to anchor future studies on semantic web technologies. The study recommended that organizations invest in semantic web technologies for knowledge management, prioritize user-friendly KMS, foster a culture of collaboration, establish robust governance, and monitor KMS performance. These measures ensured effective knowledge sharing, decision-making, and innovation, enhancing organizational competitiveness and success.

Impact of Gamification on Knowledge Acquisition

Serena Nakiyemba — Wed, 27 Mar 2024 00:00:00 +0300

Purpose: The general objective of the study was to examine the impact of gamification on knowledge acquisition.

Findings: The findings reveal that there exists a contextual and methodological gap relating to gamification on knowledge acquisition. Preliminary empirical review revealed that integrating gamification elements into educational platforms and training programs consistently led to improvements in knowledge acquisition across various contexts. Gamified learning environments effectively motivated learners, fostered engagement, and facilitated collaborative learning experiences. Findings emphasized the importance of aligning gamification strategies with instructional goals, considering learner preferences, and iterating design based on feedback. While the study highlighted the potential of gamification to enhance learning outcomes, further research is needed to explore optimal strategies and long-term effects. Overall, gamification emerged as a promising approach for promoting effective learning and skill development.

Unique Contribution to Theory, Practice and Policy: The Self Determination theory, Cognitive Load theory and Constructivism theory may be used to anchor future studies on gamification of knowledge acquisition. The study provided several recommendations based on its findings. Firstly, it suggested integrating gamification elements into educational programs to enhance engagement and learning outcomes. Secondly, it recommended leveraging gamification for promoting active learning and problem-solving skills among learners. Thirdly, it advised organizations to invest in gamified training programs to improve knowledge acquisition and job performance among employees. Lastly, the study emphasized the importance of ongoing research to better understand the effectiveness of gamification in education and training contexts.