Domain Knowledge Integration and Distillation using text Mining
Keywords:
Integration ,Distillation , Data Preprocessing , NLP, Confusion Matrix , Classification.Abstract
Imagine a media organization striving to combat the spread of misinformation during an election season. The ability to synthesize and simplify diverse knowledge sources becomes crucial for accurately predicting and identifying fake news. In today's dynamic world, this need extends across all disciplines. This paper explores two fundamental processes essential for achieving such synthesis: domain knowledge integration, which harmonizes information from various sources, and distillation, which extracts essential insights. Through a comprehensive literature review and detailed case studies, we examine the methodologies, challenges, and benefits associated with these processes. Embracing domain knowledge integration and distillation enables organizations to streamline operations, enhance strategic planning, and gain a competitive edge. The results reveal significant improvements in fake news detection accuracy, streamlined operations, and increased stakeholder confidence.
Moreover, the application of these processes in real-world scenarios underscores their practical value. By leveraging these methodologies, media organizations can better allocate resources, respond more quickly to emerging threats, and maintain public trust. This research thus provides a robust framework for other sectors facing similar challenges, highlighting the universal applicability of these techniques. The insights gained from this study are poised to influence future strategies in combating misinformation, promoting a more informed and resilient society. This research significantly contributes to understanding these methodologies, ensuring originality and unwavering integrity in its findings.
Downloads
References
Xu Chen, Yongfeng Zhang, and Zheng Qin. 2018. Adversarial Distillation for Efficient Recommendation with External Knowledge. ACM Journals. 37:35-54.
Yuzhu Wang and Lechao Cheng. 2023. Improving Knowledge Distillation via Regularizing Feature Norm and Direction
Himel Das Gupta .2022. A RoadMap to Domain Knowledge Integration in Machine Learning.
Mertins, K., & Jochem, R. (2006). Integrating knowledge management and enterprise resource planning
Zhang, X., & Zhao, J. (2018). Knowledge graph embedding: A survey of approaches and applications.
Bonomi, L., Herrera, P., & Micchelli, C. A. (2012). Distillation of knowledge from multiple information sources. Applied and Computational Harmonic Analysis
Tsai, C. H., Chiang, J. C., & Lin, C. H. (2015). Extracting and visualizing the knowledge map of educational big data
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
