Emerging Need for Disruption in the Next Trend of Artificial Intelligence-Controlled Transformation Using Knowledge Mining
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Knowledge mining is an emerging type of artificial intelligence (AI), that uses a grouping of AI facilities to determine satisfied thought over huge volumes of unstructured, semi-structured, and structured data that permit industries to extremely recognize their data, search it, expose visions and found associations and designs at scale. Although the initial trend of AI contained numerous slight applications, such as the preparation of a particular model over a single statistics basis of a positive kind for a particular problem, knowledge mining is the next trend of Artificial Intelligence, producing an active quantity of data associations and designs. It has rapidly brought a main part of initiative digital transformation creativity that basically modification how groups brand a sense of real-world statistics. Through this survey, we have analyzed more than two-thirds of 68% of respondents to a current Harvard Business Brush up Analytic Services survey think knowledge mining is key to succeeding in their corporations' considered objectives in the next 18 months. Then the requirement for knowledge mining is rapidly increasing 80% are using physical approaches to switch unstructured data, and those approaches will rapidly be overtaken by the development of statistics and possibly apply circumstances in which this data has delivered excessive rate.
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Haricharan,” Knowledge Mining – Uncover the value of unstructured data with AI-driven search”, September 15, 2022. https://saxon.ai/blogs/knowledge-mining-uncover-the-value-of-unstructured-data-with-ai-driven-search/
Auer, S. et al., “Dbpedia: A nucleus for a web of open data”, In Proceedings of the 6th International. The Semantic Web and 2nd Asian Conference on Asian Semantic Web Conference, ISWC’07/ASWC’07, 722–735 (Springer, Berlin, Heidelberg, 2007. DOI. https://dl.acm.org/doi/10.5555/1785162.1785216
Mahesh S. Raisinghani, “Knowledge Mining”, January 2006, DOI: https://doi.org/10.4018/978-1-59140-560-3.ch056
Ehrlinger, L. & Wöß, W. Towards a definition of knowledge graphs. In SEMANTiCS (Posters, Demos, SuCCESS), vol. 48, 2, 2016. https://www.researchgate.net/publication/323316736_Towards_a_Definition_of_Knowledge_Graphs
Duan, Y. et al. Specifying architecture of knowledge graphs with data, information, knowledge, and wisdom graphs. In 2017 IEEE 15th International Conference on Software Engineering Research, Management, and Applications (SERA), pp. 327–332, IEEE, June 2017. DOI: https://doi.org/10.1109/SERA.2017.7965747
Finkel, J. R., Grenager, T. & Manning, C. D. Incorporating non-local information into information extraction systems by Gibbs sampling. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05), pp. 363–370, 2005. DOI: https://doi.org/10.3115/1219840.1219885
Shen, Y., Colloc, J., Jacquet-Andrieu, A., Guo, Z. & Liu, Y. Constructing ontology-based cancer treatment decision support system with case-based reasoning. In International Conference on Smart Computing and Communication, pp. 278–288, Springer, December 2018. DOI: https://doi.org/10.48550/arXiv.1812.01891
Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S. & Sontag, D. Learning a health knowledge graph from electronic medical records. Sci. Rep. 7, pp. 1–11, 2017. DOI: https://doi.org/10.1038/s41598-017-05778-z
Auer, S. et al. Towards a knowledge graph for science. In Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics, pp. 1–6, 2018. DOI: https://doi.org/10.1145/3227609.3227689
Mavroeidis, V. & Bromander, S. Cyber threat intelligence model: An evaluation of taxonomies, sharing standards, and ontologies within cyber threat intelligence. In 2017 European Intelligence and Security Informatics Conference (EISIC), pp. 91–98, IEEE, September, 2017. DOI: https://doi.org/10.1109/EISIC.2017.20
Joshi, A., Lal, R., Finin, T. & Joshi, A. Extracting cybersecurity-related linked data from text. In 2013 IEEE Seventh International Conference on Semantic Computing, pp. 252–259. IEEE, 2013. https://doi.org/10.1109/ICSC.2013.50
A review of the use of AI in the mining industry: Insights and ethical considerations for multi-objective optimization. Extractive Industries and Society Volume 17, pp. 101440, March 2024. https://doi.org/10.1016/j.exis.2024.101440
Zhaoguang Xu, et al., Solution knowledge mining and recommendation for quality problem-solving Computers & Industrial Engineering Volume 159, pp. 107313, September 2021. DOI: https://doi.org/10.1016/j.cie.2021.107313
Kenneth A. Kaufman, Ryszard S. Michalski, From Data Mining to Knowledge Mining Handbook of Statistics Volume 24, pp. 47-75, 2005- DOI: https://doi.org/10.1007/s10506-022-09334-7
Pai, R., & Wadhwa, A. (2022). Artificial Intelligence based Modern Approaches to Diagnose Alzheimer s. In Indian Journal of Artificial Intelligence and Neural Networking (Vol. 2, Issue 2, pp. 1–14). https://doi.org/10.54105/ijainn.b1045.022222
Naresh Patel K M, Kiran P, Preprocessing Methods for Unstructured Healthcare Text Data. (2019). In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 2S, pp. 715–719). https://doi.org/10.35940/ijitee.b1024.1292s19
Sokegbe, A. I., & Nainwal, A. (2020). Unstructured Data Processing using Spark for Topics Modelling. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 5, pp. 1060–1063). https://doi.org/10.35940/ijeat.e9992.069520
Kiran Adnan, Rehan Akbar, Khor Siak Wang, Information Extraction from Multifaceted Unstructured Big Data. (2019). In International Journal of Recent Technology and Engineering (Vol. 8, Issue 2S8, pp. 1398–1404). https://doi.org/10.35940/ijrte.b1074.0882s819
Jain, V. (2023). An Overview on Data Mining and Data Fusion. In Indian Journal of Data Mining (Vol. 3, Issue 1, pp. 1–5). https://doi.org/10.54105/ijdm.a1624.053123