Knowledge Mining: A Cross-disciplinary Survey

Knowledge mining is a widely active research area across disciplines such as natural language processing (NLP), data mining (DM), and machine learning (ML). The overall objective of extracting knowledge from data source is to create a structured representation that allows researchers to better under...

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Bibliographic Details
Published in:International journal of automation and computing 2022-04, Vol.19 (2), p.89-114
Main Authors: Rui, Yong, Carmona, Vicente Ivan Sanchez, Pourvali, Mohsen, Xing, Yun, Yi, Wei-Wen, Ruan, Hui-Bin, Zhang, Yu
Format: Article
Language:English
Subjects:
Data mining
Interdisciplinary aspects
Machine learning
Natural language processing
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Summary:Knowledge mining is a widely active research area across disciplines such as natural language processing (NLP), data mining (DM), and machine learning (ML). The overall objective of extracting knowledge from data source is to create a structured representation that allows researchers to better understand such data and operate upon it to build applications. Each mentioned discipline has come up with an ample body of research, proposing different methods that can be applied to different data types. A significant number of surveys have been carried out to summarize research works in each discipline. However, no survey has presented a cross-disciplinary review where traits from different fields were exposed to further stimulate research ideas and to try to build bridges among these fields. In this work, we present such a survey.
ISSN:2731-538X
1476-8186
2731-5398
1751-8520
DOI:10.1007/s11633-022-1323-6