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Knowledge Mining:A Cross-disciplinary Survey 被引量:3
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作者 Yong Rui Vicente Ivan Sanchez Carmona +4 位作者 Mohsen Pourvali Yun Xing Wei-Wen Yi hui-bin ruan Yu Zhang 《Machine Intelligence Research》 EI CSCD 2022年第2期89-114,共26页
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 ... 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. 展开更多
关键词 Knowledge mining knowledge extraction information extraction association rule INTERPRETABILITY
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