Intelligent Knowledge Management in Expert Mining in Traditional Chinese Medicines
Yong Shi (),
Lingling Zhang,
Yingjie Tian and
Xingsen Li
Additional contact information
Yong Shi: Chinese Academy of Sciences
Lingling Zhang: University of Chinese Academy of Sciences
Yingjie Tian: Chinese Academy of Sciences
Xingsen Li: Zhejiang University
Chapter 8 in Intelligent Knowledge, 2015, pp 131-139 from Springer
Abstract:
Abstract In the research of intelligent knowledge acquirement, it is hard to integrate domain knowledge to the algorithms, which involves some key issues as follows. First, how to accomplish the structured representation of domain knowledge, which can help computers understand human languages. Second, results of data mining algorithms refreshing with the change of domain knowledge; finally, needing a friendly interface, which can help the experts represent structured domain knowledge, and make the interaction between the experts and the computers conveniently during setting and adjusting the parameters of algorithms.
Keywords: Association Rule; Domain Knowledge; Bitter Taste; Semantic Knowledge; Mining Result (search for similar items in EconPapers)
Date: 2015
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:spbrcp:978-3-662-46193-8_8
Ordering information: This item can be ordered from
http://www.springer.com/9783662461938
DOI: 10.1007/978-3-662-46193-8_8
Access Statistics for this chapter
More chapters in SpringerBriefs in Business from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().