Abstract
Approximately 90% of the world’s data is held in unstructured formats which mean that it is getting harder for people to extract information from huge amount of data. Motivated by the practical need of specific types of real world data analysis problems, many mining algorithms and knowledge management systems are designed and studied. The management of a vast amount of unstructured customer-related knowledge in academic libraries has become area of significant recent interest. In this paper, we proposed a scheme organizing customer knowledge in academic libraries by using text mining technology. The entity extraction phase called Named Entity Recognition aims to discover proper names, their variations and classes. A database with related entities needs to be created for entity extraction and correlation processes. Benefited from LRD method for text mining, the scheme provides a formal and explicit specification to deliver a shared conceptualization of customer knowledge.
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Zhang, Y., Gu, H. (2011). Text Mining with Application to Academic Libraries. In: Yu, Y., Yu, Z., Zhao, J. (eds) Computer Science for Environmental Engineering and EcoInformatics. CSEEE 2011. Communications in Computer and Information Science, vol 158. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22694-6_28
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DOI: https://doi.org/10.1007/978-3-642-22694-6_28
Publisher Name: Springer, Berlin, Heidelberg
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