The Less-Used Books Management Using Knowledge Management at Library and Information Health Science Department, Chiang Mai University Library

Kankonsue, Thanapun; Sookruay, Tippawan; Gunta-in, Sodsri; Srimora, Prakaidao; Meta, Aorrakanya; Saravudecha, Chompunuch

doi:10.1145/3354142.3354147

Cited by 1 publication

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“…Many scholars have explored the ways to aggregate the knowledge in L&I resources, aiming to scientifically organize, mine, and manage the knowledge, and to innovate the knowledge service model [18][19][20][21]. For example, Kankonsue et al [22] defined the connotations of knowledge aggregation of multi-source L&I resources, effectively organized the knowledge contained in L&I resources, and mined the associations between the knowledge. Borrego [23] proposed a knowledge aggregation strategy based on topic-generated multi-source L&I resources: the topic probability model of latent Dirichlet allocation (LDA) was combined with the hybrid neural network BiLSTM-CNN-CRF (bidirectional LSTM-convolutional neural networkconditional random field) to learn and segment the contents, and to generate knowledge topics.…”

Section: Introductionmentioning

confidence: 99%

Resource Classification and Knowledge Aggregation of Library and Information Based on Data Mining

Qin¹

2020

ISI

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The traditional knowledge service systems have nonuniform data structures. Some data are structured, while some are semi-structured and even non-structured. Big data technology helps to optimize the integration and retrieval of the massive data on library and information (L&I), making it possible to classify the resources and optimize the configuration of L&I resource platforms according to user demand. Therefore, this paper introduces the new information service model of big data resources and knowledge services to the processing of L&I data. Firstly, the data storage structure and relationship model of the L&I resource platform were established, and used to sample and integrate the keywords of resource retrieval. Next, an L&I resource classification model was constructed based on support vector machine (SVM), and applied to extract and quantify the attributes of the keywords of resource retrieval. After that, a knowledge aggregation model was developed for a complex network of multiple L&I resource platforms. Experimental results demonstrate the effectiveness of the proposed knowledge aggregation model. The research findings provide a reference for the application of data mining in resource classification.

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Section: Introductionmentioning

confidence: 99%

Resource Classification and Knowledge Aggregation of Library and Information Based on Data Mining

Qin¹

2020

ISI

View full text Add to dashboard Cite

show abstract

The Less-Used Books Management Using Knowledge Management at Library and Information Health Science Department, Chiang Mai University Library

Cited by 1 publication

References 1 publication

Resource Classification and Knowledge Aggregation of Library and Information Based on Data Mining

Resource Classification and Knowledge Aggregation of Library and Information Based on Data Mining

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