Discovering Classification Dimensions for Managing Scientific Resources

Bing Ma, Hai Zhuge

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The Resource Space Model is a systematic theory and method for managing the contents of resources by a multi-dimensional classification space. Different dimensions represent different classification methods. Each dimension can be regarded as a classification tree. Design of Resource Space is mainly based on domain knowledge and experience of designer. This paper implements an approach to automatic discovering dimensions from texts (taking scientific papers as experimental data). It consists of four stages: 1) representing each text as a collection of tuples in the form of (word, value); 2) constructing an initial dimension; 3) constructing the hierarchical dimensions based on the initial dimension; and 4) post-processing of outlier texts. The experimental results show that our model achieves a significant improvement of performance on classifying texts.
Original languageEnglish
Title of host publicationProceedings - 15th International Conference on Semantics, Knowledge and Grids
Subtitle of host publicationOn Big Data, AI and Future Interconnection Environment, SKG 2019
EditorsHai Zhuge, Xiaoping Sun
PublisherIEEE
Pages97-102
Number of pages6
ISBN (Electronic)978-1-7281-5823-5
ISBN (Print)978-1-7281-5824-2
DOIs
Publication statusPublished - 23 Mar 2020
Event2019 15th International Conference on Semantics, Knowledge and Grids (SKG) - Guangzhou, China
Duration: 17 Sep 201918 Sep 2019

Publication series

NameProceedings - 15th International Conference on Semantics, Knowledge and Grids: On Big Data, AI and Future Interconnection Environment, SKG 2019

Conference

Conference2019 15th International Conference on Semantics, Knowledge and Grids (SKG)
Period17/09/1918/09/19

Keywords

  • Classification dimensions
  • Hierarchical dimensions
  • Resource Space Model

Cite this

Ma, B., & Zhuge, H. (2020). Discovering Classification Dimensions for Managing Scientific Resources. In H. Zhuge, & X. Sun (Eds.), Proceedings - 15th International Conference on Semantics, Knowledge and Grids: On Big Data, AI and Future Interconnection Environment, SKG 2019 (pp. 97-102). [9044054] (Proceedings - 15th International Conference on Semantics, Knowledge and Grids: On Big Data, AI and Future Interconnection Environment, SKG 2019). IEEE. https://doi.org/10.1109/SKG49510.2019.00024