@inproceedings{268eb77ac7c2499cb49cbbc064a4d83a,
title = "Discovering Classification Dimensions for Managing Scientific Resources",
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.",
keywords = "Classification dimensions, Hierarchical dimensions, Resource Space Model",
author = "Bing Ma and Hai Zhuge",
year = "2020",
month = mar,
day = "23",
doi = "10.1109/SKG49510.2019.00024",
language = "English",
isbn = "978-1-7281-5824-2",
series = "Proceedings - 15th International Conference on Semantics, Knowledge and Grids: On Big Data, AI and Future Interconnection Environment, SKG 2019",
publisher = "IEEE",
pages = "97--102",
editor = "Hai Zhuge and Xiaoping Sun",
booktitle = "Proceedings - 15th International Conference on Semantics, Knowledge and Grids",
address = "United States",
note = "2019 15th International Conference on Semantics, Knowledge and Grids (SKG) ; Conference date: 17-09-2019 Through 18-09-2019",
}