Please wait a minute...
New Technology of Library and Information Service  2007, Vol. 2 Issue (8): 56-58    DOI: 10.11925/infotech.1003-3513.2007.08.13
Current Issue | Archive | Adv Search |
XFML-based Representation of Faceted Classification
Shi Guoliang
(Department of Intormation Management, Nanjing University,Nanjing 210093,China )
Download: PDF (379 KB)  
Export: BibTeX | EndNote (RIS)      
Abstract  

XFML,standing for eXchangeable Faceted Metadata Language, exchanges metadata in the form of faceted hierarchies. Its basic building blocks are topics,also called categories. XFML can’t solve all the metadata’s needs. It focuses on interchanging faceted classification and indexing data.The background,concept,and data representation method of XFML and principles of Web indexing with XFML are discussed in this paper. As XFML is a new concept to Chinese,using XFML will benefit the develop of Web information organization in China.

Key wordsXFML      Web      Classification      Indexing     
Received: 23 May 2007      Published: 25 August 2007
ZTFLH: 

G254

 
Corresponding Authors: Shi Guoliang     E-mail: gl-shi@163.com
About author:: Shi Guoliang

Cite this article:

Shi Guoliang. XFML-based Representation of Faceted Classification. New Technology of Library and Information Service, 2007, 2(8): 56-58.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2007.08.13     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2007/V2/I8/56

[1] eXchangeable Faceted Metadata Language[EB/OL].[2007-05-07].http://xfml.org/.
[2] XFML Core - eXchangeable Faceted Metadata Language[EB/OL].[2007-05-07].http://www.xfml.org/spec/1.0.html.
[3] Murray P C.Part 1:Knowledge Organization and Faceted Classification[J].The Barrington Report on Advanced Knowledge Organization and Retrieval,2004,1(1):29-34.
[4] Barre K L. Bliss and Ranganathan:Synthesis,Synchronicity or Sour Grapes[J]ISKO,2000(7):157-163.
[5] Louise S.A Simplified Model for Facet Analysis:Ranganathan 101[J].Canadian Journal of Information and Library Science,1998,23(1/2):1-30.

[1] Yu Bengong, Zhu Mengdi. Question Classification Based on Bidirectional GRU with Hierarchical Attention and Multi-channel Convolution[J]. 数据分析与知识发现, 2020, 4(8): 50-62.
[2] Zhao Yang, Zhang Zhixiong, Liu Huan, Ding Liangping. Classification of Chinese Medical Literature with BERT Model[J]. 数据分析与知识发现, 2020, 4(8): 41-49.
[3] Weng Mengjuan,Yao Changqing,Han Hongqi,Wang Lijun,Ran Yaxin. Classification and Indexing Method with CNN for Imbalanced Datasets[J]. 数据分析与知识发现, 2020, 4(7): 87-95.
[4] Tang Xiaobo,Gao Hexuan. Classification of Health Questions Based on Vector Extension of Keywords[J]. 数据分析与知识发现, 2020, 4(7): 66-75.
[5] Wang Xinyun,Wang Hao,Deng Sanhong,Zhang Baolong. Classification of Academic Papers for Periodical Selection[J]. 数据分析与知识发现, 2020, 4(7): 96-109.
[6] Li Keyu,Wang Hao,Gong Lijuan,Tang Huihui. Measurement and Distribution of Index Quality in Research Topics from Academic Databases[J]. 数据分析与知识发现, 2020, 4(6): 91-108.
[7] Wang Sidi,Hu Guangwei,Yang Siyu,Shi Yun. Automatic Transferring Government Website E-Mails Based on Text Classification[J]. 数据分析与知识发现, 2020, 4(6): 51-59.
[8] Zhang Runtong,Chen Donghua,Zhao Hongmei,Zhu Xiaomin. Computer-Assisted ICD-11 Coding Method Based on Chinese Semantic Analysis[J]. 数据分析与知识发现, 2020, 4(4): 44-55.
[9] Yan Chun,Liu Lu. Classifying Non-life Insurance Customers Based on Improved SOM and RFM Models[J]. 数据分析与知识发现, 2020, 4(4): 83-90.
[10] Xiong Xin,Wang Hao,Zhang Haichao,Zhang Baolong. Impacts of Chinese Term Granularity on Measuring Term Discriminative Capacity[J]. 数据分析与知识发现, 2020, 4(2/3): 143-152.
[11] Xu Yuemei,Liu Yunwen,Cai Lianqiao. Predicitng Retweets of Government Microblogs with Deep-combined Features[J]. 数据分析与知识发现, 2020, 4(2/3): 18-28.
[12] Gong Lijuan,Wang Hao,Zhang Zixuan,Zhu Liping. Reducing Dimensions of Custom Declaration Texts with Word2Vec[J]. 数据分析与知识发现, 2020, 4(2/3): 89-100.
[13] Bengong Yu,Yumeng Cao,Yangnan Chen,Ying Yang. Classification of Short Texts Based on nLD-SVM-RF Model[J]. 数据分析与知识发现, 2020, 4(1): 111-120.
[14] Weimin Nie,Yongzhou Chen,Jing Ma. A Text Vector Representation Model Merging Multi-Granularity Information[J]. 数据分析与知识发现, 2019, 3(9): 45-52.
[15] Yunfei Shao,Dongsu Liu. Classifying Short-texts with Class Feature Extension[J]. 数据分析与知识发现, 2019, 3(9): 60-67.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn