Earticle

초록

영어

In recent years, research in data provenance has attracted a lot of attention, since it helps to judge the relevance and trustworthiness of the information enclosed in the data. However, many webpages still lack provenance annotation, and this is a main obstacle of tracing the content. In this paper, we propose a model for on-line Web paper variation, based on the W3C PROV Data Model. A semantic similarity clustering method is adopted to determine the relationship within the documents derivation, and feature words variation and the responsible person can be found with the aid of PROV-O. To verify this model, a detailed case study is shown in this paper.

목차

Abstract
 1. Introduction
 2. Analysis Classes and Properties of PROV-O in our Research
 3. Modeling Paper on-Line
  3.1 Entity Extension
  3.2 Activity Extension
  3.3 Agent Extension
 4. Web Content Provenance Approach
  4.1 Analysis of Webpage Direvation
  4.2 Analysis of Web Content Properties
  4.3 Automatic Discovery Processes for Document Provenance
 5. The Process Procedures
  5.1 Similar Document and Agent Discovery Based on Document Clustering
  5.2 Tracing Property Changes in Details Within a Cluster
 6. Experimental Results
 7. Conclusions
 Acknowledgement
 References

키워드

저자

• Jing Ni [ Department of Information Management, Beijing Institute of Petrochemical Technology, Beijing, China ]
• Jia Hao [ School of mechanical engineering, Beijing Institute of Technology, Beijing, China 100081 ]
• Xuemei Li [ Department of Information Management, Beijing Institute of Petrochemical Technology, Beijing, China ]
• Tong Zhao [ Department of Information Management, Beijing Institute of Petrochemical Technology, Beijing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605