원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
Vol.9 No.4
2016.04
pp.309-320
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보