Noise data of web page is easy to cause the topic drift problem in web information extraction. To improve the accuracy of web information extraction effectively, a novel calculation method of mixing entropy is presented, which can more accurately reflect the topic information of web page. The information block is discussed under the multi-page site environment. The impacts of information within local page and the same information distribution between web pages generated by template are all considered so as to ensure the precision of calculating information entropy. The method is verified by calculating the entropy of information block in information extraction. Compared with other methods, the simulation results indicate that the novel method shows great superiority over other traditional methods in both the accuracy of information entropy calculation and discrimination between topic-related information blocks and topic-unrelated information blocks.
목차
Abstract 1. Introduction 2. Calculation of Hybrid Information Entropy of Web Page 2.1. Information Entropy Theory 2.2. Calculation for the Information Entropy based on Page Set 2.3. Calculation for the Information Entropy based on Page Content 2.4. Calculation for the Hybrid Entropy of Web Page 3. Experimental Results 4. Conclusions Acknowledgements References
키워드
web hybrid entropyinformation extractioninformation distributionfeature word
저자
Rong Li [ Department of computer Xinzhou Teachers’ University, Xinzhou 034000, China ]
Corresponding author
Hongbin Wang [ Department of computer Xinzhou Teachers’ University, Xinzhou 034000, China ]
보안공학연구지원센터(IJDTA) [Science & Engineering Research Support Center, Republic of Korea(IJDTA)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Database Theory and Application
간기
격월간
pISSN
2005-4270
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Database Theory and Application Vol.6 No.5