Earticle

초록

영어

With the rapid development of Internet technology, Web has become a huge information source with massive amounts of data. But these data are usually embedded in the semi-structured pages. In order to use these data effectively, the primary problem is to extract the data and store them in structured form. Most of current approaches use a single classifier to extract web data, but relying on a single classifier is not sufficient and different classifier has different performance for the same problem. In this paper, we use the method of ensemble learning for web data extraction. Firstly, we parse the page as a Dom tree, identify the main data regions, and construct feature sets of text nodes in the region. Secondly, we choose multiple kinds of base classifiers (SVM, KNN and Random Forest) to build classification models and then use the linear method to integrate results of each classification model. Finally, we combine integration results with heuristic rules to get the final extraction results. The experiment results show that our approach outperforms the baseline approaches and has a good robustness.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Our Approach
  3.1. Framework
  3.2. Main Region Identification
  3.3. Model Construction
 4. Experiments
  4.1. Dataset
  4.2. Category
  4.3. Evaluation Criteria
  4.4. Discussion on Experimental Results
 5. Conclusion
 References

키워드

저자

• Yongquan Dong [ School of Computer Science and Technology, Jiangsu Normal University, Xuzhou 221116, China ]
• Qiang Chu [ School of Computer Science and Technology, Jiangsu Normal University, Xuzhou 221116, China ]
• Ping Ling [ School of Computer Science and Technology, Jiangsu Normal University, Xuzhou 221116, 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