Multi-feature and DAG-Based Multi-tree Matching Algorithm for Automatic Web Data Mining

Web data extraction has received considerable attention and study in recent decades. To improve efficiency, many automatic Web data record mining approaches have been proposed. Among these approaches, each complete approach involves data record identification as well as data item alignment. In this...

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Bibliographic Details
Published in:	2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) Vol. 1; pp. 118 - 125
Main Authors:	Shengsheng Shi, Chengfei Liu, Chunfeng Yuan, Yihua Huang
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.08.2014
Subjects:	Accuracy data item alignment Data mining directed acyclic graph Educational institutions HTML Manuals multi-tree matching multiple features Visualization Web data mining Web pages
Online Access:	Get full text
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Summary:	Web data extraction has received considerable attention and study in recent decades. To improve efficiency, many automatic Web data record mining approaches have been proposed. Among these approaches, each complete approach involves data record identification as well as data item alignment. In this paper, we propose a new multi-feature and DAG (Directed Acyclic Graph) based multi-tree matching algorithm for automatic data item alignment. Our algorithm improves alignment accuracy in two aspects. First, it combines multiple features to cope with the limitations of existing algorithms, second, it employs a DAG-based method to deduce the global alignment of data items with high accuracy. Experimental results show that our algorithm outperforms state-of-the-art data item alignment algorithms.
DOI:	10.1109/WI-IAT.2014.24