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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Vydáno v:2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) Ročník 1; s. 118 - 125
Hlavní autoři: Shengsheng Shi, Chengfei Liu, Chunfeng Yuan, Yihua Huang
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.08.2014
Témata:
Accuracy
data item alignment
Data mining
directed acyclic graph
Educational institutions
HTML
Manuals
multi-tree matching
multiple features
Visualization
Web data mining
Web pages
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Shrnutí: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