A Holistic Solution for Duplicate Entity Identification in Deep Web Data Integration

Liu, Wei; Meng, Xiaofeng

doi:10.1109/skg.2010.38

Cited by 3 publications

(1 citation statement)

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“…In addition, the cost of preparing the training set for n Web databases is higher than that for two Web databases. A holistic solution to address the new challenges posed by deep Web, whose goal is to build one duplicate entity matcher over multiple Web databases is proposed in [41].…”

Section: Ontology Assisted Deep Web Searchmentioning

confidence: 99%

Survey of Techniques for Deep Web Source Selection and Surfacing the Hidden Web Content

Khurana¹,

Chandak²

2016

ijacsa

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Abstract-Large and continuously growing dynamic web content has created new opportunities for large-scale data analysis in the recent years. There is huge amount of information that the traditional web crawlers cannot access, since they use link analysis technique by which only the surface web can be accessed. Traditional search engine crawlers require the web pages to be linked to other pages via hyperlinks causing large amount of web data to be hidden from the crawlers. Enormous data is available in deep web that can be useful to gain new insight for various domains, creating need to access the information from the deep web by developing efficient techniques. As the amount of Web content grows rapidly, the types of data sources are proliferating, which often provide heterogeneous data. So we need to select Deep Web Data sources that can be used by the integration systems. The paper discusses various techniques that can be used to surface the deep web information and techniques for Deep Web Source Selection.

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Section: Ontology Assisted Deep Web Searchmentioning

confidence: 99%