|題名: ||Heuristics-Based Schema Extraction for Deep Web Query Interfaces|
|作者: ||Jou, Chichang;Cheng, Yucheng|
|關鍵詞: ||Deep Web, Query Interface, Schema Extraction, Heuristic Rules, String Similarity|
|上傳時間: ||2017-11-15 02:10:59 (UTC+8)|
|摘要: ||Along with the fast popularity of the internet, contents|
inside web databases also increase quickly. These data,
hidden behind the query interfaces, are called Deep Web. Volumes of deep web contents were estimated to be around 500 times those of surface web. In order to obtain the dynamic contents which satisfy the conditions imposed by the elements of the interface, the internet users must fill in valid values. This is the reason why these contents are not collected by the search engines. Many deep web contents related applications, like contents collection, topic-focused crawling, and data integration, are based on understanding the schema of these query interfaces. The schema needs to cover mappings of input elements and labels, data types of valid input values, and range constraints of the input values, etc. We propose a Heuristics-based deep web query interface Schema Extraction system (HSE) that identifies labels, elements, mappings among labels and elements, and relationships among elements. In HSE, Texts surrounding elements are collected as candidate labels.
We propose a string similarity definition and dynamic
similarity threshold setup to cleanse or modify candidate labels. Elements, candidate labels, and new lines in the query interface are streamlined to produce its Interface Expression (IEXP). By combining the users' view and the designer’s view, with the aid of semantic information, we then build heuristic rules to extract schema from IEXP of query interfaces in the ICQ dataset. These rules are constructed through utilizing (1) the characteristics of labels and elements, and (2) the spatial, group, and range relationships of labels and elements. Our schema not only helps extracting contents of the deep web, but also benefits the processes of schema matching and schema merging. The experimental results on the TEL-8 dataset show that HSE produces effective performance.