Heuristics-Based Schema Extraction for Deep Web Query Interfaces

Along with the popularity of the internet, contents inside web databases also increase quickly. These data, hidden behind the query interfaces, are called deep web. These contents normally 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. 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 function and dynamic similarity threshold setup to cleanse candidate labels. In HSE, elements, candidate labels, and new lines in the query interface are streamlined to produce its Interface Expression (IEXP). By combining the user's view and the designer's view, with the aid of semantic information, we 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.