VIQI: A New Approach for Visual Interpretation of Deep Web Query Interfaces

TL;DR

This paper introduces VIQI, a novel method for automatically interpreting deep web query interfaces to enable unified querying across multiple web services, improving accessibility and efficiency.

Contribution

VIQI is a new approach that emulates user interpretation to extract queries from deep web interfaces, facilitating cross-service querying.

Findings

Proved effective on two standard datasets.

Outperformed existing methods in query interpretation accuracy.

Enhanced user access to deep web databases.

Abstract

Deep Web databases contain more than 90% of pertinent information of the Web. Despite their importance, users don't profit of this treasury. Many deep web services are offering competitive services in term of prices, quality of service, and facilities. As the number of services is growing rapidly, users have difficulty to ask many web services in the same time. In this paper, we imagine a system where users have the possibility to formulate one query using one query interface and then the system translates query to the rest of query interfaces. However, interfaces are created by designers in order to be interpreted visually by users, machines can not interpret query from a given interface. We propose a new approach which emulates capacity of interpretation of users and extracts query from deep web query interfaces. Our approach has proved good performances on two standard datasets.