Truth Finding on the Deep Web: Is the Problem Solved?

TL;DR

This paper investigates the truthfulness of Deep Web data in stock and flight domains, revealing significant inconsistencies and low accuracy, and evaluates data fusion methods to address these issues.

Contribution

It provides an empirical analysis of data quality in the Deep Web and assesses the effectiveness of current data fusion techniques in resolving conflicts.

Findings

Large inconsistencies found in Deep Web data sources

Some sources exhibit quite low accuracy

State-of-the-art data fusion methods have limitations

Abstract

The amount of useful information available on the Web has been growing at a dramatic pace in recent years and people rely more and more on the Web to fulfill their information needs. In this paper, we study truthfulness of Deep Web data in two domains where we believed data are fairly clean and data quality is important to people's lives: {\em Stock} and {\em Flight}. To our surprise, we observed a large amount of inconsistency on data from different sources and also some sources with quite low accuracy. We further applied on these two data sets state-of-the-art {\em data fusion} methods that aim at resolving conflicts and finding the truth, analyzed their strengths and limitations, and suggested promising research directions. We wish our study can increase awareness of the seriousness of conflicting data on the Web and in turn inspire more research in our community to tackle this problem.