A Probabilistic Model for Estimating Realvalued Truth from Conflicting Sources
One important task in data integration is to identify truth from noisy and conflicting data records collected from multiple sources, i.e., the truth finding problem. Previously, several methods have been proposed to solve this problem by simultaneously learning the quality of sources and the truth. However, all those methods are mainly designed for handling categorical data but not numerical data. While in practice, numerical data is not only ubiquitous but also of high value, e.g. price, weather, census, polls, economic statistics, etc.