MTruths：An Approach of Multiple Truths Finding from Web Information

Web has been a massive information repository on which information is scattered in different data sources. It is common that different data sources provide conflicting information for the same entity. It is called the truth finding problem that how to find the truths from conflicting information. According to the number of attribute values, object attributes can be divided into two categories: single-valued attributes and multiple-valued attributes. Most of existing truth finding work is designed for truth finding on single-valued attributes. In this paper, a method called MTruths is proposed to resolve truth finding problem for multiple-valued attributes. We model the problem using an optimization problem. The objective is to maximize the total weight similarity between the truths and observations provided by data sources. In truth finding process, two methods are proposed to find the optimal solution: an enumeration algorithm and a greedy algorithm. Experiments on two real data sets show that the correctness of our approache and the efficiency of the greedy algorithm outperform the existing state-of-the-art techniques.