In the era of big data, large?scale data are often contributed by numerous data sources and used by many data?driven applications. Because of different trustworthiness of sources, different sources often produce data conflicts, making it difficult to determine which information is true. In recent years, truth finding has become a research hotspot by finding the most credibility values from multiple sources. The current truth finding methods usually assume that the entity has only one truth, while in reality, entities may have multiple true values. In this paper, we present an approach for multi?truth finding, which transforms the multi?truth finding into an optimization problem. In so doing, we select the values with the highest credibility as truths of entities. We also propose an asymmetric approach to compute support between values and incorporate influences of similar values to measure value credibility for better truth finding. Experiments on several data sets show that the effectiveness of our algorithm outperform the existing state?of?the?art techniques.