# Data Fusion: Resolving Conflicts from Multiple Sources

arXiv: https://arxiv.org/abs/1503.00310 · HTML: https://mlsanity.com/paper/1503.00310/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2015.

## Built on by (most cited first, 4 of 4)

- [Construction of Knowledge Graphs: State and Challenges](https://mlsanity.com/paper/2302.11509/) (2023)
- [Swift Markov Logic for Probabilistic Reasoning on Knowledge Graphs](https://mlsanity.com/paper/2210.00283/) (2022)
- [SourceSplice: Source Selection for Machine Learning Tasks](https://mlsanity.com/paper/2507.22186/) (2025)
- [TRACE: State-Aware Query Processing over Temporal Evidence Graphs for Conversational Data](https://mlsanity.com/paper/2607.00339/) (2026)

## Abstract (arXiv)

Many data management applications, such as setting up Web portals, managing enterprise data, managing community data, and sharing scientific data, require integrating data from multiple sources. Each of these sources provides a set of values and different sources can often provide conflicting values. To present quality data to users, it is critical to resolve conflicts and discover values that reflect the real world; this task is called em data fusion. This paper describes a novel approach that finds true values from conflicting information when there are a large number of sources, among which some may copy from others. We present a case study on real-world data showing that the described algorithm can significantly improve accuracy of truth discovery and is scalable when there are a large number of data sources.