# ProbFuse: A Probabilistic Approach to Data Fusion

arXiv: https://arxiv.org/abs/1409.8518 · HTML: https://mlsanity.com/paper/1409.8518/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2014.

## Built on by (most cited first, 1 of 1)

- [Learning to Fuse LLMs with Ontology Rankers for Rare-Disease Diagnosis](https://mlsanity.com/paper/2609.02473/) (2026)

## Abstract (arXiv)

Data fusion is the combination of the results of independent searches on a document collection into one single output result set. It has been shown in the past that this can greatly improve retrieval effectiveness over that of the individual results. This paper presents probFuse, a probabilistic approach to data fusion. ProbFuse assumes that the performance of the individual input systems on a number of training queries is indicative of their future performance. The fused result set is based on probabilities of relevance calculated during this training process. Retrieval experiments using data from the TREC ad hoc collection demonstrate that probFuse achieves results superior to that of the popular CombMNZ fusion algorithm.