# From Data Fusion to Knowledge Fusion

arXiv: https://arxiv.org/abs/1503.00302 · HTML: https://mlsanity.com/paper/1503.00302/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2015.

## Built on by (most cited first, 10 of 10)

- [Large Language Models and Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2308.06374/) (2023)
- [Exploration of Attention Mechanism-Enhanced Deep Learning Models in the Mining of Medical Textual Data](https://mlsanity.com/paper/2406.00016/) (2024)
- [Generations of Knowledge Graphs: The Crazy Ideas and the Business Impact](https://mlsanity.com/paper/2308.14217/) (2023)
- [Completeness, Recall, and Negation in Open-World Knowledge Bases: A Survey](https://mlsanity.com/paper/2305.05403/) (2023)
- [KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering](https://mlsanity.com/paper/2509.04716/) (2025)
- [A Survey on State-of-the-art Techniques for Knowledge Graphs Construction and Challenges ahead](https://mlsanity.com/paper/2110.08012/) (2021)
- [Open Domain Knowledge Extraction for Knowledge Graphs](https://mlsanity.com/paper/2312.09424/) (2023)
- [Neurosymbolic Methods for Rule Mining](https://mlsanity.com/paper/2408.05773/) (2024)
- [Collaborative Knowledge Graph Fusion by Exploiting the Open Corpus](https://mlsanity.com/paper/2206.07472/) (2022)
- [A microservice architecture for real-time IoT data processing: A reusable Web of things approach for smart ports](https://mlsanity.com/paper/2401.15390/) (2024)

## Abstract (arXiv)

The task of em data fusion is to identify the true values of data items (eg, the true date of birth for em Tom Cruise) among multiple observed values drawn from different sources (eg, Web sites) of varying (and unknown) reliability. A recent surveyciteLDL+12 has provided a detailed comparison of various fusion methods on Deep Web data. In this paper, we study the applicability and limitations of different fusion techniques on a more challenging problem: em knowledge fusion. Knowledge fusion identifies true subject-predicate-object triples extracted by multiple information extractors from multiple information sources. These extractors perform the tasks of entity linkage and schema alignment, thus introducing an additional source of noise that is quite different from that traditionally considered in the data fusion literature, which only focuses on factual errors in the original sources. We adapt state-of-the-art data fusion techniques and apply them to a knowledge base with 1.6B unique knowledge triples extracted by 12 extractors from over 1B Web pages, which is three orders of magnitude larger than the data sets used in previous data fusion papers. We show great promise of the data fusion approaches in solving the knowledge fusion problem, and suggest interesting research directions through a detailed error analysis of the methods.