# DomainNet: Homograph Detection for Data Lake Disambiguation

arXiv: https://arxiv.org/abs/2103.09940 · HTML: https://mlsanity.com/paper/2103.09940/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2021.

## Built on by (most cited first, 9 of 9)

- [A Comprehensive Survey on Source-free Domain Adaptation](https://mlsanity.com/paper/2302.11803/) (2023)
- [SoundChoice: Grapheme-to-Phoneme Models with Semantic Disambiguation](https://mlsanity.com/paper/2207.13703/) (2022)
- [ED4: Explicit Data-level Debiasing for Deepfake Detection](https://mlsanity.com/paper/2408.06779/) (2024)
- [Cross Modal Data Discovery over Structured and Unstructured Data Lakes](https://mlsanity.com/paper/2306.00932/) (2023)
- [Learning an Efficient Optimizer via Hybrid-Policy Sub-Trajectory Balance](https://mlsanity.com/paper/2511.00543/) (2025)
- [Learning to Learn Weight Generation via Local Consistency Diffusion](https://mlsanity.com/paper/2502.01117/) (2025)
- [Generative Benchmark Creation for Table Union Search](https://mlsanity.com/paper/2308.03883/) (2023)
- [The Fellowship of the Authors: Disambiguating Names from Social Network Context](https://mlsanity.com/paper/2209.00133/) (2022)
- [Clustering-based Image-Text Graph Matching for Domain Generalization](https://mlsanity.com/paper/2310.02692/) (2023)

## Abstract (arXiv)

Modern data lakes are deeply heterogeneous in the vocabulary that is used to describe data. We study a problem of disambiguation in data lakes: how can we determine if a data value occurring more than once in the lake has different meanings and is therefore a homograph? While word and entity disambiguation have been well studied in computational linguistics, data management and data science, we show that data lakes provide a new opportunity for disambiguation of data values since they represent a massive network of interconnected values. We investigate to what extent this network can be used to disambiguate values. DomainNet uses network-centrality measures on a bipartite graph whose nodes represent values and attributes to determine, without supervision, if a value is a homograph. A thorough experimental evaluation demonstrates that state-of-the-art techniques in domain discovery cannot be re-purposed to compete with our method. Specifically, using a domain discovery method to identify homographs has a precision and a recall of 38% versus 69% with our method on a synthetic benchmark. By applying a network-centrality measure to our graph representation, DomainNet achieves a good separation between homographs and data values with a unique meaning. On a real data lake our top-200 precision is 89%.