Summary of Smutf: Schema Matching Using Generative Tags and Hybrid Features, by Yu Zhang et al.
SMUTF: Schema Matching Using Generative Tags and Hybrid Features
by Yu Zhang, Mei Di, Haozheng Luo, Chenwei Xu, Richard Tzong-Han Tsai
First submitted to arxiv on: 22 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel approach to large-scale tabular data schema matching (SM) called SMUTF. The method leverages a combination of rule-based feature engineering, pre-trained language models, and generative large language models to enable effective cross-domain matching. By deploying “generative tags” for each data column, inspired by the Humanitarian Exchange Language, SMUTF showcases its versatility in working seamlessly with any pre-existing pre-trained embeddings, classification methods, and generative models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to match big datasets. It combines different techniques like language models and rules-based engineering to make this matching work. The goal is to be able to match data from different places or sources. The system does well with many types of existing language models and classification methods. |
Keywords
» Artificial intelligence » Classification » Feature engineering
