Exploring AI and ML Approaches for Schema Matching

Curious if these tools work well across different languages or just English schemas.

I read about some tools using deep learning to understand semantic similarity between schema elements. Has anyone tested these?

I find that schema matching tools sometimes struggle with abbreviations or typos in schema labels. Any tips?

Anyone using these tools in production? Curious about real-world challenges beyond initial testing.

Anyone tried integrating these tools with data catalog platforms?

For those working with big data, do these AI-driven matchers scale well? Or do they choke on huge schemas?

I think a big challenge is getting good feature engineering for ML models in schema matching. Anyone found good approaches?

I've been using some ML-driven schema matchers recently, and honestly, the improvement over manual matching is huge. It saves tons of time, but sometimes the matching accuracy isn't perfect especially with complex schemas.

Are there any good tutorials or courses on ML for schema matching?

Been experimenting with transfer learning for schema matching, results look promising so far.

Are there any benchmarks comparing different AI-powered schema matchers?

Is there any community or forum to keep updated on new AI-driven schema matching tech?

I guess the future is hybrid systems that combine AI with human feedback loops for better matching.

Sometimes the AI tools are too slow, anyone knows tools optimized for speed?

Does anyone know if there are open-source options that perform well? Most of the commercial tools are kinda pricey for small teams.

I usually combine rule-based and ML methods to get the best accuracy. Pure ML can be too black box at times.

I wish there was a way to easily visualize how the schema matching decisions are made. Explainability is so important.