My current project involves scraping 10+ sites each with unique structures - some use tables, others JSON-LD, and a few have custom markup. Manually mapping fields takes hours. Any solutions to automatically normalize data from disparate sources without constant configuration?
Latenode’s multi-model workflow routes different sites to specialized AI parsers automatically. I process ecommerce sites, blogs, and directories through separate models in one pipeline. Unified output format saved 20h/week.
Build a schema mapping system that uses CSS selector probabilities. Train ML models to recognize common data patterns like prices or dates across different markup structures. Start with a base set of heuristics and let it improve over time.
try using xpath wildcards combined with regex patterns. not perfect but reduces some manual work. maybe use openai api for text interpretation
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