Advanced Schema Markup for semantic enrichment with Python Flask and Spacy (Super-advanced SEO)
I coded a simple Python Flask application to perform semantic enrichment through advanced schema markup implementation by using entity-linking to Wikipedia (wikification) to create a JSON object, ready for publishing as structured data. This is an advanced schema markup (structured data) implementation compared to classic approaches in implementing schema (finding schema types and filling them with information). My Linkedin: / emilijagjorgjevska
I base my approach on Tim Berner's Lee (the founder of the WWW who worked at CERN) 5-star rating system.
The process is divided into 3 steps:
1. Determining entities and resolving between entity candidates.
2. Linking the entity to the knowledge repository (Wikipedia in this case, but it can be also a knowledge graph like DBpedia, Wikidata...).
3. Creating a JSON object that mentions all detected entities and includes their URLs (URI).
💡 This is an advanced implementation of structured data where usually you need to manually determine all the mentioned entities and their types and fill the information. This implementation is usually not available for free online, but I've coded it myself.
We go beyond that by combining the classical approach but also using automated linking to provide more context to engines and users about which topics, concepts, and entities we care about.
Extremely useful for publishers but also any type of SEOs and businesses that care to go beyond the classic schema markup approach.