Structured Data Extraction: HTML DOM Parsing, Schema.org & JSON-LD Synthesis

Extracting structured knowledge from messy web markup enables search engines to power rich answer snippets, knowledge panels, and entity graph links.

1. Parsing JSON-LD & Microdata Graphs

Crawlers parse embedded <script type="application/ld+json"> blocks and RDFa/Microdata attributes, deserializing them into normalized Schema.org entity graphs (e.g., Article, Product, Organization, FAQPage).

2. Heuristic DOM Main-Content Extraction

For pages lacking structured schema markup, machine learning readability algorithms evaluate DOM tree node density, text-to-tag ratios, and semantic HTML5 container elements (<article>, <main>) to strip navigation headers, sidebars, and advertising blocks.

3. Knowledge Graph Entity Linking

Extracted entities are disambiguated against authoritative knowledge bases (Wikidata, Wikipedia) to create interconnected entity graphs that enrich search query understanding.