Knowledge Vault
Also called: Google Knowledge Vault
Knowledge Vault was a 2014 Google research project that automatically extracted facts from the web (free text, HTML tables, page markup) and fused them with existing knowledge bases into a probabilistic store, giving each subject-predicate-object fact a calibrated confidence score.
Google presented Knowledge Vault at KDD 2014 (Dong et al.). It has three parts: extractors that pull candidate facts from the web, graph-based priors that estimate how likely a fact is given what a knowledge base already contains, and a knowledge fusion step that merges both into one probability per fact.
Facts came from four source types: free text (TXT), HTML DOM trees (DOM), HTML tables (TBL), and human annotations using schema.org and microformats (ANO). Each fact is stored as a subject-predicate-object triple, for example (Tim Berners-Lee, invented, World Wide Web), with a calibrated probability attached.
The reported scale: about 1.6 billion candidate triples across 4,469 relation types and 1,100 entity types, of which 324M scored 0.7 confidence or higher and 271M scored 0.9 or higher. Roughly a third of those 271M confident facts were not already in Freebase, so the method genuinely extended the graph rather than just re-scoring what was known.
Was it ever shipped?
Not under that name. Google told the press in 2014 that Knowledge Vault “was a research paper (May 2014) and is not an active Google product in development.” The label faded after 2014 and 2015, but the core idea (corroborate a claim across many independent sources, then attach a confidence score) is now standard machinery behind entity understanding, Knowledge Panels, and how AI answer engines decide which facts about you are safe to repeat.
How it affects your traffic
Knowledge Vault is the clearest published blueprint for how search systems decide which facts about your brand to trust: they look for the same claim across many independent sources, then attach a confidence score. If your key facts (who you are, what you do, founders, locations, products) are stated consistently, marked up with schema.org, and echoed by authoritative third-party pages, machines score them as high-confidence and become more willing to surface them in Knowledge Panels, AI Overviews, and LLM answers. Contradictory or thinly-sourced facts get low scores and get left out. Building that corroborated, machine-readable entity footprint is the core of our AI SEO work.
Get AI SEO that moves the needle
We turn terms like this into ranked pages and qualified pipeline. Start with a free Initial SEO Strategy.