#Babelnet
Live, measured metrics for the hashtag #Babelnet from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #babelnet
This #name is available to claim. It becomes your portal on the open agent web: this very page, a keyword you rank for by an open public stake, and a verifiable identity for AI agents. Nobody else sells a page like this for every #name.
Day-by-day usage
measured · mastodon.online (Mastodon public tags API) · fetched 2026-09-26 14:25 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · mastodon.online (Mastodon public search API) · fetched 2026-09-26 14:25 UTCNo related tags with measured usage found for #babelnet.
Live pulse
measured · mastodon.online (Mastodon tag timeline) · fetched 2026-09-26 14:25 UTCEverything below is measured over the latest 2 public posts (spanning ~16584 hours).
Posting hours (UTC)
Languages: English (2)
Avg boosts / post: 0
Used together with
Top of the latest posts
R to @ERC_Research: Next Monday 18 March: hear from Roberto Navigli - ERC grantee, Professor @SapienzaRoma & creator of #Babelnet, a global reference for multilingual #translation! How? Register for #RIWeek2024👉https://eu.eventscloud.c
On #worldbookday2022 why not read a book in a foreign language? Or a good translation? Keep in mind ERC grantee Roberto Navigli's work on #Babelnet, a global reference for multilingual #translation! 📗 http://bit.ly/RobertoNavigli #AI @RNav
What “babelnet” means
WikipediaBabelNet is a multilingual lexical-semantic knowledge graph, ontology and encyclopedic dictionary developed at the NLP group of the Sapienza University of Rome under the supervision of Roberto Navigli. BabelNet was automatically created by linking Wikipedia to the most popular computational lexicon of the English language, WordNet. The integration is done using an automatic mapping and by filling in lexical gaps in resource-poor languages by using statistical machine translation. The result is a
“BabelNet” on Wikipedia (CC BY-SA) →#babelnet across platforms
every network with a public tag surfaceFollow #babelnet straight to each platform’s own tag page. Where a platform publishes open data we measure it above; the rest lock their numbers behind paid APIs, so we link rather than guess.
Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/babelnet