The document discusses upcoming features for semantic tools including improved APIs, bulk content importing, activity streams, and expanded linguistic analysis capabilities such as transliterations, word parsing, word sense disambiguation, and frame semantics. It also references databases like Wikipedia, DBPedia, and Freebase that provide semantic content.
We build linguistic tools in order to improve language learning.
As you all know this has changed, with now pro-sumers generating vast amounts of often irrelevant information. Social-networks are a huge catalyst to this change. Also, bots and machines are playing active role in the contribution of new data.
One of the reasons why we’re all in this room together is because of the vast amount of digital information. One of the goals of Recommender systems is to provide more targetted delivery of information. So how did we get here? Let’s do a short recap.
All apps are becoming semantic.
Black box
Cannot enhance semantic functionality (e.g. adding new forms of personalization)
Bottom up
Start with Linguistics
Provide layers of more and more semantic capability
More potential for mashups (e.g. Google’s language detector)
We work together with an American research institute, Novamente, to accomplish these things.
Novamente has the goal of building an Artificial General Intelligence and work with iKnow! to solve one of the AGI’s components, Natural Language Parsing.