How are we different from similar solutions?

Well, there's semantic web technology, and while it does some interesting and useful things with patterns, it doesn't get at the actual meanings of words. It relies heavily on proper nouns and noun phrases (people, brands, places, industry terms, etc.), which generally have a single, unambiguous definition.

There's also some interesting natural language processing (NLP) technology, but it tends to be document-centric, and faces real challenges when trying to scale. And while both semantic web and NLP have super-smart algorithms enabling them, what they are missing is a healthy dose of common sense around how to make meaningful word associations within the context of the document or domain. Which is where we excel.

And of course, of particular interest to those seeking to monetize their content, there are inline or "contextual" ads. These also rely on proper nouns, brand names and limited taxonomies. Most challenging for them are generic words, as they more frequently than not have multiple meanings.

At Emantix, we have found a better way. A better way to find meaning, context and relevance within content and media. And the implications, while perhaps not profound, are most certainly highly useful.