BY aarrieta
We’ve been talking about Semantic Web for more than six years, but it hasn’t been until recently that the first start ups using this concept and paradigm have appeared to do something practical that generates a tangible benefit for which somebody is willing to pay.
The current state of the sub-industry is similar to the one that Behavioral Targeting had a couple of years ago, when it seemed a promise that everybody was talking about but there weren’t any large revenues behind it. Exactly the same it is what has been happening with companies of natural language processing, semantic optimization and other semantic analysis.
Let’s review a little this concept, according to Wikipedia.
The Semantic Web is an evolving extension of the World Wide Web in which the semantics of information and services on the web is defined, making it possible for the web to understand and satisfy the requests of people and machines to use the web content.[1][2] It derives from World Wide Web Consortium director Sir Tim Berners-Lee‘s vision of the Web as a universal medium for data, information, and knowledge exchange.[3]
In practical terms, what they are trying to do with this technology is something that human beings do all the time, which is through the context of a word to be able to value what I am talking about, when for example I use metaphors and to recognize the subjacent values. This allow for example to know if I am talking in a good way of a brand or not or of its features to carry out a market research, or to know if the ad campaign that a brand is running is not neutralized by a negative comment that a user is doing in the comment field of a media.
How do we do that? Creating a structure of relations between each Word, that allows evaluation of the context for each industry/sector that share the same word values (ontology) and not by doing a keyword counting that would be the same as counting how many times the word good is next to a brand.
Based on this processing of natural language services can be created:
- Brand/image monitoring
- Advertising optimization
- Preferences/interests analysis
- Relating this interests and contents to behavioral targeting profiles
- Thematic assessments
- – Relating this theme based on a value with the associative ad (contextual advertising 2.0)
Giving a clearer example of the last point, if I run a campaign of an airline, I would advertise in all places where there is a good Word on planes and that is not associated to safety issues, accidents or bad plain food. Or going a little further, promoting a new menu or service only in the sites where they are talking bad about food on other airlines.
Here is a video of the company Amplify that specializes on processing natural language: