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The keyword method has been very useful to-date, especially when searching within large structured databases. It allows users to search by date, location, category, etc., to come up with useful results.

The problem with keyword searching is the expanding mass of unstructured information we now have. Keyword searching has become inadequate and at times counter productive to finding the right information quickly and affordably.

Concept search is one method for solving this problem. My definition: The ability to extract structure from unstructured data.

Concept searching is just coming into the market, with players like Recommind, Autonomy and Collexis. As an emerging technology, the challenge is good implementation. Companies and firms are attacking this problem now, so I would expect this challenge to diminish over time.

Semantic search is truly Web 3.0. Sir Tim suggested this concept over a decade ago and now efforts are under way to make it a reality. My definition: Attach meaning to each piece of data. In practice this means describing each piece of information by its relationship to another piece. In the geek world this is referred to as “subject, predicate, object” and is defined with a standard called RDF (more on that in another post).

In fact in this environment the machine can discover knowledge. By connecting all the triples via their relationships, the machine will answer questions we never ask.

Semantic search currently lives mostly in the minds of geeks and venture capitalists (with some exceptions).