Semantic networks the secret sauce
Expert System believes it can go the extra mile because it has a semantic network a lexical database that provides a knowledge representation of word definitions and their relationships. In essence it has poured Websters dictionary into an in-memory database comprising 350,000 words and 2.8 million relationships.
Importantly, Expert Systems semantic network also focuses on common words. Thats different from most ontological approaches that concern themselves with wrapping meaning and context around specialised (often scientific) content and skip common words that comprise 90% of all content.
Riding the Web 3.0 wave
Expert System isnt the only company eyeing the semantic web (currently dubbed Web 3.0). Other semantic start-ups include Powerset, Yedda, Trovix and Hakia. Awareness of semantic search rose this summer when Microsoft picked up San Francisco-based Powerset. Interestingly, Microsoft followed that up with its second semantic buy Zoomix, a data quality provider that has baked semantic self-matching methods into its software. These moves are important. Up to now semantic search has been a market where there has been a lot of theoretical hype but little real substance or proof that it works better than current search technology.
Semantic networks are tricky to build and not all are equal. However, its unlikely that Expert System and other semantic technologies will ever be able to provide 100% precision in their analysis and results. Moreover there are still question marks over potentially sticky performance issues with semantic searches that eat up more processing cycles.
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