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Editor's note: we offer our long-term sponsors the opportunity to write 'Sponsor Posts' and tell their story. These posts are clearly marked as written by sponsors, but we also want them to be useful and interesting to our readers. We hope you like the posts and we encourage you to support our sponsors by trying out their products. This one is by Hakia, one of the participants in the recent 2009 Semantic Technology Conference.

Participants in the 2009 Semantic Technology Conference walked away considering fundamental questions about what is and isn't semantic technology. The relevance of this post's title will hopefully become clear by the end to those of you mischievous readers who may have stumbled upon it with other ideas. The conference was a great and well-organized affair in San Jose, California. One of the highlights was the Semantic Search Keynote panel, with all of the major players on stage (Ask, Bing, Google, Hakia, TrueKnowledge, and Yahoo!), as seen in the picture below.

It's been more than ten years since Tim Berners-Lee first spoke about the semantic web and computers indexing all web-based data. He said, "The day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines. The 'intelligent agents' people have touted for ages will finally materialize." Since then a handful of companies have attempted to tackle the issue of machine-based indexing and language interpretation. None of them are perfect. Below are 6 unique approaches to semantic data collection.

Perhaps the biggest problem with natural language search is that it's incredibly difficult to try and automate machine-assigned ontologies. Essentially, machines just don't get it.

This is precisely the reason why Canadian technologist Bruce Johnson switched his focus from semantic tagging to a new style of search. Says Johnson, "Machines don't really deconstruct language well. They miss so many of the ambiguities and they often don't pick up on synonyms." As a result, Johnson's Semanti was built in the belief that humans are best at determining search relevancy. ReadWriteWeb spoke to Johnson, about how his start up, differs from some of the semantic web's more-recognized players like Hakia and Powerset.

Most semantic search services are natural language search engines; however, Semanti employs a system of personal bookmarks, a drop-down menu with multiple definitions, and search recommendations pulled from Facebook friends. Semanti actually increases relevancy by introducing human eyes and opinions into the search process.

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