Search Engines and Algorithms: Semantic Search - Contextual Search
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Contextual Search
When you are in conversation with someone, they understand the slight differences in meanings of certain words you are saying, based upon the context of the conversation, or the other words spoken before and after. Contextual Search attempts to mimic human conversation by getting the gist of the words around the particular search term. It is in Contextual Search that Crystal Semantics’ Textonomy engine places its focus. This concept is similar in part to Fuzzy Searching, but different in the way that it takes the context of a web page as a whole to deliver relevant content, and not just the word itself.
I had mentioned before that currently, this search engine is only being used for advertising purposes, and the way it works is that it will analyze the content of web pages and return the subject of the page. “It is simple to integrate and in a matter of minutes, our clients can enjoy the benefits of enhancing their ad click-through rates and delivering relevant content,” managing director Ian Saunders states in a press release in late September, 2005. Saunders adds, “It is apparent that while Contextual Advertising is extremely popular, the current solutions are far too simplistic in their approach resulting in irrelevant ads and other content. Crystal Semantics technology enables marketers to deliver any content in true context and without forcing the end user to compromise on their viewing experience or privacy.”
Current Contextual Advertising merely presents ads based on the presence of particular keywords on a page. The obvious problem with this type of system is that keywords could easily be taken out of context, and cause the system to deliver ads that are irrelevant. The consequences of this problem could easily be seen; for example, a company selling a new kitchen knife could have their ad appear next to a news story about a stabbing death, which could be devastating to ad revenue. So with Textonomy Advance, Crystal Semantics goes beyond the prevalent concept of presenting ads based on keywords alone by analyzing the content of a web page, and drawing upon human understanding of language, or linguistics, to determine the relevant ads and the appropriate audience.
“Search engines, advertisers and online retailers must recognize that most English words have a variety of different meanings and contexts, not to mention various dialects and slang terms, that need to be accounted for in order to compile and deliver accurate results," said professor Crystal. "Unfortunately, search techniques in wide use today fail to adequately take these subtleties into account; resulting in clutter for end users and inaccurate targeting by advertisers.”
Relevant results still seem to be a big problem for search engines. It is the number one reason people fail to find information they need on the internet, and is also the top reason Pay-per-click advertising can be ineffectual, and result in unqualified traffic. Search engines have made good use of Boolean, Fuzzy, and even Wildcard searches, but even still, there is only so much these technologies can accomplish on their own. It is within Contextual Search that I feel the next step for search engines to take resides. I will be watching this closely, in hopes that similar concepts will be incorporated into a public search engine very soon.
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