OntoSearch

San Jose, CA–Yebol is a general “decision” search engine that has developed a semantic search platform. Yebol’s artificial/human-infused intelligence algorithms automatically cluster and categorize search results, web sites, pages and contents that it then presents in a visually indexed format that is designed to be more alligned with initial human intent. Yebol applies association, ranking and clustering algorithms to analyze related keywords, categories and web pages. Yebol presents as one of its goals the creation of a unique “homepage look” for every possible search term.

Yebol says it aims for absolute relevance and eliminating the need for refined, secondary, or advanced search steps, as currently required by Google, Yahoo! and others. Yebol sees the Future of Search as being very different than today’s current Pay-Per-Click dominated model. Its public beta version launched July 27, 2009, at which time Yebol announced that it covered in excess of 10 million search terms with its current knowledge-based search/organized results format. In fact, Yebol says its main distinction is its verticality (versus the horizontal nature of today’s major indexing search engines). Like all engines, Yebol uses “a recursive procedure in which an automatic problem solver seeks a solution by iteratively exploring sequences of possible alternatives.”

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Smartlogic have announced the release of Semaphore 3.1, the latest release of the semantic platform. The new version with its revolutionary information classification capabilities increases the ability of diverse audiences to quickly find information, promoting “self service” and cutting costs.

Smartlogic’s CEO, Jeremy Bentley, explains the concept of “self service” as provided by semantic search:

“Self service involves the 3 A’s – audience, author, and algorithm. Say the author is a world-class vet, working for the Department of Agriculture, who has written a research paper on BSE. She writes the document in ‘vet speak’ and uses the terminology she’s used to. The audience in this case is a farmer, who’s worried about mad cow disease. But when he types “mad cow” into the Department of Agriculture’s website, the research paper doesn’t show up. That’s because the website’s search algorithm is too simple and has no understanding of meaning, and looks only for the words “mad” and “cow” in the documents on its system.

We could take this a step further, by assuming the farmer then phones up the Department to ask for advice about mad cow disease. The telephone operator has a third vocabulary, and she might search for ‘MCD’ on the Department’s intranet, again, turning up nothing about ‘mad cow disease’ or ‘BSE’. She’s trained to try to help the farmer answer his query, so she puts him through to the – very expensive – vet.

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We are so used to using search engines in our everyday lives now that everybody wants to be at the top of the search engines for certain keywords. Hence the search process has become ever more complicated and it can be a struggle for technology to keep up. You put in your search parameters and when the search results are returned you may find that nothing on the first page or two is even relevant to what you want because there is not sufficient context.

Semantic search gives context and meaning to keywords and aims to understand how human beings really approach the search process.  With the study of human behaviour, we now know that people search differently depending upon their purpose, whether that’s information-gathering, finding a document or fact or just gaining a feel for a topic. Therefore taxonomy classification is a very important aid to semantic search.

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