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Other types of links may also be used on the web, many of which pass no ranking or spidering value due to their use of re-direct, Javascript or other technologies. A link that does not have the classic text format, be it image or text, should be generally considered not to pass link value via the search engines (although in rare instances, engines may attempt to follow these more complex style links). The AdSense Marketing
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In this example, the redirect used scrambles the URL by writing it backwards, but unscrambles it later with a script and sends the visitor to the site. It can be assumed that this passes no search engine link value.

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This sample shows the very simple piece of Javascript code that calls a function referenced in the document to pull up a specified page. Creative uses of Javascript like this can also be assumed to pass no link value to a search engine. Free the AdSense E-mail Marketing
It’s important to understand that based on a link’s anatomy, search engines can (or cannot) interpret and us the data therein. Whereas the right sort of links can provide great value, the wrong sort will be virtually useless (for search ranking purposes). More detailed information on links is available at this resource – anatomy and deployment of links.

Keywords and Queries
Search engines rely on the terms queried by users to determine which results to put through their algorithms, order and return to the user. But, rather than simply recognizing and retrieving exact matches for query terms, search engines use their knowledge of semantics (the science of language) to construct intelligent matching for queries. An example might be a search for loan providers that also returned results that did not contain that specific phrase, but instead had the term lenders.
The engines collect data based on the frequency of use of terms and the co-occurrence of words and phrases throughout the web. If certain terms or phrases are often found together on pages or sites, search engines can construct intelligent theories about their relationships. Mining semantic data through the incredible corpus that is the Internet has given search engines some of the most accurate data about word ontologies and the connections between words ever assembled artificially. This immense knowledge of language and its usage gives them the ability to determine which pages in a site are topically related, what the topic of a page or site is, how the link structure of the web divides into topical communties and much, much more.
Search engines’ growing artificial intelligence on the subject of language means that queries will increasingly return more intelligent, evolved results. This heavy investment in the field of natural language processing (NLP) will help to achieve greater understanding of the meaning and intent behind their users’ queries. Over the long term, users can expect the results of this work to produce increased relevancy in the SERPs (Search Engine Results Pages) and more accurate guesses from the engines as to the intent of a user’s queries.

Sorting the Wheat from the Chaff
In the classic world of Information Retrieval, when no commercial interests existed in the databases, very simplistic algorithms could be used to return high quality results. On the world wide web, however, the opposite is true. Commercial interests in the SERPs are a constant issue for modern search engines. With every new focus on quality control and growth in relevance metrics, there are thousands of individuals (many in the field of SEO) dedicated to manipulating these metrics in order to control the SERPs, typically by aiming to list their sites/pages first.

Grab practical hints in the sphere of free traffic – your personal guide.