In document analysis, search engines look at whether they find the search term in important areas of the document - the title, the metadata, the heading tags, and the body of the text. They also attempt to automatically measure the quality of the document based on the document analysis, as well as many other factors.
Reliance on document analysis alone is not enough for today's search engines, so they also look at semantic connectivity. Semantic Connectivity refers to words or phrases that are commonly associated with one another.
For example, if you see the word aloha you associate with Hawaii, not Florida. Search engines actively build their own thesauruses and dictionaries to help them determine how certain terms and topics are related. By simply scanning their massive databases of content on the Web, they can use Fuzzy Set Theory and certain Equations to connect terms and start to understand web pages/sites more like a human does.
The professional SEO practitioner does not necessarily need to use semantic connectivity measurement tools to optimize websites, but for those advanced practitioners who seek every advantage, semantic connectivity measurements can help in each of the following sectors.
- Measuring which keyword phrases to target
- Measuring which keyword phrases to include on a page about a certain topic
- Measuring relationships of text on other high-ranking sites/pages
- Finding pages that provide "relevant" themed links
Although the source for this material is highly technical, SEO specialists need only know the principles to obtain valuable information. It is important to keep in mind that although the word of IR (Information Retrieval) incorporates hundreds of technical and often difficult-to-comprehend terms, these can be broken down and understood even by a SEO novice.
The following are some common types of searches in the IR (Information Retrieval) field.
Proximity Searches : A proximity search uses the order of the search phrase to find related documents. For example, when you search for "Sweet German Mustard" you are specifying only a precise proximity match. if the quotes are removed, the proximity of the search terms still matters to the search engine, but it will now show documents whose contents don't exactly match the order of the search phrase, such as Sweet Mustard - German.
Fuzzy Logic : Fuzzy logic technically refers to logic that is not categorically true or false. A common example is whether a day is sunny (e.g., if there is 50% cloud cover, is it still a sunny day?). One way engines use Fuzzy Logic is to detect and process misspellings.
Boolean Searches : Boolean searches use Boolean terms such as AND, OR, and NOT. This type of logic is used to expand or restrict which documents are returned in a search.
Term Weighting : Term weighting refers to the importance of the particular search term to the query. The idea is to weight particular terms more heavily than others to produce superior search results. For example, the word the in a query will receive very little weight in the selecting the results because it appears in nearly all English Language documents. There is nothing unique about it, and it does not help in document selection.
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