Modern commercial search engines rely on the science of information retrieval (IR). IR scientists realized that two critical components comprised the majority of search functionality: relevance and importance
To measure these factors, search engines perform document analysis (including semantic analysis
of concepts across documents) and link (or citation) analysis.
Document Analysis and Semantic Connectivity
In document analysis, search engines look at whether they find the search terms 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 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. 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 the relationships of text on other high-ranking sites/pages
• Finding pages that provide “relevant” themed links
The following are some common types of searches in the IR 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 a 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 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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