A Semantic Search Engine could be described as an agent that allows users to search at the topic level. Once a topic of interest is identified, then drill down to the specific document. Semantics is been applied these days by search engines in a few closely related fields. A possible way, is by using statistical analysis to retrieve semantic matches to a query. With this approach, they are capable of providing relational results based on the topics and keywords given by the user, and help them in the process of narrowing the search to be more specific.



To outline the process, there are basically 3 steps to consider:

(1) User enters a search query, with a given number of words describing with the topic of their search is,

(2) The engine returns results matching the words user entered, plus adding suggested alternatives

(3) The second step repeats itself untill the results returned are satisfactory to the user.

Automated semantic tagging is also possible, when the topic of a given page is well known. In this application, dynamic categorization of topical information about a query is implemented without any human intervention. These categorizations could take place in real time or be maintained incrementally over a time period. This is possible because there are available cognitive objects and conceptual models to identify sense information. Then these could be precisely weighted and measured, to determine the relevance among variable words forms and grammars in multiple corpus of content. You could have a travel site wanting to optimize for "discount tickets" when actually your visitors are searching for "cheap tickets". Another thing is the timeline to achieve results, like in the short, mid, or long-term. No unrealistic expectations here. One way I like to approach this is to optimize by levels. For example, I will choose 3 levels of difficulty and select the according keywords.

As I achieve the easiest level, I move on to the next and so on. Results differ per competition volume and markets, but overall results prove it does work.

On-Page Factors.

The most important elements in the page are the Title, Headings within the copy and keyword focused content. Keeping your code compliant is also beneficial. On-page optimization a very important part of the process, but it is almost indirectly proportional with the competition levels for the given target keywords. So it is highly scored into rankings when present within low to medium levels, but less significant among high competitive terms.

Google's application of semantics goes beyond Adsense. Relational Search is now evident when returning results from their index. This takes place if you get search results alternatives based upon an initial query you performed or after pulling pre categorised phrases on a database. Not so generalized yet due to multiple current limitations like speed, scalability and others. Performance is getting better and better, but it will be a matter of years before the big search engines start annotating webpages purely by using semantic ontologies.