Basic logic behind how Google’s AdSense works — automatically analyze the content of web pages to dynamically determine which ads are the most relevant ones to be served there. If you are on a site gathering information about Next generation Notebooks, they show you ads for retailers who sell them — without the publisher or the advertiser (or even the Online Ad Network) having to explicitly specify anything. This is simply contextual advertising, and if this is done using Semantic Technology, its nothing but Semantic Advertising.

Semantic Technology: encodes meanings separately from data and content files, and separately from application code. Semantic technologies are “meaning-centered.” They include tools for: auto-recognition of topics and concepts, information and meaning extraction, and Categorization.

Given a question, semantic technologies can directly search topics, concepts, associations that span a vast number of sources. From the portal perspective, semantic technologies can be thought of as a new level of depth that provides far more intelligent, capable, relevant, and responsive interaction than with information technologies alone.

With HTML and a tool to render it, one can create and present a page that lists items for sale. The HTML of this catalog page can make simple, document-level assertions such as “this document’s title is ‘Widget Superstore’. But there is no capability within the HTML itself to assert unambiguously that, for example, item number XYZ is an Electronic Gadget with a retail price of $199, or that it is a consumer product. Rather, HTML can only say that the span of text “XYZ” is something that should be positioned near “Electronic Gadget” and “$199″, etc. There is no way to say “this is a catalog” or even to establish that “Electronic Gadget” is a kind of title or that “$199″ is a price. There is also no way to express that these pieces of information are bound together in describing a discrete item, distinct from other items perhaps listed on the page.

The Semantic Web takes the solution further. It involves publishing in languages specifically designed for data: Resource Description Framework (RDF), Web Ontology Language (OWL), and Extensible Markup Language (XML). HTML describes documents and the links between them where as RDF, OWL, and XML, by contrast, can describe arbitrary things such as people, meetings, or airplane parts. Tim Berners-Lee calls the resulting network of Linked Data the Giant Global Graph, in contrast to the HTML-based World Wide Web.

These technologies (RDF, OWL, and XML)_are combined in order to provide descriptions that supplement or replace the content of Web documents. Thus, content may manifest as descriptive data stored in Web-accessible databases, or as markup within documents. The machine-readable descriptions enable content managers to add meaning to the content, i.e. to describe the structure of the knowledge we have about that content. In this way, a machine can process knowledge itself, instead of text, using processes similar to human deductive reasoning and inference, thereby obtaining more meaningful results and facilitating automated information gathering and research by computers.

Semantic targeting makes a step ahead of Contextual Marketing by going deeper than keywords and understanding the page’s actual content, which results in intelligently placed ads and higher conversion rates and revenue. Various Publisher Ad Networks have integrated Semantic technology in their system which discovers, classifies and interprets text information automatically, builds detailed taxonomies in any language, and classifies content on publisher web pages into these taxonomies, a method of organizing content on a website.

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