- Semantic advertising
Semantic advertising applies semantic technologies to
online advertisingsolutions. The function of semantic advertising technology is to semantically analyze every web page in order to properly understand and classify the meaning of a web pageand accordingly ensure that the web page contains the most appropriate advertising. Semantic advertising increases the chance that the viewer will click-thru because only advertising relevant to what they are viewing, and therefore their interests, should be displayed.
The Evolution of Online Advertising
Advertising on the Internet has the potential to be finely
narrowcast, i.e., specifically adjusted to the interests of the individual viewer. However, mainstream techniques for identifying these interests - contextual advertising and behavioral targeting - are problematic [http://www.ojr.org/ojr/stories/050706glaser/] [http://www.searchenginejournal.com/critical-flaws-in-googles-behavioral-targeting-puts-advertisers-at-risk/5898/] .
Semantic analysis of a web page provides an understating of its overall meaning in the semantic approach. By comparison,
contextual advertisingtechnology bases web page advertising on a keywordscan of the text of a websiteor Internet search. An automated function loads advertisements onto the web page based on the text. However, keywords can have various meanings. For example, a web page containing the word “jaguar” may generate ads about zoos and cars. The very different nature of these contextual results means that someone’s advertising budget is not being spent wisely.
Behavioral targetinguses information collected on an individual's web-browsing behavior, such as the pages they have visited or the searches they have made, to select which advertisements to display to that individual. However, many platforms identify visitors by assigning a unique HTTP cookiethat tracks the web pages they visit. Cookies are the subject of controversy due to privacy issues. In fact, the United States and European governments have taken action towards restricting their use [http://www.itbusinessedge.com/blogs/ssg/?p=366] [http://europa.eu/scadplus/leg/en/lvb/l24120.htm] .
The Function and Benefits of Semantic Advertising [Based on information from the web pages of iSense (see http://www.isense.net) & Peer39 (see http://www.peer39.com/advertisers.html)]
Any technology based upon
semantics, should be capable to identify the meaning and context of the words on the page, in order to determine the appropriate advertising. This is an advancement to the current contextual model where the simple identification of keywords is seen as sufficient to represent the context of the entire page.
Semantic advertising is based upon two genres, the pure semantic solutions using ontologies and taxonomies, and those using technologies such as
natural language processingand machine learning
The semantic solution allows for a broad ranging, often human developed, taxonomy of advertising categories. The taxonomy categories will contain all the related terms and phrases articulating a specific subject, enabling the categorization of a page as being about a specific subject.
The machine learning technologies are often based upon concepts such as the
naive Bayes classifieror support vector machine. These technologies require 'training' to assimilate data to lead to effective categorization but are scaleable and have multilingual capabilities.However, in many cases, the use of the phrase semantic in describing a product is in fact a misnomer as the products are simply extensions to the current contextual model and provide an ability to "identify" the high value keywords on a webpage.
The advantages of using semantic advertising are the capabilities to identify the entire content of a webpage and extract all of the core themes from within the content, enabling the advertiser to choose the most commercially relevant theme. A further advantage is the ability to perform word sense disambiguation, i.e the ability to understand the correct sense of the words used in order to avoid misplacements of ads. To use the previous example, semantic advertising would be able to differentiate between pages discussing zoos versus cars.
Deploying semantic technologies for advertisement can serve a whole set of other benefits as well. Semantic advertising can also recognize
sentiment, for example, if a web page is discussing a topic in a negative or positive manner. Therefore, an ad for the next US Democratic presidential candidate would be posted on a web site with a positive opinion of the person.In addition, semantic advertising products such as ad pepper's iSense products, provide a capability to filter potentially objectionable content such as adult, gambling, drugs and oter such controversial themes.
Semantic advertising is a valuable technology because it increases an advertiser’s return on investment and allows an Internet user to more easily locate an appropriate product or service. Semantic advertising also allows analytical functions that provide data to track the effectiveness of an advertising campaign.
Semantic advertising is a natural development in the general area of the
Semantic Web, otherwise known as Web 3.0. Although the eventual nature of Web 3.0 has yet to be determined, the existence of organizations such as the World Wide Web Consortiumindicates that there is significant momentum of development in this area.
Several companies are pioneering semantic advertising technologies:
*adpepper media (http://www.adpepper.com), based in Nurnberg, Germany, the market leader in semantic advertising with its iSense semantic advertising solution (http://www.isense.net). Developed by linguist David Crystal and launched by Crystal Semantics in 2004. Subsequently acquired by adpepper in 2006.
Peer39(http://www.peer39.com), based in New York City, is a dedicated developer of semantic advertising technology and has been in operation since early 2006.
* [http://www.w3.org/2001/sw/ W3C Semantic Web Activity]
* [http://infomesh.net/2001/swintro/ The Semantic Web: An Introduction]
Wikimedia Foundation. 2010.
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