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Semantic Web
The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries.
On the Semantic Web, anything (objects or concepts) can be identified by URI references, and anybody can express whatever he/she wants to say about anything by using the RDF data model. Therefore, the Semantic Web can be modeled as a graph of semantically linked things (objects and concepts), instead of the linkage graph of Web pages (the basic model of the earlier Web).
The concept of Web 3.0 (the Semantic Web) includes a vision for the next evolution of on-line Search. It would improve Search results for users by understanding the meaning (semantics) of their queries and the results.
For example, a user could ask about a “cheap SUV” and the computer would look for automobiles in the “SUV” category with low prices. In order for this to work, the software would need to understand the concept of “cheap” with respect to SUVs, the concept of “SUV”, and the brands of cars that are in the SUV category.
The ideal software for the Semantic Web would be a full Artificial Intelligence system with natural language processing that computes the meaning of words and phrases. To get to such an AI system, which does not currently exist, we must take incremental forward-moving steps to make the computer behave more intelligently without really being intelligent.
In “bottom-up” semantics, individual pages are hand-tagged with semantic categories.
In “top-down” semantics, a developer models the semantics of a vertical domain (such as, farm equipment, movie cameras, etc) and the kinds of things users would want to do in that domain, and then links Web pages into a meaningful series of information views and user actions.
As an example of “bottom-up semantics” as used on the Web, pages about “SUVs” would be tagged with the categories “SUV” and “automobile”.
Search software could then reason with an ontology relating various Web pages.
For example, a page about “automobiles” would be linked by semantically-tagging to pages about “SUV”, “coupe”, “sports-car”, “Porsche”, etc.
Special tagging languages, such as RDF and OWL, are typically employed for hand-tagging. Some say these languages are daunting, but whether they are or not, most Websites have not tagged their pages due to the massive amount of labor involved in the process. This represents a significant barrier to motivating Websites to tag their content.
To overcome this obstacle, automated tagging has been explored, but such an automatic tagging algorithm would, by its very nature, be the very kind of intelligent (semantic) system desired which would obviate the need for tagging altogether.
Since “bottom-up” tagging has not been widely adopted, a “top-down” approach has been tried. In this scenario, a semantically coherent domain of sites is targeted, and developers model the typical users’ interactions in that domain.
This is an example of “broad semantics” (i.e. an interpretation), in that the user’s knowledge of the domain, needs in the domain and actions in the domain are modeled.
Typical actions desired in that domain are hand-categorized and hand-encoded.
For example, the vertical Search site Retrevo handles information about electronic devices employing the semantics of that space.
Semantics of the space means the way the space, or topic of interest, is categorized cognitively by anyone interacting with it. For example, in interacting with a Website about electronic devices, people want to find out details about particular equipment, while other users are interested in reactions to the equipments, pricing, etc.
Using “top-down” semantics, the Website is organized around these user needs into meaningful categories, such as, Most Popular Results, Manufacturer Info, Reviews and Articles, Forums and Blogs, Daily Deals, and Stores.
Thus, the user doesn’t have to browse all the results for a given query, but can browse just the information in the desired category.
http://www.readwriteweb.com/archives/the_top-down_semantic_web.php
An alternative method to “bottom-up” and “top-down” semantics is Semantic Natural Language Processing, such as that employed by Cognition Technologies, in which the computer has been taught the meanings and relationships of all of the words and phrases, and also recovers the meanings of the words and phrases in searched document set.
With this approach, no hand- or automatic-tagging is required, and the computer appears to understand what the user and documents mean. Visit Cognition for a demonstration of this technology.
