Practical Applications of Semantics: Some natural language processing tasks (e.g., message routing, textual information retrieval, translation) can be carried out quite well using statistical or pattern matching techniques that do not involve semantics...more

Semantic networks are knowledge representation schemes involving nodes and links (arcs or arrows) between nodes. The nodes represent objects or concepts and the links represent relations between nodes. The links are directed and labeled; thus, a semantic network is a directed graph...more

The Semantic Web is a mesh of information linked up in such a way as to be easily processable by machines, on a global scale. You can think of it as being an efficient way of representing data on the World Wide Web, or as a globally linked database. more

The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries. It is a collaborative effort led by W3C with participation from a large number of researchers and industrial partners...more

Semantics: Semantic Networks and the Semantic Web

Definition: A semantic network is a knowledge representation tool consisting of a framework of semantically related terms, with the purpose of allowing a definition of those words through their relationships.

The Semantic Web is a project intended to convert the World Wide Web into a universal semantic network through the use of markup languages and advanced metadata, allowing the Internet to become machine-readable in order to enhance the knowledge management of computers.

Information has always played a vital role in the success or failure of an enterprise. From the smallest one-man enterprise to the largest multinational, the importance of business intelligence cannot be overstated.

Semantic Networks, semantic webThe simple possession of information, however, is not enough to ensure business intelligence. Much more important is the ability to convert that information into valuable data, to allow it to be used in the formation of strategies.

Enterprises collect vast amounts of commercial data every day, from customer feedback and market research to sales data and supply chain information. Unfortunately, it isn’t possible for an enterprise to devote the resources to study all of this data closely (and, even if they could, it would be out of date by the time they had finished). It is vital, therefore, that enterprises have some method of analysing data quickly efficiently so as to highlight patterns and trends that may be useful in the achievement of their objectives.

Semantic Networks

Semantic networks can be invaluable tools in allowing enterprises to quickly convert these vast volumes of information into valuable data (see John F Sowa’s article on semantic networks for reference).

Essentially, a computer-based semantic network uses metadata (data describing data) in order to accurately discern the meaning of information. Without metadata it is impossible for a computer to understand what information means, which severely hampers its ability to manage and serve up valuable data to the user.

semantic webThe benefits of semantic networks in business are plain to see. By allowing computers a method of recognising the meaning of data held within a data warehouse (or other information storage medium), our computer searches for specific information become immeasurably more effective.

Content management systems equipped to support such standards as Extensible Markup Language (XML) and Resource Description Framework (RDF) are able to sort through vast amounts of data automatically, recognising relationships between information and presenting high-quality, relevant data to the user on demand.

An additional benefit of semantic networks in content management is that fact that the sum total of data held within an organisation can be held centrally. When all data is assigned standardised semantic metadata there is no need to store data outside the corporate database. Instead, all data can be stored in a single, inexpensive and easy to manage database to be used by any application.

This approach also lends itself to data integration. There are often problems faced by content management professionals in finding solutions to the problems posed by incompatible data infrastructures. If one department names its products ‘product_name’ while another uses ‘product_description’ there can be problems with compatibility. However, by standardising both datasets using RDF metadata it becomes possible to combine the data from those datasets and put them to good use.

The Semantic Web

The Internet is the largest repository of information on earth. Potentially, this data can be of enormous value to enterprises. However, in reality valuable information often proves difficult to find. At present we are limited to performing searches for information that contains specific words. However, there is no guarantee that this will provide us with useful information.

The Semantic Web aims to transform the way we find information stored on the Internet. Rather than search for information containing our targeted keywords we will be able to search the semantic meaning of the content, allowing search engines to return vastly more targeted information to us.

Enterprises can also benefit from the semantic web from their own websites. By adopting a semantic model on a corporate website it becomes possible to provide the customer with the information they want. Websites built using semantic metadata can recognise synonyms, so they are more flexible and intuitive that typical syntactic sites.

For example, if the enterprise sells bicycles, performing a product search for ‘mountain bike’ may present no results using a syntactic model, as the website may lack a section titled ‘Mountain Bikes’. A semantic site, however, would recognise the connection between ‘mountain bike’ and their product section titled ‘Off-road bicycles’, and would direct the user towards the appropriate section.