Tim Bernars-Lee, the inventor of the World Wide Web (a.k.a., The Web), has spent three decades defining the standards that bring us web pages on our computers and mobile devices. Since his initial proposal in March 1989, The Web has evolved from displaying read-only documents to the interactive social-networking websites we use today. One of the driving technologies of The Web is HTML. It takes these web documents and presents the information in a readable form on your screen with which you can interact. This information is designed specifically to be human-readable. The volume of data on The Web has grown to such a degree as to necessitate computers to read these documents and try to understand its content as closely to a human as possible. Enter the Semantic Web.

The Web is entering a new era, Web 3.0. The result of this evolution in web technology has ramifications for Knowledge Management. The technology and capability objectives of Web 3.0, driven by the large search engines of The Web, endeavor to make The Web's content more understandable to these search engines.

At the heart of every web document is the content. Humans can block out the objects on the page that are extraneous to the central content of the document. If the page is designed properly, the masthead, menu, side navigation, advertising, and footer information is separated from the main content area. Our eyes can see the borders of each element so we can get the context of the document, read the main content, and find links to additional information.

Search engines do not see the document as we do. They need sophisticated algorithms to try to identify the salient information and the extraneous information. The Web 3.0 standards have specific markers that web designers can use to indicate what part of the page is what. The search engine can then apply its algorithms on the main text and multimedia content, and frame that context with the information provided in the header, aside, nav, and footer tags, etc.

Ontological classifications help search engines associate words and word phrases with a derived meaning, i.e., concepts of the text, as opposed to a literal interpretation of each word. Idioms, composite nouns, and proper nouns give search engines a difficult time to get the accurate concept of the document. A search for the word "jaguars" could refer to cats, cars, or an American football team. There are several Semantic Search Engines that are gaining in popularity for returning more accurate, pertinent results than the previous generation of search engines. The result of migrating to this new wave of tools is a better, more pertinent pairing of data and researcher. This technology will no doubt propel Knowledge Management to its next evolutionary stage.

Knowledge Management (KM) started out with the concept of collecting as much information as possible: Documents, authors, relevant dates, location, and user-defined categories. The more information a system could collect, the better chance that the information you wanted would get returned in the results. The failure of this concept is akin to the "needle in the haystack" theory: It is more difficult to find that needle when the haystack is bigger and bigger.

The current stage of KM took the social networking approach. What my friends and colleagues find interesting is probably more interesting to me. Blogs and wikis gained in popularity at the end of the 1990s, which propelled social networking to get a foothold in the ensuing years. This concept is an improvement over the initial strategy of KM. Where this current version of KM falls short is that (to be frank) your friends have a lot of crap that they share that is of no interest to you.

The next stage of KM is one of collecting information for oneself, not pushing information on others. Semantic search engines will be a tool to help with this process. Instead of relying solely on the author or your associates to dictate the relevant concepts of documents, these search engines invoke concept matching for more relevant search results. The search terms you have used in the past in conjunction with the results that you use will help the search engine fine tune the results to give you more meaningful information. When used internally in an organization, KM gets the added bonus of knowing who you are, the role you play in your organization, the teams and teammates with which you work, and pools your search activity with theirs for a social search experience.

In Part 2 of this blog, we will discuss how you can prepare yourself and your organization for KM 3.0.