Search engines like Google are fundamentally based on the presence or absence of specific words or phrases in a given object. The object (html, picture, video) is uploaded to an internet site, the search engines grabs the available data and stuffs it into a searchable index. Then based on how much money you pay them, you end up on page 1 or page 4,568 of the search result returned if and only if the person enters the same words you used. An example perhaps will help.

The caption and tags for this picture uploaded will be “Surfing at Cox Bay – January 2011”

The search engine will grab the data from my Flickr photo site and index it accordingly.

Now I go to the Google or BING or Yahoo site and type in “Things to do in Tofino BC in the winter”

The above photo will never be returned in the set as the metadata does not match.

Enter the next generation of Expert System driven Knowledge technology.

The folks at CMU have been busy working on this problem with creation of the SCONE engine. 

The technical description is:

“Scone supports simple inference over the elements and statements in the knowledge base: inheritance of properties from more general descriptions, following chains of transitive relations and detection of type mismatches.In addition, Scone provides support for search within the knowledge base. For example, we can ask Scone to return all individuals or types represented in the KB that exhibit some set of properties, whether these properties are explicitly stated or inherited from a superior class in the type hierarchy.Scone’s type hierarchy allows multiple inheritance and exceptions. In addition, Scone supports multiple contexts in the knowledge base. The context mechanism allows us to efficiently represent and reason about different states of the knowledge base, including hypothetical or counter-factual states, various opinions, and groups of statements that are true only in some specific time or place.”

Okay you can cut and paste that statement into a translator or make do with my version of it which is this.

In its simplest form take the surfing example above.

“Surfing at Cox Bay – January 2011”  we can infer (expert system rule) certain things

• surfing is a leisure activity, sport, recreation activity (Inheritance)
• Cox Bay is in or close to Tofino, BC, Canada,  (inheritance)
• January 2011 it is winter in Tofino, BC, Canada  (inheritance)

“Things to do in Tofino BC in winter”

• things to do implies the query issuer is looking for leisure, sport , recreation or entertainment events or practices (context)
• Tofino BC implies a location or surrounding area where activities are found
• Winter in Canada implies months of Dec through March of any 2 years factually and +/- 2 months colloquially. (Winter is an aggregation of serial months between two years)

 

The next generation in technology will be much smarter than what we have today. The search in the case above will find that surfing photo when you ask it for “Things to do in Tofino BC in winter”. Yes it is in fact true by the way that surfing is a big draw to Tofino year round including New Years day when I took that photo. What to do in Tofino in winter

It will also change the SEO market. (Oh yeah and the search engine that gets this commercialized first, that’s the one you want to buy all of their stock with every available penny as they will dominate the rest and you will get rich)

It’s not just search engines that benefit from this technology, but imagine the impact on police databases,  medical and bio research and  other commercial applications. Perhaps linkedin?

“Brilliant, good looking architect that knows a lot about little and a little about a lot in western Canada” <find>

The smiley is actually important to this blog post because the originator of the  : – >  is a gentleman named Dr. Scott Fahlman who created it at CMU in 1982. He is also the Principal Researcher of the CMU SCONE project.