Terminology vs Knowledge

In our perpetual climb up the value chain beginning with the bits and bytes of data, then to recognizable information and finally knowledge, terminology fits right in the middle. Terms, or words, are recognizable slices of information that may be organized into a hierarchy or system. In fields such as medicine or biology with long histories of terminology development, many different systems of terms have cropped up due to the various needs of sub-groups of users and their isolation from each other. As a result, there is much duplication, overlap and confusion in terminology use even within the knowledge domain.

The need to interface between various networks of healthcare payers and providers has driven the demand for organization in this confusion, resulting in systems such as Current Procedural Terminology (CPT), SNOMED Clinical Terms, and International Classification of Diseases all developed to assist healthcare providers in finding a common vocabulary to describe the services they provide.

Selecting a standard terminology provides a common framework and is a significant step forward, but these are merely words; text strings in a matrix of other text strings without the ability to transfer significant quantities of knowledge. Humans naturally make the connection between a term and the object it represents and in fact expect that term to be imbued with the requisite information, but such is not the case with computers. To a computer "Joe Smith" is just a nine character text string.

Object Oriented Programming (OOP) sought to change all that, providing the knowledge underpinnings to turn simple terms into full-fledged objects that behave as do the terms they represent, or at least to the degree necessary to fulfill the requirements of the software. And there you have it: what exactly are the requirements? For a terminology management system it is to standardize and organize the diverse terminologies and there it stops. Most knowledge management systems aspire to loftier goals such as supporting decision making processes.

And thus we have a grey area: terminology management systems that aspire to be knowledge management systems. Or users who want them to be. A successful terminology management system is one which includes and classifies as many terms in the domain as possible whereas a successful knowledge management system includes as many functions in the domain as possible.

Let's take for example an anatomical terminology management system. If this includes a complete catalog of anatomical parts, including synonyms and locations, this will fulfill the requirements of users who wish to know what term to use and in what context. However, even if we know that the femur is attached to the hip and is in the leg, the terminology management system may not indicate that it is a bone or that it could suffer a fracture. This is the type of information that would be contained within a knowledge management system.

Perhaps the most effective knowledge management systems would be ones that incorporate a terminology catalog at the front end; a place where every term could be found and then once found could capture the information needed to model that object. As terminology management systems finally catch on and fulfill the needs of the industry, knowledge management systems will not be far behind.

Web 3.0 Already?

In the midst of the Web 2.0 hype cycle is it time to begin the buzz on Web 3.0? Although Web 2.0 was a big step forward, its limitations are becoming apparent even as its definition has only just been resolved. The connections put in place by Web 2.0 by social networking, folksonomies and tagging have provided a higher level of functionality for some applications, but the connections are only loosely defined. Much more powerful functionality will come with better defined connections and structured frameworks.

Although the term Web 3.0 was never used by the founders of Semantic Web, there is a growing acceptance that the two are synonymous. Certainly the proponents of Semantic Web technologies, including Tim Berners-Lee, could benefit from the idea that their ideas will form the next version of the Web. And it appears that the public is ready for the technology as well, the functionality if not the demands it will require.

So what can Web 3.0 do that 2.0 cannot? For one it helps computers better "understand" terms used on the Web. What is the difference between a book and a basketball? Simpler technologies would recognize that they are spelled differently and that would be it. Web 3.0 will categorize them and provide them with a set of associations that will define what they are. A book is a subject that contains information and is associated to readers by a relationship called "is read by." Many such associations can connect the book to other objects, i.e. "is stored on" a bookshelf. As these associated networks grow, more knowledge about what a book is, and how it is distinguished from a basketball, is compiled and a clearer vision of book is developed.

This is a similar process to human learning and, like humans, as the knowledge networks grow they will become more "intelligent." The process will begin with specific knowledge domains, such as libraries of books, airline travel or drug development, and continue, theoretically, until the barriers between the domains break down and connections through the entire Web are established.

One obstacle will be the structure imposed by the Semantic Web. Web 2.0 calls for a very informal structure where users apply their own tags however they see fit or not at all. Semantic Web on the other hand, requires strict adherence if it's going to function correctly.

But the pay off for applying structure is inference and reasoning; the ability for the software to make connections when given the proper data. This ranges from simple inferences such as: if hepatitis is a disease and it occurs in the liver, it must be a disease of the liver. Although not rocket science for humans, assembling networks of logical statements in a structured framework will be a big step forward for computers.

Much of human knowledge is acquired over time and through experiences. This type of knowledge is stored away in the brain to be pulled out at a later time when certain associations may be required, say in diagnosing a disease. A less experienced physician may not have experienced a patient with certain symptoms that an older colleague would have. But an effective Web 3.0 knowledge base may supplement the less experienced physician's knowledge and allow her to operate as if she had the knowledge possessed by the more experienced physician.

The value of such a system in the hands of a skilled user is to rapidly amplify the knowledge that they can process. Web 3.0 technology has been called "XML on steroids." Given the discipline that is required to implement it however, its use will be constrained to only the most valuable markets for the near term.

The Human Brain as a Hard Drive

I have always been somewhat put off by fields of endeavor that require large amounts of rote memorization. This is why I chose physics over biology as an undergraduate; I assumed that with physics I could simply derive everything from first principles while with biology, due to the lack of knowledge regarding mechanistic relations in biological systems, was more a matter of memorizing large amounts of names and reactions. No doubt I was also somewhat unsure of my powers of memory; just memorizing correct spellings could give me fits. But I still believe that the human mind is much better suited as an analysis tool rather than a data storage device.

While working with clinicians I am often amazed at how much information they have memorized. In the medical television series "ER" actors posing as doctors frequently rattle off diseases, symptoms, diagnoses and possible treatments but we all know they just memorized these minutes before the cameras started rolling. In reality many doctors do have this type of encyclopedic memory. Dr. John Hutton of the Cincinnati Children's Hospital Medical Center told me that he had memorized about 400 diseases that he could name, diagnose and treat. Based on that knowledge, he could then infer on several times more diseases. Although this volume of knowledge is a tremendous advantage in clinical care, WebMD lists over 4,000 diseases and conditions and the numbers are constantly increasing. We have reached a point where the biomedical knowledge base has overwhelmed the memory capacity of the human brain.

Although it would be easy enough to develop a system to provide rapid information retrieval at the point-of-care, what we really need is rapid knowledge retrieval; information that has already gone through preliminary processing and can deliver answers, not just pages out of medical journals. This is the leading edge of clinical informatics; a fledging field with some daunting challenges.

The Value of Evangelists

I had a conversation this morning with long time medical informaticist Rob McClure. Having been at the forefront of health IT for years, Rob has seen his share of companies rise and fall as the healthcare industry has struggled with its information issues. His belief is that our society values the evangelists over producers; that we are all too easily pulled in by the next great thing. He does admit that when he uses the word "value" he means "attracted to." Put that way, I tend to agree with him. If he meant "value" as in "invest resources in," all academics would be billionaires. But the days of throwing money at good ideas before they've been demonstrated are over as any entrepreneur can tell you. Nevertheless, ideas are sexy, and we'd much rather listen to someone telling us about the way their new idea will save us time, money and make our belly's flatter than to listen to someone tell us that nothing less than 1,000 sit-ups a day will do the trick.

I continue to be reminded of the film Envy (obviously left a better impression on me than the vast majority of the film going audience) where the Jack Black character comes up with a winning, although completely unproven, product idea and the Ben Stiller character dismisses the thing outright as inane. Sure enough, Black makes a mint while Stiller wallows in mediocrity. And sure enough again the whole thing comes crashing down when one LITTLE detail (as originally foretold by Stiller) was overlooked. Oh well, off to the next big thing...