by Konstantin M Golubev
Revised 30-Nov-2001, 1-Sep-2008
Presented for SSGRR (Scuola Superiore G. Reiss Romoli) international e-business conference in Italy
Address
Konstantin M Golubev, GKM Research Group coordinator
General Knowledge Machine Research Group, Kiev, Ukraine
Mailbox 33, Kiev-191, Ukraine, 03191
Email: gkm-ekp at users.sf.net
Web site: http://geocities.datacellar.net/gkmgeosite
Keywords
Knowledge; Management; Learning; Publishing; Consulting
Biographical notes
General Knowledge Machine Research Group was founded in 1986 as informal non-profit institution by mathematicians and IT experts. Now it counts 11 members, including experts in medicine, arts, banking, IT. The aim is research, development and introduction of advanced approach to knowledge presentation and distribution called Electronic Knowledge Publishing, intended to provide transformation of individual knowledge into knowledge publicly accessible and usable.
Konstantin M Golubev is GKM Research Group coordinator. He is working also as a Head IT Expert for Space Research Institute, Kiev, Ukraine. Author of 5 printed papers on e-knowledge applications. Member of ENTOVATION Knowledge Network (http://www.entovation.com/kleadmap/index.htm), International Society for Professional Innovation Management (http://www.ispim.org).
ABSTRACT
This paper includes description of Adaptive Learning, based on the Electronic Knowledge Publishing. It is advancement of distance learning, providing using and learning only knowledge needed. Electronic knowledge systems may be used in any Distance Learning, Knowledge Management and Innovation projects, or for development of help desks, on-line consulting systems, intelligent Web sites.
1. PRACTICAL DEFINITIONS
1.1 Data
We would propose to treat as a "data" everything that could be perceived by human: speech, text, pictures, video etc. Computers are well suited for capturing and dissemination of various types of data. Initial title of computing was "data processing".
1.2 Information
We would propose to treat as an "information" part of data, relevant to experience, knowledge of perceiving person. What is understandable for one person, may be senseless for other. The bright example is using foreign language: those, who understand, benefit, and others treat speech as senseless.
The further development of "data processing" lead to "information systems", "database management systems", "information technologies", "information retrieval systems", "search engines", "directories". The main point is an attempt to assist humans in searching data, relevant both their experience and existing problems. Main principles are using keywords and taxonomy (hierarchy) to find needed information. The known example is Internet search engines and directories. Tools proposed should be estimated as not strong enough, providing two types of faults: you will get data you don't need, and you will not get information you really need. The first is irritating, and the second is very disappointing indeed - information exists, but you may not use it.
1.3 Knowledge
We would propose to treat as "knowledge" part of data that we may learn on. From our point of view every knowledge item should consist of 3 parts: description of problem, title of problem, verification and solution of problem. This definition is in a close correspondence with a definition of production in Artificial Intelligence made by Alan Newell and Herbert Simon, famous developers of General Problem Solver, - "If Situation Then Action".
We think that knowledge creation is always result of real problem solving. You are extremely hungry and at last you get tasty dinner - all situation will be imprinted in your consciousness: what was going, what you were doing and great result!
1.4 Abstract knowledge
Usually knowledge regards concrete problems, but with the time knowledge about typical situations may appear, applied to many cases. The form is the same 3-parts knowledge item. For example, we can remember details of several train journeys to different destinations, but abstract knowledge will be - if you want to reach destination, look for trains schedule and try to get a seat.
1.5 Obsolete knowledge
We think that abstract, most valuable knowledge rarely becomes obsolete. Many items of knowledge remain valid through centuries, only being modified in some degree from time to time.
1.6 Ideas: volume or contents?
People, as a rule, use words to describe their problems. There are so many words, and number of their combinations is countless. It may seem impossible to find a sense in such huge amount of data. But, fortunately, what is really important - it's ideas. Plenty of words are just like clothes that people wear. The same man can wear different garment and remain unchanged as a personality. Therefore we believe that description may be transformed into a set of standard ideas. We propose to define «idea's text» as a standard text unequivocally and directly defining a specific side of a situation i.e. representing a stable structure in a brain's right part responsible for working with images of the world. We think that intellectual activity is based on ideas as images of the world, but not on specific words representing them. This text should not include any excessive words and the words included should always have the right sense. For example, people may say: «It looks so green to me»; «I think it's a greenish stuff»; «It reminds me a fresh grass.» The idea's text should be: «The color is green.» Note that people can express the same idea with words absolutely different.
And what the number of such ideas could be? Famous American psychologist Mr Cattel in his work «Universal Index of Source Traits» has made an attempt to propose a list of items for a human personality features description. Preliminary list included 4,550 different items used by many authors. After excluding synonyms it was appeared that only 171 were left. The same result we have always got from our experience (medicine, art, banking, business etc). Number of ideas used for a description of problems and their solutions in a particular area of knowledge, which may be learnt by one man, was always not exceeded several hundreds.
We would like to note that there is no correspondence between volume of source and richness of contents. That's why work of e-knowledge developers should be respected in appropriate way - in a result huge volumes may transform into small lists of really useful ideas. Please note that human persons only have an ability to find ideas and to exclude synonyms, it's a highly creative and intellectual work
1.7 Intellectual activity
We think that intellectual activity, directed to real problems solving, has the following steps:
1. Examination of situation
2. Producing propositions based on knowledge
3. Ranging propositions, pointing most valuable
4. Verification of propositions in the ranged order
5. Exclusion of impossible propositions
6. Finding the most suitable propositions on the basis of additional examination, special
for them
1.8 Knowledge-based organization
Document Software Strategies Analysis by CAP Ventures (1996) states that:
"As the pace of our move towards an information economy accelerates, however, we've developed a greater appreciation of knowledge as a business asset… We're moving towards a real acceptance of knowledge as intellectual capital, and away from the traditional view of knowledge management as a decision support activity.
...
Knowledge organizations have been characterized as enterprises in which the key asset is knowledge. Their competitive advantage comes from having and effectively using knowledge. Examples include the law office, accounting firm, marketing firm, software company, most government agencies, universities, the military, and significant parts of most manufacturing companies, whether they make cookies or cars.
...
Dataquest has forecast that the level of spending on developing knowledge management delivery capabilities will increase from $US 410 million in 1994 to $US 4.5 billion in 1999. By 2005, more than 50 million jobs will belong to knowledge workers (US Department of Labor)."
1.9 Knowledge of organization
Really working knowledge of organization is inside people's minds. Of course, many applicable ideas, reflecting personal knowledge, may be found in printed and electronic reports authored by people. But till now lack of tools supporting knowledge and ideas presentation and search lead to poor usability of these assets despite their great volume.
Search engines and directories are in some help in finding needed knowledge in texts and databases but they are not oriented especially on knowledge. That's why relevancy and completeness of search results are not sufficient. Please refer to a paper "Intellectual activity, knowledge, information, data… An attempt to define it in an applicable way" found at Web address
http://geocities.datacellar.net/gkmgeosite/gkmpaper.htm.
E-knowledge systems are intended to provide these knowledge and ideas search tools. As a result knowledge, extremely valuable asset, of all people ever worked for organization could be usable for organization sustainability.
You may find several examples of public betas of e-knowledge systems at Web:
E-knowledge system on negotiations:
http://geocities.datacellar.net/gkmgeosite/ekslgt/ekslgt.htm
E-knowledge system on toxicology:
http://geocities.datacellar.net/gkmgeosite/emergmed/emergmed.htm
2. LEARNING
Exams:
Prof.: You are looking very worried. Any problems with exams questions?
Stud.: Oh, no! Questions are OK. It is the answers that I worry about.
2.1 Traditional learning
Traditional learning is based on linear process, when students must learn all proposed knowledge, topic by topic. Then, students must pass exams to get acknowledgement from professors that knowledge is in their minds. Initial time of learning is very great, usually up to 17 years (school + university). There are many exams, sometimes very difficult, having great influence on the life of students. But all this very hard work does not guarantee that students have all or even greater part of knowledge needed to solve problems, which arise in their post-school activity, in a real world life.
2.2 Adaptive learning
2.2.1 Is adaptive learning new in principle?
It seems that adaptive learning is the main stream of human life-long learning activity, though we are not trying usually to distinguish directly traditional learning from adaptive. Traditional learning supplies us with preliminary education, with knowledge of language needed to learn further, and with great amount of knowledge «just-in-case». After that, during all our life, we are trying to find and to learn knowledge needed to solve our problems in our working activity. We are learning not in formal manner in special institutions but as a rule, by our own efforts.
2.2.2 «Just-in-time» knowledge
Adaptive learning tools such as e-knowledge systems are based on a concept called Just-in-time knowledge (JIT-Knowledge). Total amount of external knowledge in various sources, even in specific areas, becomes greater all the time. It is not possible, taking into account limitations of human brain, to learn it with traditional learning, topic by topic. It means that in reality great part of knowledge is not used by anyone, and many problems are not solved because no one learns needed knowledge.
2.2.3 Why adaptive learning tools are not widely recognized?
Till now adaptive learning was mainly manual activity, very time- and efforts-consuming. If we have a real problem, but don't know - how to solve it, whether solution exists and where to learn it, we are trying to find a person experienced in that kind of problems - expert, professor, doctor. And this person, if we are lucky, can tell us how to find facilities to learn how to solve a problem. But it is very hard work to find right expert and in many cases we can appeal to a person not experienced in these problems, and get wrong advice.
For example, medical diagnostics is very complicated matter. If we are trying to get medical advice from a person not experienced in area of real patient's problem (it may occur, because list of World Health Organization counts tens thousand of possible diseases), we are at risk to get wrong diagnosis and wrong treatment. It is common problem in many countries, including developed. Practically all medical institutions resist attempts to disclose their medical errors statistics.
3. POSSIBLE SOLUTIONS
Artificial Intelligence is an attempt to build intelligent tools not based on human knowledge. That's why it is not very helpful in adaptive learning. Please refer to a paper "Is there any future for Artificial Intelligence?" at Web address http://geocities.datacellar.net/gkmgeosite/gkm-ai.htm.
General Knowledge Machine Research Group introduced Electronic Knowledge Publishing with the aim to provide efficient tools for knowledge presentation, knowledge search, online consulting and adaptive learning. Please refer to a paper "Introducing a new kind of publishing: The Electronic Knowledge Publishing" at Web address http://geocities.datacellar.net/gkmgeosite/gkmekp.htm.
4. CONCLUSIONS
Today many organizations treat knowledge as their most valuable asset. Information technologies and training programs are considered as not enough to support knowledge development and application effectively. That's why such movements as knowledge management and knowledge innovation appear to find new possibilities in intellectual assets management.
General Knowledge Machine Research Group proposes absolutely practical and reliable approach for organization's knowledge capturing and dissemination. Our staff got great experience developing e-knowledge systems in various areas including medicine, arts, banking, management etc. For example, from 1995 to 1997 we have had unique chance to develop really advanced e-knowledge system on banking management. The sources were daily digest «Exchanges and banks today», covering 200 magazines and newspapers from all the former USSR states, and local business digests. President of ENERGOBANK E.M.Patrushev was our sponsor, consulting expert and first user. We have learnt very much developing the system. For two years we have been watching transformation of initial huge amount of data into e-knowledge: approximately 1,000 MB of initial texts were transformed into 55 MB text by digest agencies, and into 2,3 MB text by our group. We have found 635 ideas about banking management and E.M.Patrushev used them, assisted with the e-knowledge system, as a president of bank.
We are open for collaboration in any scale intellectual assets management projects. For research and non-commercial projects we can propose development tools and consulting free of charge. Please communicate using email address KonstantinMGolubev@gkm.every1.net or by usual mail at address: Konstantin M Golubev, Mailbox 33, Kiev-191, 03191, Ukraine.
5. ACKNOWLEDGMENTS
We really appreciate Debra M. Amidon from USA, founder of ENTOVATION Knowledge
Network, Professor Ferdinando Chiaromonte from Italy, Professor Kazuyoshi Ishii from Japan,
Professor Veljko Milutinovic, General Chairman of the SSGRR-2002W - for their kind attention and
encouraging attitude.