Knowledge management

or KM can refer to the technology, techniques or social practices for organizing and collecting "knowledge" so that it applied at an appropriate time or place. Generally corporate KM empasizes the technology of databases and software applications collecting information.

Contents Definitions of 'Knowledge Management' Related definitions Helpful Tips Knowledge Management tools

Definitions of 'Knowledge Management' :

There is nothing essentially new in the basic concept of managing knowledge, even though as a new discipline it has emerged only recently and, given its newness is still developing its theoretical home. Knowledge management has always been conducted in one way or another, e.g. apprenticeships, colleagues chatting or a parent handing over her/his business to the offspring. The essential difference today is the pace of the environment we live and work in and the demands it puts on the flow of knowledge.

There are several types of knowledge relevant to an organisation. Nonaka and Takeuchi ( Nonaka, I. and Takeuchi, H. (1995). The Knowledge Creating Company, New York: Oxford University Press. ) suggest separating the concepts of data, information, tacit knowledge and explicit knowledge:

• Data is factual, raw material and therefore without information attached.

• Information is refined into a structural form, e.g. client databases.

• Explicit - or codified or articulated - knowledge relates to "knowing about" and can be written and easily transferred.This category of knowledge may include manuals, specialised databases or collections of case law or may even be in the form of standardised techniques of investigation or templates for documents. A key attribute of explicit knowledge is the possibility to store it. Explicit knowledge is 'knowing about', and few disagree that it can be stored and shared using manuals and databases.

• Tacit knowledge relates to "knowing how" or "understanding" and cannot be directly transferred between individuals; it is transferred through application, practice and social interaction.

• Organizational Knowledge management (KM) is the creation, organization, sharing and flow of knowledge in organizations.

• Knowledge Management seeks to make the best use of the knowledge that is available to an organization, creating new knowledge, increasing awareness and understanding in the process.

• Knowledge Management can also be defined as the capturing, organizing, and storing of knowledge and experiences of individual workers and groups within an organization and making this information available to others in the organization.

Related definitions :

• Personal knowledge management - PKM pays attention to the organization of information, thoughts and beliefs. In this approach, the responsibility for knowledge creation lies with the individual who is charged to learn, connect and share personal insights.
  
• Enterprise knowledge management - EKM is concerned with strategy, process and technologies to acquire, store, share and secure organizational understanding, insights and core distinctions. [KM] at this level is closely tied to competitive advantage, innovation and agility.

Helpful Tips :

• It is helpful to make a clear distinction between knowledge on the one hand, and information and data on the other.
• See: KM concepts

Knowledge Management tools :

• Wikis are examples of software systems that can be used as a knowledge management tool. For example the HowTo /Wikisolutions project wants to be to knowledge management what wikipedia is for encyclopedias: a place to organize knowledge and information related to all the things in which people might be interested in an open society. Other wikis may be used inside an organization, and the goal can be to organise knowledge related to the activity of that organisation, such as wikis used in some universities or corporations.

Key Knowledge Management Concepts :

Knowledge management discourse has adopted, invented and refined concepts from a wide range of disciplines and practices. Here is a selection of core concepts that help bound KM which has proved difficult to define.

Corporate memory - a collection of best practices, heuristics , process documents and other texts that help define how a business operates. (related terms: organizational memory or group memory). The ability to capture, maintain and grow a knowledge base, select appropriate technologies and motivate quality contributions, is a key KM theme.

Intellectual capital - the intangible assets of a firm. Competencies, culture and connections that enable and foster innovation, agility, awareness, adaptation and corporate survival. KM plays a role in mapping, recording, evaluating, stewarding, marketing and growing intellectual capital and knowledge assets.

Personal_knowledge_management - a KM theme that focusses on individual responsibility for learning, connecting, organizing and producing knowledge. This is closely tied to blogging, personal information management and branding.

Additional concepts - tacit knowledge , knowledge harvesting , business intelligence , knowledge sharing, knowledge transfer , social capital , social networking , trust (sociology) , Knowledge_representation , DIKW (data / information / knowledge / wisdom)

Lesser KM Concepts :

What we should know to be considered a proficient KM adviser and knowledge worker

• codification vs. personalization - the trade-off between capture and storage of explicit information and making connections to people who know.

• exploration vs. exploitation - should you focus on gathering external information and buying (recruiting) expertise or capture internal best practices and grow local competencies?

• practice vs. process - the balance between informal learning and strictly defined repeatable activties.

• best practices - pattern language , distinctions, collaborative writing, ontologies, inquiry, reflection, concept mapping

• after action reviews (AARs) - learning by gathering participants after completion of a significant project, exploring, reflecting, recording advances and mistakes.

• peer reviews - inviting colleagues who have experience of similar projects to share their tips, tricks and lessons learned before starting out.

• knowledge mapping & audits - discovering opportunities, knowledge gaps and charting flows. A survey to understand where current knowledge is created and who needs it.

• lessons learned (learning histories) - a systematic review of failures and successes conducted by a neutral party.

• 'Ba' - a physical or virtual collaborative space, where participants feel safe and exchange insights.

• induction (aka data mining) - searching for patterns, rules and interesting insights from collected (business) data.

• source documents - collaborative scripts that set forth the intention and vision of the firm or group.

• web standards & protocols - HTML, XML, XTM, RSS

meta-data reciprocity (principle of) web-conferencing blogging knowledge diffusion & sharing CRM - customer relationship management SCM - supply chain management auto-profiling classification stories helpdesks memes heuristics ontology (taxonomy / thesaurus / index) community - CoP, CoI, (facilitation, development, sustainability) group dynamics

External links :

• U Toronto: http://www.fis.utoronto.ca/resources/inforum/kmrg/concept.htm
• http://www.insightknowledge.com/ - InsightKnowledge Inc., KM concepts and theories

Knowledge Representation

Knowledge representation is needed for library classification and for processing concepts in an information system . In the field of artificial intelligence , problem solving can be simplified by an appropriate choice of knowledge representation . Representing the knowledge in one way may make the solution simple, while an unfortunate choice of representation may make the solution difficult or obscure; the analogy is to make computations in Arabic numerals or in Roman numerals ; long division is simpler in one and harder in the other. Likewise, there is no representation that can serve all purposes or make every problem equally approachable.

Contents : 1 History 2 Links and structures 3 Storage and manipulation 4 Language and notation 4.1 Notation 4.2 Language 5 Techniques of knowledge representation

History :

The term "Knowledge Representation" is most commonly used to refer to representations intended for processing by modern computers , and particularly for representations consisting of explicit objects (the class of all elephants, or Clyde a certain individual), and of assertions or claims about them (Clyde is an elephant, or all elephants are grey). Representing knowledge in such explicit form enables computers to draw conclusions from knowledge already stored (Clyde is grey).

Many KR methods were tried in the 1970s and early 1980s, such as heuristic question-answering, neural networks , theory proving , and expert systems , with varying success. Medical diagnosis was a major application area, as were games such as chess.

In the 1980s formal computer knowledge representation languages and systems arose. Major projects attempted to encode wide bodies of general knowledge; for example the "CYC" project went through a large encyclopedia, encoding not the information itself, but the information a reader would need in order to understand the encyclopedia: naive physics; notions of time, causality, motivation; commonplace objects and classes of objects. The CYC project was later taken over by CyCorp, and much but not all of the data is now freely available.

Through such work, the difficulty of KR came to be better appreciated. In computational linguistics , meanwhile, much larger databases of language information were being built, and these, along with great increases in computer speed and capacity, made deeper KR more feasible.

Several programming languages have been developed that are oriented to KR. Prolog developed in 1972 (see http://www.aaai.org/AITopics/bbhist.html#mod ), but popularized much later, represents propositions and basic logic, and can derive conclusions from known premises. KL-One (1980s) is more specifically aimed at knowledge representation itself.

In the electronic document world, languages were being developed to represent the structure of documents more explicitly, such as SGML and later XML . These facilitated information retrieval and data mining efforts, which have in recent years begun to relate to KR. The Web community is now especially interested in the Semantic Web , in which XML-based KR languages such as RDF , Topic Maps , and others can be used to make KR information available to Web systems.

Links and structures :

While hyperlinks have come into widespread use, the closely related semantic link is not yet widely used. The mathematical table has been used since Babylonian times. More recently, these tables have been used to represent the outcomes of logic operations, such as truth tables , which were used to study and model Boolean logic, for example. Spreadsheets are yet another tabular representation of knowledge. Other knowledge representations are trees , by means of which the connections among fundamental concepts and derivative concepts can be shown.

Storage and manipulation :

One problem in knowledge representation consists of how to store and manipulate knowledge in an information system in a formal way so that it may be used by mechanisms to accomplish a given task. Examples of applications are expert systems , machine translation systems , computer-aided maintenance systems and information retrieval systems (including database front-ends).

Language and notation :

Some people think it would be best to represent knowledge in the same way that it is represented in human mind , which is the only known working intelligence so far, or to represent knowledge in the form of human language . Unfortunately, we don't know how knowledge is represented in the human mind, or how to manipulate human languages the same way that the human mind does it. One clue is that primates know how to use point and click user interfaces; thus the gesture-based interface appears to be part of our cognitive apparatus, a modality which is not tied to verbal language , and which exists in other animals besides humans .

For this reason, various artificial languages and notations have been proposed for representing knowledge. They are typically based on logic and mathematics , and have easily parsed grammars to ease machine processing .

Notation :

The recent fashion in knowledge representation languages is to use XML as the low-level syntax. This tends to make the output of these KR languages easy for machines to parse , at the expense of human readability .

First-order predicate calculus is commonly used as a mathematical basis for these systems, to avoid excessive complexity . However, even simple systems based on this simple logic can be used to represent data that is well beyond the processing capability of current computer systems: see computability for reasons.

Examples of notations:

• DATR is an example for representing lexical knowledge
• RDF is a simple notation for representing relationships between and among objects.

Languages :

Examples of artificial languages intended for knowledge representation include:

• CycL
• Loom
• OWL
• KM : the Knowledge Machine (frame-based language used for knowledge representation work)

Techniques of knowledge representation :

Semantic networks may be used to represent knowledge. Each node represents a concept and arcs are used to define relations between the concepts.

From the 1960s , the knowledge frame or just frame has been used. A frame consists of slots which contain values; for instance, the frame for house might contain a color slot, number of floors slot, etc.

Frames can behave something like object-oriented programming languages, with inheritance of features described by the " is-a " link. However, there has been no small amount of inconsistency in the usage of the "is-a" link: Ronald J. Brachman wrote a paper titled "What IS-A is and isn't", wherein 29 different semantics were found in projects whose knowledge representation schemes involved an "is-a" link. Other links include the " has-part " link.

Frame structures are well-suited for the representation of schematic knowledge and stereotypical cognitive patterns. The elements of such schematic patterns are weighted unequally, attributing higher weights to the more typical elements of a schema . A pattern is activated by certain expectations: If a person sees a big bird, he or she will classify it rather as a sea eagle than a golden eagle, assuming that his or her "sea-scheme" is currently activated and his "land-scheme" is not.

Frames representations are more object-centers than semantic networks : All the facts and properties of a concept are located in one place - there is no need for costly search processes in the database.

A script is a type of frame that describes what happens temporally; the usual example given is that of describing going to a restaurant . The steps include waiting to be seated, receiving a menu, ordering, etc.

 

 

Knowledge Transfer

Knowledge transfer in the fields of Organizational development and organizational learning , is the practical problem of getting a packet of knowledge from one part of the organization to another (or all other) parts of the organization. It is considered to be more than just a communications problem. If it were merely that, then a memorandum , an e-mail or a meeting would accomplish the knowledge transfer.

Contents 1 Challenges 2 Process 3 Practices 4 External links

Challenges :

What complicates knowledge transfer? There are many factors, including:

• The inability to recognize & articulate "compiled" or highly intuitive competencies
  
• geography
  
• language
  
• areas of expertise
  
• internal conflicts (for example, professional territoriality )
  
• generational differences
  
• union-management relations
  
• incentives
  
• the use of visual representations to transfer knowledge ( Knowledge visualization )
  
• problems with sharing beliefs, assumptions, heuristics and cultural norms.
  
• previous exposure or experience with something.
  
• misconceptions
  
• faulty information

Everett Rogers pioneered diffusion of innovations theory, presenting a research-based model for how and why individuals and social networks adopt new ideas, practices and products. In anthropology, the concept of diffusion also explores the spread of ideas among cultures.

Process :

• identifying the key knowledge holders within the organization
• motivating them to share
• designing a sharing mechanism to facilitate the transfer
• executing the transfer plan
• measuring to ensure the transfer
• applying the knowledge transferred

Practices

• mentoring
• guided experience
• simulation
• guided experimentation
• work shadowing
• paired w

 

External links :

• Leonard, D and Swap, w. 2005 Deep Smarts. How to cultivate and transfer enduring business wisdom. HBSP. ISBN 1591395283
• http://opensource.mit.edu/papers/Willpope.pdf
• Knowledge Transfer in Information Systems Engineering