How to Get the Most Out of



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Scripting


A popular technique to improve the productivity of people involved in low-end knowledge work. An expert lays down a script that tells lower-level knowledge workers what to do under different circumstances. Scripting can bring the lowest performers to a certain level of proficiency. But it is unlikely to create a high-performing knowledge work force. Moreover, for jobs involving high levels of knowledge, scripting is unlikely to be effective. The trick may lie in identifying the parts of the job that can be scripted. Thus, the power point slides for a B School course can be standardized. But it could be quite difficult to script the actual delivery of instruction in the class room. That depends critically on the skills of the instructor.

Search Engine


The most important technology for manipulation of explicit knowledge. Without effective search facility, a repository will be meaningless. Search engines contain software that looks for web pages containing one or more of the search terms. They then display matches ranked by a method that usually involves the location and frequency of search terms. Search engines create indexes of the web pages they visit. The search engine software then locates web pages of interest by searching through these indexes. The program used to perform the indexing function is called spider or crawler. Search queries are often ineffective because they retrieve many irrelevant documents. Improvements are possible through better understanding of the context of information needs and more knowledge of the domain being searched. An efficient taxonomy can help by arranging documents more systematically.

Search strategies can be of various types:

Metasearching: Based on meta categories and dependent on keywords and attribute tags. Metasearching minimizes the time spent in locating the right category. This approach emphasizes clarifying the context intended by the user through refinement and rejection.

Hierarchical: Knowledge is organized in a fixed hierarchy. Links can be used to efficiently locate the knowledge needed. Hyperlinks are provided to dig deeper.

Tagged Attribute: This approach matches user input attributes against attributes or tags associated with documents and pointers. Ranking of results is based on relevance.

Content: Search term, keyword or text string are matched to return results with relevant scores based on the frequency of matches. This strategy is slow and inefficient.

Combinatorial: It combines two or more of the approaches mentioned above and executes them in parallel.

Various automated mechanisms are available for enhancing knowledge search and retrieval capabilities. Clustering automatically finds groups of related documents such as technical reports. Categorization assigns new knowledge elements to one or more categories from a
user-defined taxonomy. There are tools available to generate taxonomy as well. Then there are translation capabilities which recognize and translate key concepts from one language to another. A thesaurus can be a useful tool for connecting inconsistently defined concepts in search queries.

SECI Model


Developed by Takeuchi and Nonaka, SECI (Socialization, Externalization, Combination and Internalization) is probably the best well known and the most comprehensive theory of organizational knowledge creation. The model views the process of knowledge creation as taking place in four phases.

Socialization is the process of converting tacit knowledge into Explicit knowledge by sharing experiences.

Externalization is the process of converting tacit knowledge into
explicit concepts.


Combination is the process of combining and systematizing explicit concepts into a knowledge system.

Internalization is the process of converting explicit knowledge into tacit knowledge through learning by doing or by relating to the
experiences of others.


The movement through the four modes of knowledge conversion is represented not by a circle but by a spiral. Knowledge gets amplified as it moves through the four stages of knowledge conversion. The SECI model views knowledge creation and knowledge sharing, both tacit and explicit knowledge holistically, rather than as watertight compartments.

Semantics


Formal rules and procedures for representing meaning. Semantic feature is any defining characteristic of the meaning of a word which serves to distinguish it from the meaning of other words. This is important
because words are often used loosely and interchangeably.


(See also: Semantic Network, Semantic Web).

Semantic Network


A method of representing structured knowledge using nodes and links. The nodes are concepts or entities, while the links represent relationships and associations among the concepts. A semantic network assumes
information is stored in the form of words, concepts or propositions as independent units which are interconnected by links or relations.


Important semantic relations include:

Meronymy (A is part of B)

Holonymy (B has A as a part of itself).

Hyponymy (A is subordinate of B; A is a kind of B).

Hypernymy (A is superordinate of B).

Synonymy (A denotes the same as B).

Antonymy (A denotes the opposite of B).

There are various types of semantic networks like the Semantic Network Processing System (SNePS) of Stuart C. Shapiro or the MultiNet paradigm of Hermann Helbig (MultiNet is an acronym for “Multilayered Extended Semantic Network”). MultiNet is well suited for the semantic representation of natural language expressions.

A mind map can be considered a very free form variant of a semantic network. By using colors and pictures, the emphasis is on generating a semantic net which evokes human creativity.

(See also: Mind Map)


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