The knowledge diagram below is a simple semantic network, used to give information concerning an electric space heater in company Q. According to Bedford (2003), semantic network maps are powerful and appropriate diagrams for representing the knowledge system. This diagram is easy to understand by any person, and can be applicable in the system of automated processing. This kind of a diagram is also used as a vehicle of achieving knowledge in a company, and can also act as an archive for storing a company’s knowledge.
The semantic network map in this case, is used by the maintainance department in organization Q, whose duty is to deal with the supply of electricity in the organization. The diagram illustrates how the electric space heater works, and how it should be connected in order to work effectively.
In this simple semantic network, nodes have been used to represent the specific items, the links have been used to show the relationship which exists between the items. The automated system can clearly answer the questions about the specific items that are within the network, by way of following the links. Some of the questions that may be asked for instance , how power gets to the heating element, or a question as to the purpose of the lamp. A person can get the answer by following the links within the network. It is also possible for the computer to construct the available textual statement concerning the knowledge that is found in the network. The semantic network map is accurate in terms of content, as it provides all the information needed to know how the electric space heater operates, the map is visible and serves the required purpose.
Bedford has emphasized the need for a rich data in order to support the discovery of knowledge. He states that, the tools used to leverage the knowledge dimensions cannot work effectively in full text representation of knowledge or on liner, and that there is need to have a rich metadata to have an effective discovery of knowledge. Bedford points out that the most challenging issue in the process of knowledge discovery is caused by the lack of a rich metadata.
Two major issues challenging the application of metadata in the knowledge discovery process are the, interoperability and the availability of the metadata (Bedford, ; Denise, 2003). This paper will pay attention to the issues of availability of metadata as mentioned by Bedford. The three kinds of metadata are the content metadata, user meatadata and the use metadata all of which face the problems of availability of metadata. The content metadata is not available when there is need for describing the user, the metadata required in describing the business architectures is not available, it is only available to be applied in high value context and high profiles, and this makes the metadata very expensive and time consuming. The metadata is never rich enough to support contextualizing and indexing of information. With regard to language context, the metadata is not available.
The challenge of availability with regard to user metadata is due to the fact that, the data is in different forms and syntax and is created for different purposes, it may therefore be hard to get a metadata to suit a certain requirement, the metadata which is often available is scattered in many systems, it does not describe the knowledge and interest of a community or a person with regard to the classification scheme standards. It is also inefficient in capturing the knowledge level of a community or a person. There is unavailability of use metadata because of predominance of the sensitive application of the context.
There is a recommended way of solving the problem of availability of metadata by the use of semantic technologies. These technologies have two important components, a composition component and the decomposition component. The semantic composition attempts to train technology to work like human beings. in solving the problems of metadata. Semantic decomposition on the other hand breaks down the semantic components with regard to knowledge to enable a good understanding of of the aspect at the level of machines.
The semantic decomposition copies the behavior of human beings at their most primitive level in trying to process any form of information. This method applies the processing techniques of natural language. Semantic technologies applies the rule of crammer and comprehensive dictionaries to make the parts of a speech, in the identification of verbs, nouns and various phrases. Important reference is made to significant sources such as institutions, groups or political figures to improve understanding and to provide conical forms.
This provides the basis for understanding the values of a metadata important for inferring or extracting. When knowledge is decomposed, it can then be matched with the rules for metadata. The semantic engine in addition to the fist step are important stages in the achieving of an accurate programming of a metadata generation. The machines helps in determining which words appear together and in what pattern. The tools applied in natural language are important in the discovery of knowledge, but do not help in the semantic decomposition.
Semantic composition is the second part of the knowledge discovery process. This refers to the capability of a machine to apply and interpret rules that are used in the generation of metadata. The very important factors that may lead to success in the generation of automated metadata include the availability of the required technology to implement and represent the rules, the accuracy of the available rules, and whether the metadata suits the rules.
The process used by the machine to generate metadata should be the same as the process that human beings use when they are creating metadata. In the creation of different metadata, human beings use different processes. The rules used in classifying of a certain report may be very different from the rules used to determine the person who is the author of the same report. It is appropriate and important to have the best technologies for processing language, and not merely applying a single tool in the process of generating metadata. One tool, one size, cannot be suitable in dealing with all challenges of metadata.
Taxonomy refers to the branch of science or of any other unit that deals with the classification of various things. Upon codification, a taxonomy gives a terminology that is common for describing a certain aspect in the world. Things are labeled in taxonomy so that they can be understood, improved, controlled or predicted. The taxonomy aspect leads to various discussions that call for establishment of basis which are used to solve problems. Taxonomies are architectural components important in the KM system. They apply in all KM processes, for effective working, the taxonomy should always be supported by KM technologies. knowledge management (KM) is the scientific term used in the collection of organizational data, by understanding and recognizing patterns and relationships and turning these into accessible and usable information and knowledge that is valuable.
Bedford talked about various information models that are applicable in the portals taxonomy or in the intranet suitable in the running of an organization. One of the best information models mentioned by Bedford is the knowledge matrix taxonomy. This model supports the use of two concurrent and distinct dimensions which are combined with a specific piece of the enterprise knowledge which classifies knowledge as an asset and its content.
The first category of knowledge as an asset looks at ways in which the available knowledge exists in the form of an asset, for instance, is it a magazine, a book or a data base? Knowledge is further divided into four classes of an organizational asset which form the knowledge balance sheet. This information makes a 100% of the knowledge asset base of any enterprise. Under this model, an asset is associated with the organizational functions that develop or the asset.
With regard to content, each item is to be associated with the content of knowledge that it contains for purposes of categorizing the asset. A question like, what the asset is about, is answered from the content perspective. Most of the elements of knowledge have a close connection with the market opportunities, other knowledge elements are related to competitive threats which may face a business enterprise at any point in time. The knowledge classification is compiled together in a knowledge compass.
The knowledge compass and knowledge balance, sheet form the Y and X perpendicular axes on the knowledge matrix. The knowledge balance sheet is used to explain ways in which assets in an organization are described in the knowledge asset base. Four major classes are used to categorize the knowledge assets, and they include the protected assets, which are viewed from the perspective of the intellectual property. These also include the trade marks, trade secrets, patents, copyrights, and the brands used by an organization. The purchased assets refers to the assets which third parties produce or purchase for the internal use by the organization. They include books, directories, commercial databases, periodical and syndicated reports, electronic media and custom reports.
The produced assets, talk about knowledge assets such as databases, documents and lists. These are produced either as by-products or as direct products of an organization and they are strategic in value for the organization. Some of the produced categories are the operation data, the transaction data and any other strategic documents. This model looks at people as the most important asset of the enterprise. Whether a person is involved in the manufacturing, services, non-profit work or in the government, they are viewed as the assets that have the greatest contribution in the success of an enterprise. knowledge touching on the economic and literature value of human beings as assets has been keenly looked into. The knowledge possessed by people is categorized on the basis of experience, training and education and the contacts a person has had in the past years within the enterprise, and the outside world.
The knowledge compass helps to solve the major challenge of knowledge management by showing how knowledge should be maintained, purchase and used within an enterprise (Elias, ; Ghaziri, 2003). This approach uses the matrix fashion to explain how assets are used. knowledge about entities and events is explained as internal elements of the enterprise. Process knowledge and product knowledge are used to explain the internal dimensions of an organization.
The knowledge matrix taxonomy is appropriate and effective in the classification of the assets within an organization. This model ensures that each and every asset within the organization has been properly identified and classified so that, such an asset can be understood and improved in an attempt to attaining the best output within an organization.
Bedford, T., ; Denise, A. (2003). knowledge Management Lessons Learned: What Works and What Doesn’t: California: Dominican University Press.
Elias, A., & Ghaziri, H. (2003). The knowledge Matrix: A Proposed Taxonomy for Enterprise Knowledge. Published by Spring Press.