Knowledge Management Tools: Component Technologies

Topics: Knowledge management, Knowledge, Data mining Pages: 10 (2789 words) Published: June 6, 2013
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Knowledge Management Tools : Component Technologies

Introduction :
The internet provides a multitude of vendors promising to transform our business. But , we have to know which approaches should be adopt to examine the component technologies that make up a knowledge management system or suite. The analogy of hi-fi is used where each item has a certain function or purpose. The multitude of KM system on offer in the marketplace is seen as a composite variation of a number of these component technologies. Firms may decide to buy different component-of-the-shelf or develop their own tools to meet their needs.

Organising knowledge tools

Ontology and Taxonomy
Grubber (1993) defines ontology as “a formal explicit of shared conceptualism”. It helps us on preventing wide variations on understanding or perspective to the same subject. Therefore, we have to developed ‘ontology’ to improve our level of information organization, management, and understanding. In the context of KM tools, the term ontology is often used interchangeability with taxonomy. To clarify the distinction, it’s important to recognize that an ontology is overall conceptualism whereas taxonomy is a scientifically based scheme of classification. An ontology may have non taxonomic conceptual relationship such as ‘has part’ relationship between concepts. In contrast, knowledge taxonomy generate hierarchical classification of terms that are structured to show relationship between them.

When it comes time to implement ontologies and taxonomies there are three options:

· develop the ontology then develop the supporting taxonomy. · develop taxonomy and then develop the over-arching ontology · develop the two in parallel.

Define your scope

The first step in developing the combined ontology and taxonomy is to clearly scope the effort. A clearly defined scope is critical to the success of the effort. The question that can best help shape the scope of the effort is simply, what purpose will the combined ontology and taxonomy serve? The answer to this question serves several purposes: · It sets bounds on the effort. These bounds are necessary to answering the basic managerial questions of how long will it take, and how much will it cost. · It helps identify the primary domains and perspectives to be included. · It should identify the specific business activities that will make direct use of the

ontologies and taxonomies and how the resulting knowledge will be used to accomplish their mission.

Normally the answer to this question will fall into one of three categories: · To serve as a common framework for knowledge sharing.
· To enable reuse of existing domain knowledge.
· To a better understand what the organization knows by separating domain knowledge from the operational knowledge and making assumptions explicit.

The first two of these, knowledge sharing and reuse are at the heart of most KM initiatives. Answers falling into the third category are indicative of advanced KM or academic initiatives. The following steps are applicable to all three scenarios and are specifically targeted toward those efforts focusing on knowledge sharing and reuse.

Check for Existing Ontologies and Taxonomies

Business operations today are also often dependent on, or required to adhere to one or more industry standards and may interface with applications that make use of existing ontologies or controlled vocabularies. For that reason, it is often best to use preexisting taxonomies and ontologies before launching into an extensive and possibly expensive development effort.

Identify Important Terms

If it is determined that existing ontologies and taxonomies are insufficient to meet the scope of the effort, then it is time to start collecting the raw materials for the new structures. This starts with identifying the key terms that are used to express the knowledge needed to enable specific business activities. At this...

Links: Online Analytical processing (OLAP)
The best known knowledge discovery techniques are online analytical processing (OLAP) and data mining (DM) techniques (Turban et al., 1999)
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