Knowledge management and knowledge engineering - Assignment Example

Knowledge management and knowledge engineering Activity 1 Discussion The common feature of these descriptions is their agreement that data is a representation of raw facts. Long and Long (1998) and Laudon and Laudon (1998) agree that data is any raw fact that represents an event. Hayes (1992) considers data as a composition of facts. Activity 2 Discussion Laudon and Laudon (1998) and Long and Long (1998) have similar descriptions of information. They view information as processed data that helps in the establishment of meaning. The common denominator provided by these descriptions is that information is processed data. Activity 3 Discussion From the seven topics, the topics I consider to know are; a close friend and second language. I know these topics because I have interacted with them and understand their characteristics or aspects. The topics I have information about are a company’s annual report, the weather of my location, the weather of another location and the content of a television news program. These topics only present data that has been contextualized. Activity 4 Discussion The primary characteristic that distinguishes knowledge and information is my interaction with a situation. Situations in which I interact with a topic and confirm the available information lead to the creation of knowledge about the topic. Situations in which I have contextualized data are informing situations. Activity 5 Discussion The series of readings can be transferred into knowledge through the processing and contextualization of the readings. The readings are processed according to the weather. The processed data is then contextualized to information. The information is compared or related to previous information in order to confirm the likely weather event. Activity 6 Discussion Activities in knowledge acquisition should be extraction, structuring and organization of knowledge from a source. Knowledge validation should involve the monitoring of a database in order to ensure that previous data is adequate and reliable. Knowledge representation should involve the representation of data into a form that is understandable. Inference should involve the derivation of conclusions based on premises that are assumed true. Explanation and justification should involve the articulation of knowledge using support data. Question 1 Knowledge management and knowledge engineering are two different fields concerned with knowledge. Knowledge management revolves around the execution, administration and supervision of knowledge. Conversely, knowledge engineering is the construction, layout and plan of knowledge. The difference between the two fields is that knowledge managers create the direction of a process while knowledge engineers develop ways for accomplishing the direction. Knowledge management focuses on the knowledge need of an enterprise or entity. People affiliated to knowledge management conduct studies that will enable them to understand the knowledge required to make decisions and execute actions. Knowledge management is central during the design of an enterprise and the processes of the enterprise (Kendal & Creen, 2007). Based on the knowledge requirements of an enterprise, knowledge management establishes enterprise knowledge management policies. Knowledge engineering focuses on areas such as information and data representation, data repositories, encoding methodologies, groupware technologies and workflow management. Knowledge engineering studies the technologies required for an enterprise’s knowledge management requirements. Knowledge engineering also establishes the processes used to examine, assemble and return knowledge to the requestor. The roles of knowledge management and knowledge engineering also differ. For instance, a computer scientist that specializes in the development of computer software systems or artificial intelligence knowledge bases is a knowledge engineer. Conversely, a chief information officer of a corporate is a knowledge manager. This individual is a knowledge manager because of their role in information resource management. Question 2 Knowledge management and knowledge engineering are closely related fields. The relationship between the two fields is seen in the ways in which they address technical and organizational challenges. Knowledge engineering targets the development of information systems. These information systems rely on reasoning and knowledge. Conversely, knowledge management focuses on management and computer sciences. This deals with knowledge as a crucial resource within a modern organization. It is essential to note that the management of knowledge in an organization without the use of advanced information systems is inconceivable. The design, development and implementation of these systems present technical and organizational challenges. Therefore, knowledge management must rely on knowledge engineering, which provides information systems (Kendal & Creen, 2007). Knowledge engineering and knowledge management are related because they facilitate integration, the creation of mutual trust and shared understanding. Knowledge management relies on knowledge engineering in order to collectively organize relevant knowledge. This knowledge predicts the behavior of customers, client and other employees. It also facilitates the effective use of resources. In the case of integration, knowledge engineering and management organize systems and structures in diverse teams. In case the teams need to innovate and differentiate, they use knowledge engineering. Knowledge management helps in the creation of a single cohesive unit. Knowledge management and engineering work to create an environment characterized by mutual trust. They ensure that members of an organization display parity in responses and thought processes. The two disciplines work together to create a high performing team that has the right skills and knowledge. Question 3 The situation in my environment in which data is transferred into information and then knowledge is during the statistical analysis of parameters collected from a quantitative study. In this case, data represents a sequence of quantifiable symbols collected from the field. In the raw form, the data is not considered as knowledge. Data from the field could be figures, pictures, recorded sounds and videos. This data is not organized in any form. The data is described through formal or structural representations. The quantifiable data is stored in a computer for processing. Data processing is an imperative step during the transformation of data into information. Data processing leads to the formation of data structures. Once the data has been fully processed, it becomes information, which is an informal abstraction. The information can be related to existing representations, thoughts or theories, which are significant to the quantitative study. The collected data supports the generated information. It is vital to note that the data cannot change the meaning of the information. Humans incorporate it as information because it will help in the sharing of understanding and meaning (Kendal & Creen, 2007). Knowledge is created through the comparison of information and data from different sources. Knowledge is the inner abstraction of information that is supported by data. In the case of the quantitative study, knowledge is created through the interaction of the researcher with the collected data and information. At this stage, information becomes related to knowledge. While information is theoretical, knowledge is practical. Knowledge is usually tacit, which means that it cannot be fully expressed. The expression of knowledge can be achieved using the collected data. The data is expressed in order to describe a thought, which is information that has been derived from knowledge. Information is related to semantics, while knowledge is related to pragmatics. Question 4 Knowledge engineering and artificial intelligence are two different fields. Knowledge engineering is a discipline that integrates knowledge into systems, mainly computer systems. The knowledge addresses complex problems that require human expertise. Knowledge engineering is important in solving fundamental problems in computer science. It is important to note that knowledge engineering aids the development of knowledge and knowledge based systems. Knowledge engineering focuses on knowledge acquisition, software design, knowledge representation and implementation (Kendal & Creen, 2007). In order to accomplish tasks, knowledge engineering requires the acquisition of knowledge about a system. In addition, it requires methods to represent knowledge using symbolic forms. Artificial intelligence is different from knowledge engineering because software and machines show this type of intelligence. Artificial intelligence relies on knowledge engineering. Whereas knowledge engineering is concerned with humans and knowledge based systems, artificial intelligence is focused on intelligent agents. An intelligent agent is an agent or system that perceives an environment and determines the actions that suites the environment. In artificial intelligence, intelligent agents use and learn deductive reasoning in order to make conclusions and decisions. Humans in knowledge engineering accomplish this function. Question 5 Knowledge engineers have different sources of information. They can employ expert systems, neural network and case based reasoning to obtain the information they require to complete their tasks. Knowledge engineers can employ semantic web based technologies in order to support adaptive information. In order to utilize these tools, knowledge engineers use domain specific vocabularies. These are applicable in the compilation of software components and application software. In addition, they can use open source tools and cases, which are available in digital humanities. An expert system is a system within a computer. The system can offer intelligent advice and make an intelligent decision. Besides, an expert system has the capacity to justify its reasoning. A neural network is a network that collects data (Kendal & Creen, 2007). It performs functions such as estimation, simulation, classification and prediction of data. It also generates data through these functions. A neural network is designed in a way that it will estimate relationships in data. This is useful to knowledge engineers during the mapping of raw data and relating raw data. Knowledge engineers to solve new problems can use case based reasoning. In this case, they can collaborate with digital humanities in order to solve existing problems using past cases. A case comprises of a problem and the solution to the problem. Most importantly, it has an annotation of the ways in which the problem was solved. References Kendal, S. L., & Creen, M. (2007). An introduction to knowledge engineering. London: Springer. Read More