Data & Knowledge Engineering

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Data & Knowledge Engineering

Introduction
Database Systems and Knowledgebase Systems share many common principles. Data & Knowledge Engineering (DKE) stimulates the exchange of ideas and interaction between these two related fields of interest. DKEreaches a world-wide audience of researchers, designers, managers and users. The major aim of the journal is to identify, investigate and analyze the underlying principles in the design and effective use of these systems.DKE achieves this aim by publishing original research results, technical advances and news items concerning data engineering, knowledge engineering, and the interface of these two fields.

DKE covers the following topics:

1. Representation and Manipulation of Data & Knowledge: Conceptual data models. Knowledge representation techniques. Data/knowledge manipulation languages and techniques. 2. Architectures of database, expert, or knowledge-based systems: New architectures for database / knowledge base / expert systems, design and implementation techniques, languages and user interfaces, distributed architectures. 3. Construction of data/knowledge bases: Data / knowledge base design methodologies and tools, data/knowledge acquisition methods, integrity/security/maintenance issues. 4. Applications, case studies, and management issues: Data administration issues, knowledge engineering practice, office and engineering applications. 5. Tools for specifying and developing Data and Knowledge Bases using tools based on Linguistics or Human Machine Interface principles. 6. Communication aspects involved in implementing, designing and using KBSs in Cyberspace.

Knowledge engineering (KE) was defined in 1983 by Edward Feigenbaum, and Pamela McCorduck as follows: KE is an engineering discipline that involves integrating knowledge into computer systems in order to solve complex problems normally requiring a high level of human expertise. It is used in many computer science domains such as artificial intelligence, including databases, data mining, expert systems, decision support systems and geographic information systems. Knowledge engineering is also related to mathematical logic, as well as strongly involved in cognitive science and socio-cognitive engineering where the knowledge is produced by socio-cognitive aggregates (mainly humans) and is structured according to our understanding of how human reasoning and logic works. Various activities of KE specific for the development of a knowledge-based system: * Assessment of the problem

* Development of a knowledge-based system shell/structure * Acquisition and structuring of the related information, knowledge and specific preferences (IPK model) * Implementation of the structured knowledge into knowledge bases * Testing and validation of the inserted knowledge

* Integration and maintenance of the system
* Revision and evaluation of the system.
Being still more art than engineering, KE is not as neat as the above list in practice. The phases overlap, the process might be iterative, and many challenges could appear. -------------------------------------------------

Knowledge engineering principles
Since the mid-1980s, knowledge engineers have developed a number of principles, methods and tools to improve the knowledge acquisition and ordering. Some of the key principles are: * There are different:

* types of knowledge each requiring its own approach and technique. * types of experts and expertise, such that methods should be chosen appropriately. * ways of representing knowledge, which can aid the acquisition, validation and re-use of knowledge. * ways of using knowledge, so that the acquisition process can be guided by the project aims (goal-oriented). * Structured methods increase the efficiency of the acquisition process. * Knowledge Engineering is the process of eliciting Knowledge for any purpose be it Expert system or AI development...
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