Introduction
“Database Management System (DBMS) is an analog-based on the idea of Knowledge Base Management System (KBMS) and the Knowledge Warehouse (KW)” (Date, 1981). In determining the differences among data, information, and knowledge, it is important to note that they all are common in the fact that they emerge from the social process of organizations.
A datum can be defined as the value of a recognizable, quantifiable, or assessable element. Multiple data leads to data. “Information is supplied by data, since data is constantly particular in some abstract context” (Date, 1981). According to Date, “the context must include the class to which the character belongs, the object that is a member of that class, some ideas about object operations or behavior, and relationships to other objects and classes” (1981).
Data by itself and the nonrepresentational form, therefore, do not relay information. “Information is a combination of data, conceptual commitments, and interpretations. Information is data extracted, filtered, or formatted in some way” (Date, 1981).
Knowledge is a division of information that can be removed, sieved, or arranged in a unique manner. It is information that has been subjected to and satisfied evaluations of confirmation. “Common sense knowledge is information that has been validated by common-sense experience. Scientific knowledge is information validated by the rules and tests applied to it by some scientific community” (Date, 1981).
Knowledgebase and database
The chain of commands of the organization’s allowed regulations is its knowledge base. Due to the knowledge base, it is possible to “explain, anticipate, and predict events and interaction patterns in the organization and in its environment” (Karp, 1994).
As a matter of fact, Karp states that the constituents of the knowledge base that deal with the network’s organization comprise “its set of remembered data; its validated propositions and models; its refuted propositions and models; its metamodels; and the software it uses for manipulating these” (1994).
Databases and knowledge bases are quite similar technologies, and both refer to the contents of an information management system. Both systems are a product of research aimed at identifying general information management functions that are common to application programs.
The differences between the two systems are attributed to varying information-management capabilities from the application programs which the researcher can extract and generalize. One of the main differences between the two programs is that database management systems provide high-speed access to huge volumes of information.
In addition to this, they have superior support for information security and standardized database query languages. The knowledge bases, on the other hand, are well suited to representing complex data types and store a lot of meta-information in each slot. They also facilitate the design and evolution of complex information-based schemas through inheritance and run-time schema alteration (Freundlich, 1990).
At the knowledge level, the FRS is more expressive than the RDBMSs in that the former can represent generalizations and particulars. They can also represent partial information and do not make a closed-world assumption. In addition to this, they have three inference mechanisms, namely, inheritance, production rules and classification, and reason-maintenance systems, which are not contained in RDBMSs (Karp, 1994).
Challenges in KBMS
A Knowledge Base Management System (KBMS) is a special computer program that helps to control and deal with the AKB. In fact, it is compared with a DBMS that has similar functions but operates with a database. Challenges raised by the use of knowledge base management systems include the following issues.
Knowledge is dynamic, so this fact allows it to change continuously, thus the value and quality of knowledge are altered all the time; the sources of input information is gathered from multiple sources, which also change continuously. The constant change of the knowledge base is done due to varying knowledge, and the information or data require varying storage and processing solutions.
These challenges imply that things are unknown to a single individual or even a group of individuals since it is cumulative. The complexity of the knowledge base makes it smarter than databases since they process data and use expert knowledge to provide answers, recommendations, and expert advice (Freundlich, 1990).
Implementation of Knowledge Base Management Systems
“Measures of an organization’s knowledge base may be found in its cultural artifacts, including its linguistic products, its electronic artifacts, and its artistic expressions if any” (Firestone, 1999). Artificial Knowledge Base (AKB) is a computer appliance that deals with the information that is stored in the computer persistent and non-persistent memory.
The AKB is similar to a database in that it is “self-descriptive, composed of bits and bytes, permanent in that it is an on-going system, located both in specific in memory locations and in a specific persistent storage location, and is kept to fulfill an organization’s purposes.
Unlike a database that stores records, however, an AKB stores a network of objects and components, and these encapsulate data and methods (validated and invalidated procedural or declarative rules that use validated and invalidated data). So the AKB stores data and information as well as knowledge” (Firestone, 1999).
Conclusion
Data warehousing systems are about to evolve into AKMSs or other programs that are similar to these ones, such as Knowledge Base Management Systems, or Knowledge Warehousing Systems. Thus the coincidence between data warehousing, DSS, and KM can appear as a result of this. As a matter of fact, there exists no other Knowledge Base Management System that is separate from data warehousing, DSS, and KM.
The KBMS is both the AKMS and the Knowledge Warehousing System. When it is necessary to make some decisions, then the Knowledge Warehouse may be used because it may help to deal with some problems. According to Firestone (1999), “it is also a combination of volatile and non-volatile objects and components, and it stores not only data but also information and knowledge.”
The Artificial Knowledge Management System, AKMS, is an On-line Complex Processing (OLCP) System, and not a merely DSS system, like today’s data warehousing system, or an OLTP system, like contemporary ERP systems. The AKMS provides a technology that helps to operate with a system that can be called a Distributed Knowledge Management System (DKMS).
As a matter of fact, it appears that there is no alternative to DBMS templates that could deal with creating and maintaining AKMSs.
“Such tools would need to provide templates for creating persistent data stores, for in-memory object models, for broad connectivity of the AKM to applications, databases, client modules, and communications buses. Current tools come close to having that broad range of capability, and it is possible to constitute a “best-of-breed” suite for constructing AKMSs” (Firestone, 1999).
Both database management systems and knowledge systems have advantages and disadvantages with respect to the other. The knowledge systems work best in operations that involve knowledge while the database management systems operate at the symbol-level best of all. None of the two management systems is well versed in system engineering.
In case when the technical differences are a significant factor in the large disparity in the market share between the two types of systems, their research should be targeted at integrating into knowledge base management systems. However, the capabilities and technical characteristics of those systems may be lacking for maintaining all the operations.
References
Date, C. J. (1981). An Introduction to Database Systems (Vol. I). Menlo Park, Cal.: Addison-Wesley Pub. Co.
Firestone, J. M. (1999). The Artificial Knowledge Manager Standard. Gaithersburg, MD: Knowledge Management Consortium.
Freundlich, Y. (1990). Knowledge bases and databases: Converging technologies, diverging interests. IEEE Computer, 23, 51-57.
Karp, P. D. (1994). Distinguishing Knowledge Bases and Data Bases: Who’s on first and what’s on second. Web.