By Nelson M. Mattos
This publication addresses the advance of a brand new new release of structures, wisdom Base administration structures (KBMS), especially developed for powerful and effective administration of data bases, with the purpose of filling a part of the technological hole among synthetic intelligence and databases. The publication first investigates intimately the layout technique, the structure, and the operating equipment of data established structures (KS) which will indicate the foremost features of the sector in addition to its present boundaries, which serve then as foundation for a precise formula of KS specifications. An research and evaluate of alternative techniques (e.g., traditional DBS, non-standard DBS, coupling professional structures and DBS) to wisdom administration is given. The booklet indicates that during constructing KBMS, the event received by means of the research of every of those methods is intensely very important in view that they supply the elemental suggestions for construction KBMS. The ways shouldn't be considered as whole and ultimate yet as a part of examine paintings in the direction of KBMS. a unique structure is defined for KBMS which integrates the performance, flexibility and modeling strength of DBS and AI. the most a part of the publication offers with all very important architectural difficulties of KBMS: equipment for wisdom illustration with distinctive emphasis on abstraction options, language proposals, and ideas for functionality development. The publication is predicated on useful adventure gathered over 5 years of winning learn in coupling latest professional structures and DBS, extending DBS with deductive features, and notably within the layout and implementation of a KBMS prototype. therefore the book's proposals are illustrated with specified descriptions in their consciousness in latest structures or prototypes.
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Extra info for An Approach to Knowledge Base Management
Example text
As shown in chapter two, human knowledge and problem solving process do not fit this model. Firstly, people organize their knowledge in a separate entity (our brain) and access it to reason out specific problems. And secondly, the human problem solving process involves at least three types of knowledge: domain knowledge, heuristics, and problem solving strategy, organized on two or three levels, depending on the existence and use of meta knowledge or not. XPS follow this human model. They collect their "pieces" of knowledge in a separate entity, called knowledge base (KB), which is manipulated by a separate clearly identifiable reasoning component.
By diagnosing XPS weaknesses and deficiencies, the knowledge engineer can begin acquiring new knowledge, modeling it once again, and reevaluating the system. This leads to an incremental, evolutionary development with performance that first is typically away from human levels and then generally comes near to, and sometimes exceeds them. 4. It might be important to point out here one of the most significant aspects in this developing context. Knowledge engineers extract knowledge from experts and integrate it into an XPS.
For this reason, it is quite difficult to make changes in such KB as well as to localize desired information. Here, it should be clear that the above construct is closely linked with the operation of retrieving intentional knowledge from the KB. Nevertheless, this activity is not provided by KR schemes supporting rules but by the inference engine. Only in the last few years, did KR researches begin to realize that KB should not be seen simply as a passive repository of knowledge. , inference) became part of the operations provided by some KR schemes.