DBMS-Goal of KR Techniques
The goal of KR techniques is to develop concepts for accurately modeling some domain of discourse by creating an ontology that describes the concepts of the domain. This is then used to store and manipulate knowledge for drawing inferences, making decisions, or just answering questions. The goals of KR are similar to those of semantic data models, but we can summarize some important similarities and differences between the two disciplines:Both disciplines use an abstraction process to identify common properties and important aspects of objects in the miniworld (domain of discourse) while suppressing insignificant differences and unimportant details.
• Both disciplines provide concepts, constraints, operations, and languages for defining data and representing knowledge.
• KR is generally broader in scope than semantic data models. Different forms of knowledge, such as rules (used in inference, deduction, and search), incomplete and default knowledge, and temporal and spatial knowledge, are represented in KR schemes. Database models are being expanded to include some of these concepts
• KR schemes include reasoning mechanisms that deduce additional facts from the facts stored in a database. Hence, whereas most current database systems are limited to answering direct queries, knowledge-based systems using KR schemes can answer queries that involve inferences over the stored data. Database technology is being extended with inference mechanisms
• Whereas most data models concentrate on the representation of database schemas, or meta-knowledge, KR schemes often mix up the schemas with the instances themselves in order to provide flexibility in representing exceptions. This often results in inefficiencies when these KR schemes are implemented, especially when compared to databases and when a large amount of data (or facts) needs to be stored.
