What are the main similarities and differences between conceptual database modeling techniques and knowledge representation techniques?

Knowledge representation and conceptual data modeling techniques have many differences and similarities between them. Here are some of the most significant ones:
The purpose of knowledge representation and conceptual data modeling is to present objects from the miniwordd and relationships among them. In both cases, an abstraction process is used to define common properties and fundamental aspects of selected objects. When doing so, objects are simplified and all the unnecessary information is omitted.
In both disciplines, we can notice concepts, relationships, operations, constraints and specialized langungesfor representation of dataand knowledge.
Knowledge representation has broader scope than conceptual data modeling. That scope includes rules (for inference, deduction and search) and temporal, spatial, incomplete and default knowledge. These forms are in the process of being included into database models.
Reasoning machines are included in knowledge representation schemes. They are used to establish new facts from the already given facts and they can answer queries hy making conclusions over the given data. Database systemscan only answer direct queries, but nowadays they arebeing extended with inference mechaniame.
Data models concisely represent database schemas. KR schemes include meta-knowledge too, but they also include object instances, examples and exceptions in order to provide flexibility. This can be inefficient because the mix-up of large data with all the instances, exceptions and examples may not be clear enouth.