Data Abstraction, Knowledge Representation, and Ontology Concepts
In this section we discuss in general terms some of the modeling concepts that we described quite specifically in our presentation of the ER and EER models in Chapter 7 and earlier in this chapter. This terminology is not only used in conceptual data modeling but also in artificial intelligence literature when discussing knowledge representation (KR). This section discusses the similarities and differences between conceptual modeling and knowledge representation, and introduces some of the alternative terminology and a few additional concepts. The goal of KR techniques is to develop concepts for accurately modeling some domain of knowledge by creating an ontology12 that describes the concepts of the domain and how these concepts are interrelated. Such an ontology is used to store and manipulate knowledge for drawing inferences, making decisions, or answering questions. The goals of KR are similar to those of semantic data models, but there are 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 (also known as domain of discourse in KR) while suppressing insignificant differences and unimportant details.
? Both disciplines provide concepts, relationships, 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.
? 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.
? 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 with databases and when a large amount of data (facts) needs to be stored.
We now discuss four abstraction concepts that are used in semantic data models, such as the EER model as well as in KR schemes:
(1) classification and instantiation
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