Expressive power (computer science) in the context of Web Ontology Language

Database theory encapsulates a broad range of topics related to the study and research of the theoretical realm of databases and database management systems.

Theoretical aspects of data management include, among other areas, the foundations of query languages, computational complexity and expressive power of queries, finite model theory, database design theory, dependency theory, foundations of concurrency control and database recovery, deductive databases, temporal and spatial databases, real-time databases, managing uncertain data and probabilistic databases, and Web data.

The Resource Description Framework (RDF) is a method to describe and exchange graph data. It was originally designed as a data model for metadata by the World Wide Web Consortium (W3C). It provides a variety of syntax notations and formats, of which the most widely used is Turtle (Terse RDF Triple Language).

RDF is a directed graph composed of triple statements. An RDF graph statement is represented by: (1) a node for the subject, (2) an arc from subject to object, representing a predicate, and (3) a node for the object. Each of these parts can be identified by a Uniform Resource Identifier (URI). An object can also be a literal value. This simple, flexible data model has a lot of expressive power to represent complex situations, relationships, and other things of interest, while also being appropriately abstract.

In formal language theory, a noncontracting grammar (also called monotonic grammar) is a type of formal grammar whose production rules never decrease the total length of a string during derivation. This means that when applying any rule to transform one string into another, the resulting string must have at least as many symbols as the original.

Noncontracting grammars are significant because they are equivalent in expressive power to context-sensitive grammars and define the same class of languages (the context-sensitive languages) in the Chomsky hierarchy. This equivalence makes them important for understanding the computational limits of natural language processing and compiler design, as they can model complex linguistic phenomena while maintaining certain desirable mathematical properties. Some authors use the term context-sensitive grammar to refer to noncontracting grammars in general, though this usage varies in the literature.

A deductive database is a database system that can make deductions (i.e. conclude additional facts) based on rules and facts stored in its database. Datalog is the language typically used to specify facts, rules and queries in deductive databases. Deductive databases have grown out of the desire to combine logic programming with relational databases to construct systems that support a powerful formalism and are still fast and able to deal with very large datasets. Deductive databases are more expressive than relational databases but less expressive than logic programming systems such as Prolog. In recent years, deductive databases have found new application in data integration, information extraction, networking, program analysis, security, and cloud computing.

Deductive databases reuse many concepts from logic programming; rules and facts specified in Datalog look very similar to those written in Prolog, but there are some important differences: