3.1.1 Overview

Semantic Repositories are tools that combine the characteristics of database management systems (DBMS) and inference engines. Their major functionality is to support efficient storage, querying and management of structured data. One major difference to DBMS is that Semantic Repositories work with generic physical data models (e.g. graphs). This allows them to easily adopt updates and extensions in the schemata, i.e. in the structure of the data. Another difference is that Semantic Repositories use ontologies as semantic schemata, which allows them to automatically reason about the data.

The two principle strategies for rule-based reasoning are:

• Forward-chaining: to start from the known facts and to perform inference in an inductive fashion. The goals of such reasoning can vary: to answer a particular query or to infer a particular sort of knowledge (e.g. the class taxonomy).
• Backward-chaining: to start from a particular fact or a query and to verify it or get all possible results, using deductive reasoning. In essence, the reasoner decomposes (or transforms) the query (or the fact) into simpler (or alternative) facts, which are available in the KB or can be proven through further recursive transformations.

Imagine a repository, which performs total forward-chaining, i.e. it tries to make sure that after each update to the KB, the inferred closure is computed and made available for query evaluation or retrieval. This strategy is generally known as materialization.