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Knowledge bases are categorized into two major types:
- Machine-readable knowledge bases store knowledge in a computer-readable form, usually for the purpose of having automated deductive reasoning applied to them. They contain a set of data, often in the form of rules that describe the knowledge in a logically consistent manner. An ontology can define the structure of stored data - what types of entities are recorded and what their relationships are. Logical operators, such as And (conjunction), Or (disjunction), material implication and negation may be used to build it up from simpler pieces of information. Consequently, classical deduction can be used to reason about the knowledge in the knowledge base. Some machine-readable knowledge bases are used with artificial intelligence, for example as part of an expert system that focuses on a domain like prescription drugs or customs law. Such knowledge bases are also used by the semantic web.
- Human-readable knowledge bases are designed to allow people to retrieve and use the knowledge they contain. They are commonly used to complement a help desk or for sharing information among employees within an organization. They might store troubleshooting information, articles, white papers, user manuals, or answers to frequently asked questions. Typically, a search engine is used to locate information in the system, or users may browse through a classification scheme.
- Commonsense knowledge bases
- Comparison of knowledge base management software
- Enterprise bookmarking
- Text mining
- List of artificial intelligence projects
- Argumentation in Artificial Intelligence by Iyad Rahwan, Guillermo R. Simari