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The ideal representation for a knowledge base is an object model (often called an ontology in artificial intelligence literature) with classes, subclasses and instances. Early expert systems also had little need for multiple users or the complexity that comes with requiring transactional properties on data.
A knowledge-based system (KBS) is a computer program that reasons and uses a knowledge base to solve complex problems. Knowledge-based systems were the focus of early artificial intelligence researchers in the 1980s. The term can refer to a broad range of systems.
Knowledge representation and reasoning (KRR, KR&R, or KR²) is a field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks, such as diagnosing a medical condition or having a natural-language dialog.
In knowledge representation and reasoning, a knowledge graph is a knowledge base that uses a graph-structured data model or topology to represent and operate on data. Knowledge graphs are often used to store interlinked descriptions of entities – objects, events, situations or abstract concepts – while also encoding the free-form semantics ...
Cyc (pronounced / ˈ s aɪ k / SYKE) is a long-term artificial intelligence project that aims to assemble a comprehensive ontology and knowledge base that spans the basic concepts and rules about how the world works. Hoping to capture common sense knowledge, Cyc focuses on implicit knowledge.
In the field of artificial intelligence, an inference engine is a software component of an intelligent system that applies logical rules to the knowledge base to deduce new information. The first inference engines were components of expert systems. The typical expert system consisted of a knowledge base and an inference engine.
One of the most well known of these early Lisp knowledge-base tools was the Knowledge Engineering Environment (KEE) from Intellicorp. KEE provided a full Frame language with multiple inheritance, slots, triggers, default values, and a rule engine that supported backward and forward chaining.
Commonsense knowledge can underpin a commonsense reasoning process, to attempt inferences such as "You might bake a cake because you want people to eat the cake." A natural language processing process can be attached to the commonsense knowledge base to allow the knowledge base to attempt to answer questions about the world. [2]