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The knowledge is stored as statements in mathematical logic. The project lasted from 2004 to 2008. [18] [19] Lycos used Cyc for search term disambiguation, but stopped in 2001. [20] CycSecure was produced in 2002, [21] a network vulnerability assessment tool based on Cyc, with trials at the US STRATCOM Computer Emergency Response Team. [22]
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.
These issues led to the second approach to knowledge engineering: the development of custom methodologies specifically designed to build expert systems. [1] One of the first and most popular of such methodologies custom designed for expert systems was the Knowledge Acquisition and Documentation Structuring (KADS) methodology developed in Europe.
Knowledge retrieval seeks to return information in a structured form, consistent with human cognitive processes as opposed to simple lists of data items. It draws on a range of fields including epistemology (theory of knowledge), cognitive psychology, cognitive neuroscience, logic and inference, machine learning and knowledge discovery, linguistics, and information technology.
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.
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]
S-LOR (Sensor-based Linked Open Rules) semantic reasoner S-LOR is under GNU GPLv3 license.. S-LOR (Sensor-based Linked Open Rules) is a rule-based reasoning engine and an approach for sharing and reusing interoperable rules to deduce meaningful knowledge from sensor measurements.
Rule-based systems provided acceptable computational efficiency while still providing powerful knowledge representation. Also, rules were highly intuitive to knowledge workers. Indeed, one of the data points that encouraged researchers to develop rule-based knowledge representation was psychological research that humans often represented ...