Of course, we can make these expressions a bit more complicated for instance, the concentration could also depend on some combination of other substances a 1 and a 2: e.g., c = 5 a 1 + a 2 + 10. When modeling some aspect of a neural system, we can represent static variables with algebraic expressions, e.g., the concentration c of some chemical in the brain is 10 units, c = 10. Finally, we compare the various natural language reasoning systems and methods. Next, we describe the Watson system for natural language question answering and the WatsonPaths system built on top of Watson, which use unstructured information as a source of knowledge for reasoning. Then we discuss automated reasoning directly using natural language. We then review several restricted versions of English for reasoning, including reasoning in the event calculus. First, we describe attempts to use natural language as a programming language. We discuss the use of unstructured information, specifically natural language text, for commonsense reasoning. Given that there is so much unstructured information available, the question arises of how we might be able to use it for automated commonsense reasoning. Much of the world’s data is unstructured. Because unstructured information is so easy for humans to create, it is abundant. Examples of unstructured information include natural language text, audio, images, and video. Unstructured information is information that is not represented in such a form and is difficult for computers to understand. Structured information is information that computers can easily understand and reason with, such as algebraic expressions, logical formulas, frames, and database tables. Mueller, in Commonsense Reasoning (Second Edition), 2015 Abstract
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