Re: Two challenges related to KR of scientific papers and books

My experience in the W3C Math Working Group (which I set up in the late nineties) showed that math notation leaves a lot of the semantics to the reader based upon conventions shared with the author, as well as definitions in the accompanying natural language text. There were two schools of thought in the Math WG, one focussed on a notation for presentation and the other focussed on the meaning. MathML  was designed as a compromise between both schools.  Math expressed in LaTeX is very much in the former school.

To understand a science paper with embedded math, you need:

	a) to relate the presentation of the math to the meaning
 	b) to understand the accompanying definitions in the text
	c) good background knowledge of shared conventions
	d) mathematical competence in applying math

Anyone interested in this should take a look at the capabilities of Wolfram Mathematica, which is a mature closed source system for working with math. Some open source alternatives include SageMath, Gnu Octave, Jupyter and Maxima, e.g.

> Maxima is a system for the manipulation of symbolic and numerical expressions, including differentiation, integration, Taylor series, Laplace transforms, ordinary differential equations, systems of linear equations, polynomials, sets, lists, vectors, matrices and tensors. 


An AGI agent would go a lot further, and it would be interesting to consider how to train an AGI to give it good mathematical skills, and enable it to improve further by studying the mathematical literature.  

Dave Raggett <dsr@w3.org>