KnowTD-An Actionable Knowledge Representation System for Thermodynamics
Abstract
We demonstrate that thermodynamic knowledge acquired by humans can be transferred to computers so that the machine can use it to solve thermodynamic problems and produce explainable solutions with a guarantee of correctness. The actionable knowledge representation system that we have created for this purpose is called KnowTD. It is based on an ontology of thermodynamics that represents knowledge of thermodynamic theory, material properties, and thermodynamic problems. The ontology is coupled with a reasoner that sets up the problem to be solved based on user input, extracts the correct, pertinent equations from the ontology, solves the resulting mathematical problem, and returns the solution to the user, together with an explanation of how it was obtained. KnowTD is presently limited to simple thermodynamic problems, similar to those discussed in an introductory course in Engineering Thermodynamics. This covers the basic theory and working principles of thermodynamics. KnowTD is designed in a modular way and is easily extendable.
Cite
@article{arxiv.2407.17169,
title = {KnowTD-An Actionable Knowledge Representation System for Thermodynamics},
author = {Luisa Vollmer and Sophie Fellenz and Fabian Jirasek and Heike Leitte and Hans Hasse},
journal= {arXiv preprint arXiv:2407.17169},
year = {2024}
}
Comments
This document is the unedited Author's version of a Submitted Work that was subsequently accepted for publication in the Journal of Chemical Information and Modeling, copyright \c{opyright} 2024 The Authors, published by American Chemical Society after peer review. To access the final edited and published work see https://pubs.acs.org/doi/full/10.1021/acs.jcim.4c00647