KnowTD-An Actionable Knowledge Representation System for Thermodynamics

TL;DR

KnowTD is an ontology-based system that transfers human thermodynamic knowledge to computers, enabling them to solve problems and provide explainable, correct solutions for introductory thermodynamics tasks.

Contribution

It introduces KnowTD, a modular, extendable knowledge representation system that combines thermodynamic ontology with reasoning capabilities for problem solving and explanation.

Findings

Successfully solves simple thermodynamic problems

Provides explainable solutions with correctness guarantees

Modular design allows easy extension

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.