Physics-Based Semantic Reasoning for Function Model Decomposition

Abstract

In graph-based function models, the function verb and flow noun types are usually controlled by vocabularies of standard classes. The grammar is also controlled at different levels of formalism and contribute to reasoning. However, the text written in plain English for the names of the functions and flows is not used for formal reasoning to help with modeling or exploring the design space. This paper presents a formalism for semantic and physics-based reasoning on function model graphs, esp. to automatically decompose black box models and to generate design alternatives using those plain-English texts. A previously established formal language, which ensures that function models are consistent with physics laws, is used as a baseline. Semantic reasoning is added to use the unstructured information of the flow phrases to infer possible means of decomposing the model into a topology connecting appropriate subfunctions and to generate multiple alternative decompositions. A data structure of flow nouns, flow attributes, qualitative value scales, and qualitative physics laws is used as the data representation. An eight-step algorithm manipulates this data for reasoning. The paper shows two validation case studies to demonstrate the workings of the language.

Plan-Based Derivation of General Functional Structures in Product Design

“…In Mao's [13] work, designers should be able to apply function-based design methods even without appropriate knowledge. For this purpose, a formalism of representation and inference is proposed to enable semantic and physical inference from the hidden information in plain-text flow labels.…”

Engineering Knowledge Graph From Patent Database

“…The scope of an existential rule could vary from a specific product domain to engineering as a whole. In the context of input-output transformations, Mao and Sen [51], [52] propose a reasoning algorithm that infers flow types, suitable functions and topologies. The algorithm is supported by an ontology that includes the following: 1) abstract classes for common material and energy flows (e.g., solid, liquid), 2) common flow attributes (e.g., hot, cold) that are categorised into pressure, temperature, and volume, 3) a map between functions (energy, work) and flow attributes using a correlation matrix that was built using qualitative physics.…”

Students’ semantic reasoning characteristics on solving double discount problem

“…Semantic reasoning is the process of thinking to understand the meaning of each stage of the problem-solving process to find a solution to the problem (Davis, 2013;Liang et al, 2016;Littlefield & Rieser, 1993;Murphy, 2015;Prayitno et al, 2018). The study about semantic reasoning has been focused on different things, such as semantic reasoning in computer programs (Patelli et al, 2014;Shi et al, 2015), physics education (Mao & Sen, 2018;Uhden et al, 2012), proofing (Alcock & Inglis, 2008), analyzing errors or obstacles (Kaur & Yeap, 2001;Nesher & Katriel, 1977;Prayitno et al, 2018), and problem-solving (Cirino et al, 2007;Davis, 2013;Liang et al, 2016;Littlefield & Rieser, 1993;Murphy, 2015). Besides, there are also semantic reasoning in word numeracy problems (Davis, 2013), statistical math word problems (Liang et al, 2016), puzzles (Murphy, 2015), word problems with irrelevant information (Littlefield & Rieser, 1993) number word problems (Cirino et al, 2007;Shi et al, 2015), and calculation (Cirino et al, 2007;Patelli et al, 2014).…”