Semantic and Qualitative Physics-Based Reasoning on Plain-English Flow Terms for Generating Function Model Alternatives

Abstract

In graph-based function models, the function verbs and flow nouns are usually chosen from predefined vocabularies. The vocabulary class definitions, combined with function modeling grammars defined at various levels of formalism, enable function-based reasoning. However, the text written in plain English for the names of the functions and flows is presently not exploited for formal reasoning. This paper presents a formalism (representation and reasoning) to support semantic and physics-based reasoning on the information hidden in the plain-English flow terms, especially for automatically decomposing black box function models, and to generate multiple design alternatives. First, semantic reasoning infers the changes of flow types, flow attributes, and the direction of those changes between the input and output flows attached to the black box. Then, a representation of qualitative physics is used to determine the material and energy exchanges between the flows and the function features needed to achieve them. Finally, a topological reasoning is used to infer multiple options of composing those function features into topologies and to thus generate multiple alternative decompositions of the functional black box. The data representation formalizes flow phases, flow attributes, qualitative value scales for the attributes, and qualitative physics laws. An eight-step algorithm manipulates these data for reasoning. This paper shows four validation case studies to demonstrate the workings of this formalism.

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.…”

“…For such inferences to be enabled, a supporting ontology (e.g., Mao and Sen [51]) must be built. Such an ontology should include classes (e.g., entity classification in Google Knowledge Graph 14 ) and attributes that characterize each entity and relationship, e.g., attributes of an analog-digital converter.…”