Cross-Domain Inference Using Conceptual Graphs in Context of Laws of Science

Abstract

Knowledge bases, as conceptual graphs, are considered to be brittle as they are highly domain specific. This paper attempts to get some flexibility by predicting the possible nodes, using the other existing graphs. Graph theory principles of maximum common sub-graph and minimum common super-graph for labelled graphs, allow extension of a given conceptual graph. This paper attempts to solve this problem for laws of science. Given a few fundamental equations of two different domains, but similar mathematical structure,equations can be converted to a common set of dummy variables. These transformed equations will be the labels for further set operations. Extending the two graphs using the minimum common super-graph and maximum common super-graph, we then convert these transformed equations back to their original variables. Then, apply constraints to check the feasibility and finalize this extension. Thus we have inferred some part of the knowledge base from other domains.