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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Berg-Cross, G., Price, M.E.: Acquiring and Managing Knowledge Using a Conceptual Structures Approach: Introduction and Framework. IEEE Transactions on Systems, Man, and Cybernetics 19(3) (1989)
Bunke, H., Shearer, K.: A graph distance metric based on the maximal common subgraph. Pattern Rec. Lett. 19, 255–259 (1998)
Bunke, H., Jiang, X., Bern, Kandel, A.: On the Minimum Common Supergraph of Two Graphs. Computing 65(1), 13–15 (2000)
Welling, R.: A Performance Analysis on Maximal Common Subgraph Algorithms. In: 15th Twente Student Conference on IT, Enschede, The Netherlands. University of Twente (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Inamdar, S. (2013). Cross-Domain Inference Using Conceptual Graphs in Context of Laws of Science. In: Pfeiffer, H.D., Ignatov, D.I., Poelmans, J., Gadiraju, N. (eds) Conceptual Structures for STEM Research and Education. ICCS 2013. Lecture Notes in Computer Science(), vol 7735. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35786-2_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-35786-2_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35785-5
Online ISBN: 978-3-642-35786-2
eBook Packages: Computer ScienceComputer Science (R0)