Searching for Geometric Theorems Using Features Retrieved from Diagrams
Searching for knowledge objects from knowledge bases is a basic problem that need be investigated in the context of knowledge management. For geometric knowledge objects such as theorems, natural language representations may not exactly reveal the features and structures of geometric entities, and that is why keyword-based searching is often unsatisfactory. To obtain high-quality results of searching for theorems in plane Euclidean geometry with images of diagrams as input, we propose a method using geometric features retrieved from the images. The method consists of four main steps: (1) retrieve geometric features, with formal representations, from an input image of a diagram D using pattern recognition and numerical verification; (2) construct a graph G corresponding to D from the retrieved features and weaken G to match graphs produced from formal representations of theorems in OpenGeo, an open geometric knowledge base; (3) calculate the degree of relevance between G and the graph for each theorem found from OpenGeo; (4) rank the resulting theorems according to their degrees of relevance. This method, based on graph matching, takes into account the structures of diagrams and works effectively. It is capable of finding out theorems of higher degree of relevance and may have potential applications in geometric knowledge management and education.
KeywordsTheorem searching Graph matching Degree of relevance Knowledge management
This work has been supported by the project SKLSDE-2015ZX-18 and the NSFC project 11371047.
- 6.Einwohner, T.H., Fateman, R.J.: Searching techniques for integral tables. In: 1995 International Symposium on Symbolic and Algebraic Computation, pp. 133–139. ACM (1995)Google Scholar
- 7.Giugno, R., Shasha, D.: GraphGrep: a fast and universal method for querying graphs. In: 16th International Conference in Pattern Recognition, pp. 112–115. IEEE (2002)Google Scholar
- 8.Grottke, S., Jeschke, S., Natho, N., Seiler, R.: mArachna: a classification scheme for semantic retrieval in elearning environments in mathematics. Recent Research Developments in Learning Technologies (2005)Google Scholar
- 9.Haralambous, Y., Quaresma, P.: Querying geometric figures using a controlled language, ontological graphs and dependency lattices. In: Watt, S.M., Davenport, J.H., Sexton, A.P., Sojka, P., Urban, J. (eds.) Intelligent Computer Mathematics (CICM 2014). LNAI, vol. 8543, pp. 298–311. Springer, Heidelberg (2014)Google Scholar
- 10.Hashimoto, H., Hijikata, Y., Nishida, S.: Incorporating breadth first search for indexing MathML objects. In: The International Conference on Systems, Man and Cybernetics, pp. 3519–3523. IEEE (2008)Google Scholar
- 11.Kamali, S., Tompa, F.W.: Improving mathematics retrieval. In: Towards a Digital Mathematics Library, pp. 37–48 (2009)Google Scholar
- 12.Kamali, S., Tompa, F.W.: A new mathematics retrieval system. In: 19th ACM International Conference on Information and Knowledge Management, pp. 1413–1416. ACM (2010)Google Scholar
- 14.Kumar, S., Manjeet, S., Avik, D.: OWL-based ontology indexing and retrieving algorithms for Semantic Search Engine. In: The 7th International Conference of Computing and Convergence Technology, pp. 1135–1140. IEEE (2012)Google Scholar
- 16.Singhal, A.: Modern information retrieval: a brief overview. IEEE Data Eng. Bull. 24, 35–43 (2001)Google Scholar
- 18.Wang, D., Chen, X., An, W., et al.: OpenGeo: an open geometric knowledge base. In: Hong, H., Yap, C. (eds.) Mathematical Software (ICMS 2014). LNCS, vol. 8592, pp. 240–245. Springer, Heidelberg (2014)Google Scholar
- 19.Wical, K.: Concept knowledge base search and retrieval system. Patent 6,038,560. U.S (2000)Google Scholar