Abstract
Patent users such as government, inventors, and manufacturing organizations strive to identify the directions in which the new technology is advancing. The organization of patent knowledge in maps aims at outlining the boundaries of existing knowledge. This article demonstrates the methodology for simulating alternative knowledge contexts beyond the border of existing knowledge. The process starts with extracting knowledge from patents and applying self-organizing maps for presenting knowledge. The knowledge extraction model was tested earlier on patents from the United States Patent and Trademark Office. A demonstrator tool is then used to perform “what-if” type of analysis/simulation on the clusters in the dataset to see alternative knowledge contexts for the new knowledge “entity”. This may open up new directions and help to plan for the future. The demonstrator tool has been tested earlier on other datasets. The proposed knowledge context simulation shows promise for the future development and applications.
This is a preview of subscription content, log in via an institution to check access.
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
Back, B., Sere, K., Vanharanta, H.: Managing complexity in large data bases using self-organizing maps. Accounting Management and Information Technologies 8(4), 191–210 (1998)
Banks, J., Carson II, J.S., Nelson, L.B., Nicol, D.M.: Discrete-event system simulation, International edition. International Series in Industrial and Systems Engineering. Prentice-Hall, Upper Saddle River (2005)
Eklund, T., Back, B., Vanharanta, H., Visa, A.: Using the self-organizing map as a visualization tool in financial benchmarking. Information Visualization 2(3), 171–181 (2003)
Kantola, J., Piirto, A., Toivonen, J., Vanharanta, H.: Simulation with Occupational Work Role Competences. In: Proceeding of Conference on Grand Challenges in Modeling and Simulation (GCMS 2009), Istanbul, Turkey, July 13-16 (2009)
Kaski, S., Kohonen, T.: Exploratory data analysis by the self-organizing map: structures of welfare and poverty in the world. In: Apostolos, P.N., Refenes, Y.A.-M., Moody, J., Weigend, A. (eds.) Neural Networks in Financial Engineering, pp. 498–507. World Scientific, Singapore (1996)
Kohonen, T.: Self-Organizing Maps. Springer, Leipzig (2001)
Mooers, C.: Descriptors. In: Encyclopedia of Library and Information Science, vol. 7, pp. 31–45. Marcel Dekker (1972)
Pegden, C.D., Shannon, R.E., Sadowski, R.P.: Introduction to Simulation Using SIMAN, 2nd edn. McGraw-Hill, New York (1995)
Segev, A., Kantola, J.: Knowledge Discovery System for Patents. In: 7th International Conference on Knowledge Management (ICKM 2010), Pittsburgh, Pennsylvania, USA, October 22-23 (2010)
Siegel, M., Madnick, S.E.: A metadata approach to resolving semantic conflicts. In: Proceedings of the 17th International Conference on Very Large Data Bases, pp. 133–145 (1991)
Ultsch, A.: Self organized feature planes for monitoring and knowledge acquisition of a chemical process. In: The International Conference on Artificial Neural Networks, pp. 864–867. Springer, London (1993)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag GmbH Berlin Heidelberg
About this paper
Cite this paper
Kantola, J., Segev, A. (2013). Simulating Patent Knowledge Contexts. In: Gaol, F. (eds) Recent Progress in Data Engineering and Internet Technology. Lecture Notes in Electrical Engineering, vol 156. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28807-4_46
Download citation
DOI: https://doi.org/10.1007/978-3-642-28807-4_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28806-7
Online ISBN: 978-3-642-28807-4
eBook Packages: EngineeringEngineering (R0)