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Knowledge Representation Models Application and Fuzziness in Innovation Projects Assessment

  • Denis A. IstominEmail author
  • Valerii Yu. Stolbov
Conference paper
  • 50 Downloads
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 136)

Abstract

Application of three knowledge representation models are presented: fuzzy production rules, frames, semantic networks. Assessment of innovative project is described as a two-stage process. The first step is processing the project attributes using knowledge models in order to build a set of optimality criteria. The second step is the construction of a comprehensive criterion of optimality and the use of a specific metric for comparing and ranking innovative projects.

The model of production rules using fuzzy inference is considered first. The use of production rules for the logical inference of more complex criteria is proposed.

The frame model of knowledge representation is considered for calculating the parameters of an innovative project, since this model supports calculations in the process of inference. An application of semantic networks is considered with a combination of node-based and arc-based inference styles. Complex rules and relationships between entities in a complex process of evaluating an innovative project are presented using a semantic network. As a last step, the combination of all the evaluated characteristics of an innovative project into one complex criterion of optimality using fuzzy sets is described.

Keywords

Knowledge representation Production rules Fuzzy set Frames Semantic network Innovation project Innovation 

Notes

Acknowledgements

The reported study was funded by the Government of Perm Krai of the Russian Federation (the project «Computer biomechanics and digital technologies in biomedicine»).

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Copyright information

© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Perm National Research Polytechnic UniversityPermRussian Federation

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