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Knowledge Representation Models Application and Fuzziness in Innovation Projects Assessment

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 136)


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.


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

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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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Correspondence to Denis A. Istomin .

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Istomin, D.A., Stolbov, V.Y. (2021). Knowledge Representation Models Application and Fuzziness in Innovation Projects Assessment. In: Antipova, T. (eds) Integrated Science in Digital Age 2020. ICIS 2020. Lecture Notes in Networks and Systems, vol 136. Springer, Cham.

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