The article develops a set of semantic and network models for analyzing the processes of generating knowledge-intensive technological solutions. The study focuses on quantitative assessment of the influence of exogenous scientific knowledge and external technological search strategies on the effectiveness of patent activity measured through citation-weighted patent applications. A system of variables is introduced, including normalized centrality, intensity and scale of external search, as well as an indicator of demand for scientific knowledge. The methodological basis is the negative binomial regression models with zero expansion, taking into account data overdispersion and error clustering. A systematized taxonomy of knowledge integration risks in innovation systems is proposed, structurally combining five key categories: risks of positioning, subject relations, extraction, innovation and institutional environment. The obtained results allow formalizing semantic links between scientific knowledge and technological development.