Subject. A multimodal knowledge base in the field of economics and finance. Objectives. To develop a methodology and build a prototype of a multimodal knowledge base for supporting expert decision-making. Methods. The study employed methods of systems, comparative, logical, and structural analysis, as well as deep learning techniques. Results. A methodology for constructing a multimodal knowledge base has been developed, along with a prototype demonstrating the application of modern deep learning models in economics and finance. The prototype has been implemented within the framework of the digital expert model concept as an interactive knowledge graph. It is based on collecting and processing scientific publications in economics and finance from the arXiv.org portal for the period from 2003 to 2025. The knowledge base has been enhanced with a module incorporating publications from the electronic library of Lomonosov Moscow State University. Additionally, an interactive graph-based ontology and a question-answering system have been implemented. Conclusions. The multimodal knowledge base, as an element of the digital expert model, enables the formation of a more comprehensive and objective picture of how modern deep learning methods and models are applied in economics and finance — in real time. It also helps improve the quality and depth of scientific and technical expertise.
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