Knowledge Graph Integration in Natural Language Processing for the Smart Cooking

Vadean Vlad, Camelia Lemnaru, Vlad-Andrei Negru, Rodica Potolea

Abstract: In this paper, we propose a solution that leverages knowledge graphs to model and compare cooking recipes, aiming to bridge the gap between free-form user instructions and a structured recipe database. By integrating a hierarchical, domain-specific knowledge graph with inductive graph convolutional networks (for scalable embedding) and a fine-tuned multilingual transformer (for precise information extraction), our approach excels in both efficiency and matching accuracy. We achieve 97.91% accuracy on embedded recipes within the semantic network, yielding a maximum similarity of 0.54 for different versions of the same dish, and reach up to 0.32 similarity for dishes not present in the database. The motivation for this work lies in the need for end users, especially in low-resource or multilingual settings, to retrieve reliable, relevant recipes without manual browsing or query formulation. Our system addresses this by parsing user-provided recipe text in six languages, embedding both user and candidate recipes into a shared vector space, and computing similarity results to return the best match.

External IDs:dblp:conf/iccp2/VladLNP25