The field of semantic technologies is experiencing significant growth in the areas of food and materials research. Recent developments have focused on creating knowledge graphs and ontologies to represent complex data in these domains. These efforts aim to enable machine-interpretable knowledge, facilitate data integration, and support AI-driven discovery and reasoning. Notable advancements include the development of flexible, graph-based modeling approaches and the application of large language models to extract structured data from scientific literature. Noteworthy papers include: Food Data in the Semantic Web, which provides a comprehensive review of nutritional resources and knowledge graphs in the food domain. MatPROV presents a dataset of synthesis procedures extracted from scientific literature using large language models, enabling machine-interpretable synthesis knowledge. AI4DiTraRe introduces a semantic pipeline for constructing a BFO-compliant Chemotion Knowledge Graph, supporting AI-driven discovery and reasoning in chemistry. ResearchPulse formalizes multi-document scientific inference, extracting and aligning motivation, methodology, and experimental results across related papers. Towards an Action-Centric Ontology for Cooking Procedures proposes an extensible domain-specific language for representing recipes as directed action graphs, capturing processes and compositional structure. Language Native Lightly Structured Databases presents a language-native database for composite materials research, capturing lightly structured information from papers and enabling high-fidelity retrieval and generation. Exploring approaches to computational representation and classification of user-generated meal logs demonstrates the potential of machine learning analysis of patient-generated health data to support patient-centered nutrition guidance.