Retrieving external knowledge to Augment Generations of downstream task solutions (RAGs) has become a standard practice in powering knowledge-intensive applications. However, real-world knowledge often manifests in heterogeneous yet distinctive structures (e.g., tabular schemas, social networks, and document trees), the effective modeling of which demands specialized modeling, practical engineering, and domain expertise. Meanwhile, adopting RAGs in high-stakes scenarios underscores rigorous safety considerations. Despite the importance of this structural perspective, the current landscape remains fragmented. Moreover, few approaches adequately consider how structured knowledge shapes RAG's safety. Against this backdrop, our tutorial offers a structural perspective on RAGs. We begin by overviewing structured RAGs across their full lifecycle, highlighting their canonical designs. We then examine how design principles can be specialized for different knowledge structures, showcasing their unique applications and security attack/defense strategies. The tutorial slide is available https://kindlab-fly.github.io/tutorials/WSDM26/

Large Language Models (LLMs) have been increasingly studied as neural knowledge bases for supporting knowledge-intensive applications such as question answering and fact checking. However, the structural organization of their knowledge remains unexplored. Inspired by cognitive neuroscience findings, such as semantic clustering and priming, where knowing one fact increases the likelihood of recalling related facts, we investigate an analogous knowledge homophily pattern in LLMs. To this end, we map LLM knowledge into a graph representation through knowledge checking at both the triplet and entity levels. After that, we analyze the knowledgeability relationship between an entity and its neighbors, discovering that LLMs tend to possess a similar level of knowledge about entities positioned closer in the graph. Motivated by this homophily principle, we propose a Graph Neural Network (GNN) regression model to estimate entity-level knowledgeability scores for triplets by leveraging their neighborhood scores. The predicted knowledgeability enables us to prioritize checking less well-known triplets, thereby maximizing knowledge coverage under the same labeling budget. This not only improves the efficiency of active labeling for fine-tuning to inject knowledge into LLMs but also enhances multi-hop path retrieval in reasoning-intensive question answering.