Open-world Question Answering (OW-QA) over knowledge graphs (KGs) aims to answer questions over incomplete or evolving KGs. Traditional KGQA assumes a closed world where answers must exist in the KG, limiting real-world applicability. In contrast, open- world QA requires inferring missing knowledge based on graph structure and context. Large language models (LLMs) excel at language understanding but lack structured reasoning. Graph neural networks (GNNs) model graph topology but struggle with semantic interpretation. Existing systems integrate LLMs with GNNs or graph retrievers. Some support open-world QA but rely on structural embeddings without semantic grounding. Most assume observed paths or complete graphs, making them unreliable under missing links or multi-hop reasoning. We present GLOW, a hybrid system that combines a pre-trained GNN and an LLM for open-world KGQA. The GNN predicts top-k candidate answers from the graph structure. These, along with relevant KG facts, are serialized into a structured prompt (e.g., triples and candidates) to guide the LLM’s reasoning. This enables joint reasoning over symbolic and semantic signals, without relying on retrieval or fine-tuning. To evaluate generalization, we introduce GLOW-BENCH, a 1,000-question benchmark over incomplete KGs across diverse domains. GLOW outperforms existing LLM–GNN systems on standard benchmarks and GLOW-BENCH, achieving up to 53.3% and an average 38% improvement.

Automating Exploratory Data Analysis (EDA) is critical for accelerating the workflow of data scientists. While Large Language Models (LLMs) offer a promising solution, current LLM-only approaches often exhibit limited accuracy and code reliability on less-studied or private datasets. Moreover, their effectiveness significantly diminishes with open-source LLMs compared to proprietary ones, limiting their usability in enterprises that prefer local models for privacy and cost. To address these limitations, we introduce RAGvis: a novel two-stage graph-guided Retrieval-Augmented Generation (RAG) framework. RAGvis first builds a base knowledge graph (KG) of EDA notebooks and enriches it with structured EDA operation semantics. These semantics are extracted by an LLM guided by our empirically-developed EDA operations taxonomy. Second, in the online generation stage for new datasets, RAGvis retrieves relevant operations from the KG, aligns them to the dataset’s structure, refines them with LLM reasoning, and then employs a self-correcting agent to generate executable Python code. Experiments on two benchmarks demonstrate that RAGvis significantly improves code executability (pass rate), semantic accuracy, and visual quality in generated operations. This enhanced performance is achieved with substantially lower token usage compared to LLM-only baselines. Notably, our approach enables smaller, open-source LLMs to match the performance of proprietary models, presenting a reliable and cost-effective pathway for automated EDA code generation.