arXiv:2606. 09648v1 Announce Type: cross Abstract: Multi-modal data management has emerged as a central research topic in the database community, spanning data integration, semantic query processing, and data quality assessment.
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arXiv:2504. 20734v5 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has shown substantial promise in improving factual accuracy by grounding model responses with external knowledge relevant to queries.
arXiv:2607. 25959v1 Announce Type: cross Abstract: Wikipedia and Wikidata are widely used for information access, LLM pre-training, and retrieval-augmented generation.
The paper introduces data stories as narrative documents that combine explanatory text, images, and executable SPARQL queries with visualized results to make cultural‑heritage knowledge graphs more accessible. It describes how these stories guide users through unfamiliar graphs, create reproducible narratives, and uncover hidden data‑quality issues. The authors present LODEON, an authoring platform with Sparnatural and AI‑assisted tools, and report early positive feedback from seminars and workshops.
arXiv:2510.26861v4 Announce Type: replace-cross Abstract: Multimodal retrieval systems are expected to operate in a semantic space, agnostic to the language or cultural origin of the query. In practi...
The paper introduces a new perspective on entity rarity in multimodal entity linking by using knowledge‑graph structural metrics instead of popularity metrics, revealing many rare entities previously overlooked. Experiments show that state‑of‑the‑art models suffer a 15.4–39.9% accuracy drop on these rare‑entity slices. The authors propose a training‑free framework that combines reasoning and retrieval with a vision‑language model, achieving a 6.9% overall accuracy gain and up to 23.3% improvement on rare entities, and release a new benchmark MERLIN‑Rare for focused evaluation.
We introduce ChinaHeritaQA, a multimodal benchmark dataset for evaluating the cultural reasoning abilities of vision-language models (VLMs) on UNESCO World Heritage sites in China. The dataset comprises 2,279 in-the-wild images paired with 14,133 bilingual (Chinese/English) multiple-choice QA pairs spanning seven cognitive dimensions, from basic identity recognition to historical periodization and architectural analysis.