# VisualSem: A High-quality Knowledge Graph for Vision and Language

arXiv: https://arxiv.org/abs/2008.09150 · HTML: https://mlsanity.com/paper/2008.09150/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2020.

## Built on by (most cited first, 30 of 30)

- [Multi-Modal Knowledge Graph Construction and Application: A Survey](https://mlsanity.com/paper/2202.05786/) (2022)
- [Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey](https://mlsanity.com/paper/2402.05391/) (2024)
- [Contrastive Language-Image Pre-Training with Knowledge Graphs](https://mlsanity.com/paper/2210.08901/) (2022)
- [Findings of the TSAR-2022 Shared Task on Multilingual Lexical Simplification](https://mlsanity.com/paper/2302.02888/) (2023)
- [UKnow: A Unified Knowledge Protocol with Multimodal Knowledge Graph Datasets for Reasoning and Vision-Language Pre-Training](https://mlsanity.com/paper/2302.06891/) (2023)
- [Cross-modal Retrieval for Knowledge-based Visual Question Answering](https://mlsanity.com/paper/2401.05736/) (2024)
- [Construction of Knowledge Graphs: State and Challenges](https://mlsanity.com/paper/2302.11509/) (2023)
- [Endowing Language Models with Multimodal Knowledge Graph Representations](https://mlsanity.com/paper/2206.13163/) (2022)
- [Kuaipedia: a Large-scale Multi-modal Short-video Encyclopedia](https://mlsanity.com/paper/2211.00732/) (2022)
- [Sample-Efficient Learning of Novel Visual Concepts](https://mlsanity.com/paper/2306.09482/) (2023)
- [Digital Twin Generation from Visual Data: A Survey](https://mlsanity.com/paper/2504.13159/) (2025)
- [A Unified Continuous Learning Framework for Multi-modal Knowledge Discovery and Pre-training](https://mlsanity.com/paper/2206.05555/) (2022)
- [AspectMMKG: A Multi-modal Knowledge Graph with Aspect-aware Entities](https://mlsanity.com/paper/2308.04992/) (2023)
- [VCD: A Dataset for Visual Commonsense Discovery in Images](https://mlsanity.com/paper/2402.17213/) (2024)
- [FineQuest: Adaptive Knowledge-Assisted Sports Video Understanding via Agent-of-Thoughts Reasoning](https://mlsanity.com/paper/2509.11796/) (2025)
- [Knowledge Completes the Vision: A Multimodal Entity-aware Retrieval-Augmented Generation Framework for News Image Captioning](https://mlsanity.com/paper/2511.21002/) (2025)
- [Domain Adaptation for Large-Vocabulary Object Detectors](https://mlsanity.com/paper/2401.06969/) (2024)
- [VAT-KG: Knowledge-Intensive Multimodal Knowledge Graph Dataset for Retrieval-Augmented Generation](https://mlsanity.com/paper/2506.21556/) (2025)
- [Multimodal Entity Tagging with Multimodal Knowledge Base](https://mlsanity.com/paper/2201.00693/) (2021)
- [Multilingual Multimodality: A Taxonomical Survey of Datasets, Techniques, Challenges and Opportunities](https://mlsanity.com/paper/2210.16960/) (2022)
- [wav2graph: A Framework for Supervised Learning Knowledge Graph from Speech](https://mlsanity.com/paper/2408.04174/) (2024)
- [Multi-Granularity Context-Enhanced RAG over Multimodal Knowledge Graphs](https://mlsanity.com/paper/2608.25986/) (2026)
- [Lexical Simplification using multi level and modular approach](https://mlsanity.com/paper/2302.01823/) (2023)
- [Farspredict: A benchmark dataset for link prediction](https://mlsanity.com/paper/2303.14647/) (2023)
- [Light Up the Shadows: Enhance Long-Tailed Entity Grounding with Concept-Guided Vision-Language Models](https://mlsanity.com/paper/2406.10902/) (2024)
- [MMPKUBase: A Comprehensive and High-quality Chinese Multi-modal Knowledge Graph](https://mlsanity.com/paper/2408.01679/) (2024)
- [Multi-modal Knowledge Graph Generation with Semantics-enriched Prompts](https://mlsanity.com/paper/2504.13631/) (2025)
- [VisKnow: Constructing Visual Knowledge Base for Object Understanding](https://mlsanity.com/paper/2512.08221/) (2025)
- [MMCOMET: A Large-Scale Multimodal Commonsense Knowledge Graph for Contextual Reasoning](https://mlsanity.com/paper/2603.01055/) (2026)
- [VISPATH: Visual-Intent-Guided Path Reasoning for Multimodal Knowledge Graph Question Answering](https://mlsanity.com/paper/2609.20843/) (2026)

## Abstract (arXiv)

An exciting frontier in natural language understanding (NLU) and generation (NLG) calls for (vision-and-) language models that can efficiently access external structured knowledge repositories. However, many existing knowledge bases only cover limited domains, or suffer from noisy data, and most of all are typically hard to integrate into neural language pipelines. To fill this gap, we release VisualSem: a high-quality knowledge graph (KG) which includes nodes with multilingual glosses, multiple illustrative images, and visually relevant relations. We also release a neural multi-modal retrieval model that can use images or sentences as inputs and retrieves entities in the KG. This multi-modal retrieval model can be integrated into any (neural network) model pipeline. We encourage the research community to use VisualSem for data augmentation and/or as a source of grounding, among other possible uses. VisualSem as well as the multi-modal retrieval models are publicly available and can be downloaded in this URL: https://github.com/iacercalixto/visualsem