# AspectMMKG: A Multi-modal Knowledge Graph with Aspect-aware Entities

arXiv: https://arxiv.org/abs/2308.04992 · HTML: https://mlsanity.com/paper/2308.04992/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 9 Aug 2023.

## Builds on (most cited first, 14 of 14)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Is ChatGPT a Good NLG Evaluator? A Preliminary Study](https://mlsanity.com/paper/2303.04048/) (2023)
- [MMKG: Multi-Modal Knowledge Graphs](https://mlsanity.com/paper/1903.05485/) (2019)
- [Multi-Modal Knowledge Graph Construction and Application: A Survey](https://mlsanity.com/paper/2202.05786/) (2022)
- [SECTOR: A Neural Model for Coherent Topic Segmentation and Classification](https://mlsanity.com/paper/1902.04793/) (2019)
- [A Survey on Cross-Lingual Summarization](https://mlsanity.com/paper/2203.12515/) (2022)
- [Boosting Entity-aware Image Captioning with Multi-modal Knowledge Graph](https://mlsanity.com/paper/2107.11970/) (2021)
- [VisualSem: A High-quality Knowledge Graph for Vision and Language](https://mlsanity.com/paper/2008.09150/) (2020)
- [ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization](https://mlsanity.com/paper/2202.05599/) (2022)
- [Zero-Shot Cross-Lingual Summarization via Large Language Models](https://mlsanity.com/paper/2302.14229/) (2023)
- [Towards Unifying Multi-Lingual and Cross-Lingual Summarization](https://mlsanity.com/paper/2305.09220/) (2023)
- [Knowledge Enhanced Sports Game Summarization](https://mlsanity.com/paper/2111.12535/) (2021)
- [Automated News Suggestions for Populating Wikipedia Entity Pages](https://mlsanity.com/paper/1703.10344/) (2017)
- [Learning Contextualized Document Representations for Healthcare Answer Retrieval](https://mlsanity.com/paper/2002.00835/) (2020)

## Built on by (most cited first, 5 of 5)

- [Improving the Robustness of Knowledge-Grounded Dialogue via Contrastive Learning](https://mlsanity.com/paper/2401.04361/) (2024)
- [VAT-KG: Knowledge-Intensive Multimodal Knowledge Graph Dataset for Retrieval-Augmented Generation](https://mlsanity.com/paper/2506.21556/) (2025)
- [Multi-modal Knowledge Graph Generation with Semantics-enriched Prompts](https://mlsanity.com/paper/2504.13631/) (2025)
- [Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey](https://mlsanity.com/paper/2607.09666/) (2026)
- [VISPATH: Visual-Intent-Guided Path Reasoning for Multimodal Knowledge Graph Question Answering](https://mlsanity.com/paper/2609.20843/) (2026)

## Abstract (arXiv)

Multi-modal knowledge graphs (MMKGs) combine different modal data (e.g., text and image) for a comprehensive understanding of entities. Despite the recent progress of large-scale MMKGs, existing MMKGs neglect the multi-aspect nature of entities, limiting the ability to comprehend entities from various perspectives. In this paper, we construct AspectMMKG, the first MMKG with aspect-related images by matching images to different entity aspects. Specifically, we collect aspect-related images from a knowledge base, and further extract aspect-related sentences from the knowledge base as queries to retrieve a large number of aspect-related images via an online image search engine. Finally, AspectMMKG contains 2,380 entities, 18,139 entity aspects, and 645,383 aspect-related images. We demonstrate the usability of AspectMMKG in entity aspect linking (EAL) downstream task and show that previous EAL models achieve a new state-of-the-art performance with the help of AspectMMKG. To facilitate the research on aspect-related MMKG, we further propose an aspect-related image retrieval (AIR) model, that aims to correct and expand aspect-related images in AspectMMKG. We train an AIR model to learn the relationship between entity image and entity aspect-related images by incorporating entity image, aspect, and aspect image information. Experimental results indicate that the AIR model could retrieve suitable images for a given entity w.r.t different aspects.