MMCOMET: A Large-Scale Multimodal Commonsense Knowledge Graph for Contextual Reasoning
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866912934594084864 |
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| author | Wang, Eileen Arnaout, Hiba Pratama, Dhita Yang, Shuo Liu, Dangyang Yang, Jie Poon, Josiah Pan, Jeff Han, Caren |
| author_facet | Wang, Eileen Arnaout, Hiba Pratama, Dhita Yang, Shuo Liu, Dangyang Yang, Jie Poon, Josiah Pan, Jeff Han, Caren |
| contents | We present MMCOMET, the first multimodal commonsense knowledge graph (MMKG) that integrates physical, social, and eventive knowledge. MMCOMET extends the ATOMIC2020 knowledge graph to include a visual dimension, through an efficient image retrieval process, resulting in over 900K multimodal triples. This new resource addresses a major limitation of existing MMKGs in supporting complex reasoning tasks like image captioning and storytelling. Through a standard visual storytelling experiment, we show that our holistic approach enables the generation of richer, coherent, and contextually grounded stories than those produced using text-only knowledge. This resource establishes a new foundation for multimodal commonsense reasoning and narrative generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_01055 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MMCOMET: A Large-Scale Multimodal Commonsense Knowledge Graph for Contextual Reasoning Wang, Eileen Arnaout, Hiba Pratama, Dhita Yang, Shuo Liu, Dangyang Yang, Jie Poon, Josiah Pan, Jeff Han, Caren Artificial Intelligence We present MMCOMET, the first multimodal commonsense knowledge graph (MMKG) that integrates physical, social, and eventive knowledge. MMCOMET extends the ATOMIC2020 knowledge graph to include a visual dimension, through an efficient image retrieval process, resulting in over 900K multimodal triples. This new resource addresses a major limitation of existing MMKGs in supporting complex reasoning tasks like image captioning and storytelling. Through a standard visual storytelling experiment, we show that our holistic approach enables the generation of richer, coherent, and contextually grounded stories than those produced using text-only knowledge. This resource establishes a new foundation for multimodal commonsense reasoning and narrative generation. |
| title | MMCOMET: A Large-Scale Multimodal Commonsense Knowledge Graph for Contextual Reasoning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.01055 |