Multimodal Cultural Heritage Knowledge Graph Extension with Language and Vision Models
The article discusses a new approach to extending cultural heritage knowledge graphs using language and vision models. The authors introduce a multimodal knowledge graph, WJoconde, which incorporates both textual and image data related to French cultural heritage. They also present a framework for enhancing knowledge graphs through automated data extraction and validation processes.
- ▪The proposed knowledge graph, WJoconde, integrates textual and image information.
- ▪The authors developed three variants of WJoconde to support downstream research.
- ▪A comprehensive benchmark for knowledge graph completion methods was created using their dataset.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.17669 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
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| Summary source text | contentText |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.17669 (cs) [Submitted on 17 May 2026] Title:Multimodal Cultural Heritage Knowledge Graph Extension with Language and Vision Models Authors:Yang Zhang, Nada Mimouni, Jean-Claude Moissinac, Fayçal Hamdi View a PDF of the paper titled Multimodal Cultural Heritage Knowledge Graph Extension with Language and Vision Models, by Yang Zhang and 3 other authors View PDF HTML (experimental) Abstract:The preservation and interpretation of cultural heritage increasingly rely on digital technologies, among which Knowledge Graphs (KGs) stand out for their ability to structure vast amounts of data. However, the construction and expansion of these KGs often face challenges due to the diverse and complex nature of cultural heritage information.
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