Summary of A Pattern to Align Them All: Integrating Different Modalities to Define Multi-modal Entities, by Gianluca Apriceno et al.
A Pattern to Align Them All: Integrating Different Modalities to Define Multi-Modal Entities
by Gianluca Apriceno, Valentina Tamma, Tania Bailoni, Jacopo de Berardinis, Mauro Dragoni
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed ontology design pattern aims to address the lack of consensus in defining and modeling modalities within Multi-Modal Knowledge Graphs. By separating concerns between an entity’s semantics and its physical information representation, this novel approach can facilitate harmonization and integration across different existing ontologies. The resulting model enables the representation of entities with different manifestations across various media, potentially benefiting intelligent applications in domains such as medicine and digital humanities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to organize data about things that can be represented in different ways, like text, images, or audio. This is important because it helps computers understand relationships between different types of information. Right now, there’s no clear agreement on how to do this, but the proposed system could solve this problem by separating what something means from how it looks or sounds. This could be useful for many applications, like medical research or digital archiving. |
Keywords
» Artificial intelligence » Multi modal » Semantics
