MERGE: A Modal Equilibrium Relational Graph Framework for Multi-Modal Knowledge Graph Completion.

Yuying Shang, Kun Fu, Zequn Zhang, Li Jin, Zinan Liu, Shensi Wang, Shuchao Li

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yuying ShangAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.ORCID 0009-0006-8052-2918

Kun FuAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.

Zequn ZhangAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.ORCID 0000-0002-2138-845X

Li JinAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.ORCID 0000-0001-8833-4862

Zinan LiuAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.

Shensi WangAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.

Shuchao LiAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The multi-modal knowledge graph completion (MMKGC) task aims to automatically mine the missing factual knowledge from the existing multi-modal knowledge graphs (MMKGs), which is crucial in advancing cross-modal learning and reasoning. However, few methods consider the adverse effects caused by different missing modal information in the model learning process. To address the above challenges, we innovatively propose a

Indexed as

graph attention networkinformation integrationknowledge graph representationmulti-modal knowledge graph

Identifiers

PMID39686142

DOI10.3390/s24237605

PMCPMC11644511

What OpenQuestion holds

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LicenceCC BY

Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.