The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
MMKG: Multi-modal Knowledge Graphs
Open accessAbstract
We present Mmkg, a collection of three knowledge graphs that contain both numerical features and (links to) images for all entities as well as entity alignments between pairs of KGs. Therefore, multi-relational link prediction and entity matching communities can benefit from this resource. We believe this data set has the potential to facilitate the development of novel multi-modal learning approaches for knowledge graphs. We validate the utility of Mmkg in the $$\mathtt {sameAs}$$ link prediction task with an extensive set of experiments. These experiments show that the task at hand benefits from learning of multiple feature types.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
(2013) · Cited by 5,726
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., et al. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0981-7 · Cited by 5,107
Freebase: a collaboratively created graph database for structuring human knowledge
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., & Taylor, J. (2008) · Proceedings of the 2008 ACM SIGMOD international conference on Management of data · doi:10.1145/1376616.1376746 · Cited by 4,852
Yago: a core of semantic knowledge
Suchanek, F. M., Kasneci, G., & Weikum, G. (2007) · Proceedings of the 16th international conference on World Wide Web · doi:10.1145/1242572.1242667 · Cited by 2,512
DBpedia: A Nucleus for a Web of Open Data
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., & Ives, Z. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-76298-0_52 · Cited by 2,454
A Review of Relational Machine Learning for Knowledge Graphs
Nickel, M., Murphy, K., Tresp, V., & Gabrilovich, E. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2483592 · Cited by 1,638
Observed versus latent features for knowledge base and text inference
Toutanova, K., & Chen, D. (2015) · Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality · doi:10.18653/v1/w15-4007 · Cited by 1,070
Relation Extraction with Matrix Factorization and Universal Schemas
Riedel, S., Yao, L., McCallum, A., & Marlin, B. M. (2013) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · Cited by 569
Cited by
Unifying Large Language Models and Knowledge Graphs: A Roadmap
Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., & Wu, X. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3352100 · Cited by 1,376
A Comprehensive Survey on Automatic Knowledge Graph Construction
Zhong, L., Wu, J., Li, Q., Peng, H., & Wu, X. (2024) · ACM Computing Surveys · doi:10.1145/3618295 · Cited by 382
Multi-Modal Knowledge Graph Construction and Application: A Survey
Zhu, X., Li, Z., Wang, X., Jiang, X., Sun, P., Wang, X., Xiao, Y., & Yuan, N. J. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3224228 · Cited by 291
Knowledge Editing for Large Language Models: A Survey
Wang, S., Zhu, Y., Liu, H., Zheng, Z., Chen, C., & Li, J. (2025) · ACM Computing Surveys · doi:10.1145/3698590 · Cited by 79
Metadata
- Authors
- Liu, Y., Li, H., Garcia-Duran, A., Niepert, M., Onoro-Rubio, D., & Rosenblum, D. S.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 459-474
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2963870853
- Cited by
- 257
- References
- 20
- FWCI
- 4.71
- Type
- conference paper
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: MMKG: Multi-modal Knowledge Graphs. https://app.cochat.ai/papers/10.1007/978-3-030-21348-0_30
- The paper
- Liu, Y., Li, H., Garcia-Duran, A., Niepert, M., Onoro-Rubio, D., & Rosenblum, D. S. (2019). MMKG: Multi-modal Knowledge Graphs. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-21348-0_30
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
