The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
A Review of Relational Machine Learning for Knowledge Graphs
Open accessAbstract
Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be “trained” on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph). In particular, we discuss two fundamentally different kinds of statistical relational models, both of which can scale to massive data sets. The first is based on latent feature models such as tensor factorization and multiway neural networks. The second is based on mining observable patterns in the graph. We also show how to combine these latent and observable models to get improved modeling power at decreased computational cost. Finally, we discuss how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web. To this end, we also discuss Google's knowledge vault project as an example of such combination.
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Cited by
A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3070843 · Cited by 2,947
Knowledge Graph Embedding: A Survey of Approaches and Applications
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Convolutional 2D Knowledge Graph Embeddings
Dettmers, T., Minervini, P., Stenetorp, P., & Riedel, S. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11573 · Cited by 2,486
Complex Embeddings for Simple Link Prediction
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., & Bouchard, G. (2016) · arXiv · doi:10.48550/arxiv.1606.06357 · Cited by 2,100
Language Models as Knowledge Bases?
Petroni, F., Rocktäschel, T., Riedel, S., Lewis, P., Bakhtin, A., Wu, Y., & Miller, A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1250 · Cited by 1,834
Metadata
- Authors
- Nickel, M., Murphy, K., Tresp, V., & Gabrilovich, E.
- Venue
- Proceedings of the IEEE
- Published
- 2016-01
- Volume
- 104
- Issue
- 1
- Pages
- 11-33
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W1529533208
- Cited by
- 1,638
- References
- 13
- FWCI
- 84.46
- Type
- journal article
- Language
- en
- Open access
- green
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