Holographic embeddings of knowledge graphs
Open accessAbstract
Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose holographic embeddings (HolE) to learn compositional vector space representations of entire knowledge graphs. The proposed method is related to holographic models of associative memory in that it employs circular correlation to create compositional representations. By using correlation as the compositional operator, HolE can capture rich interactions but simultaneously remains efficient to compute, easy to train, and scalable to very large datasets. Experimentally, we show that holographic embeddings are able to outperform state-of-the-art methods for link prediction on knowledge graphs and relational learning benchmark datasets.
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The Countries dataset contains 2 relations and 272 entities (244 countries, 5 regions and 23 subregions).
“It contains 2 relations and 272 entities (244 countries, 5 regions and 23 subregions).”
From RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space (2019) · cites this paper as Nickel et al., 2016
The Relational Learning section (p. 5) describes two predicates, 244 countries and 5 regions, as claimed. It specifies 22 subregions, not 23, making the stated total 272 inconsistent with the paper's counts (271). (LLM, conf=0.94)
Experimental — accuracy not yet measuredHolE computes circular correlation between head and tail embeddings and then matrix multiplication with the relation embedding.
“HolE [45], instead of using bilinear products, computes circular correlation (denoted by ★ in Table 1) between the embeddings of head and tail entities; then, it performs matrix multiplication with the relation embedding.”
From Knowledge Graph Embedding for Link Prediction (2021) · cites this paper as [45]
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BackgroundMethodA Survey of Data-driven and Knowledge-aware eXplainable AI
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- Authors
- Nickel, M., Rosasco, L., & Poggio, T.
- Affiliations
- Italian Institute of Technology (IT)
- Massachusetts Institute of Technology (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2016-03-02
- Volume
- 30
- Issue
- 1
- Pages
- 1955-1961
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2145544171
- Cited by
- 559
- References
- 3
- Citations by year
- 2025: 1 · 2024: 6 · 2023: 5 · 2022: 9 · 2021: 130 · 2020: 140 · 2019: 134 · 2018: 77 · 2017: 49 · 2016: 8
- FWCI
- 48.32
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- CINECA IRIS Institutial Research Information System (University of Genoa) (submittedVersion, other-oa)
- arXiv (Cornell University)
- National Conference on Artificial Intelligence
- Funders
- National Science Foundation (1231216)
- Companhia Brasileira de Metalurgia e Mineração (CCF-1231216)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- knowledge graphs, entity and relation embeddings, compositional representations, circular correlation, associative memory models, link prediction, relational learning, vector space representation
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