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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 10: AAAI-21 Technical Tracks 10

5* Knowledge Graph Embeddings with Projective Transformations

February 1, 2023

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Authors

Mojtaba Nayyeri

Smart Data Analytics Group, University of Bonn, Germany Nature-Inspired Machine Intelligence-InfAI, Dresden, Germany


Sahar Vahdati

Nature-Inspired Machine Intelligence-InfAI, Dresden, Germany


Can Aykul

Smart Data Analytics Group, University of Bonn, Germany


Jens Lehmann

Smart Data Analytics Group, University of Bonn Fraunhofer IAIS, Dresden, German


DOI:

10.1609/aaai.v35i10.17095


Abstract:

Performing link prediction using knowledge graph embedding models has become a popular approach for knowledge graph completion. Such models employ a transformation function that maps nodes via edges into a vector space in order to measure the likelihood of the links. While mapping the individual nodes, the structure of subgraphs is also transformed. Most of the embedding models designed in Euclidean geometry usually support a single transformation type -- often translation or rotation, which is suitable for learning on graphs with small differences in neighboring subgraphs. However, multi-relational knowledge graphs often include multiple subgraph structures in a neighborhood (e.g.~combinations of path and loop structures), which current embedding models do not capture well. To tackle this problem, we propose a novel KGE model 5*E in projective geometry, which supports multiple simultaneous transformations -- specifically inversion, reflection, translation, rotation, and homothety. The model has several favorable theoretical properties and subsumes the existing approaches. It outperforms them on most widely used link prediction benchmarks

Topics: AAAI

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HOW TO CITE:

Mojtaba Nayyeri||Sahar Vahdati||Can Aykul||Jens Lehmann 5* Knowledge Graph Embeddings with Projective Transformations Proceedings of the AAAI Conference on Artificial Intelligence (2021) 9064-9072.

Mojtaba Nayyeri||Sahar Vahdati||Can Aykul||Jens Lehmann 5* Knowledge Graph Embeddings with Projective Transformations AAAI 2021, 9064-9072.

Mojtaba Nayyeri||Sahar Vahdati||Can Aykul||Jens Lehmann (2021). 5* Knowledge Graph Embeddings with Projective Transformations. Proceedings of the AAAI Conference on Artificial Intelligence, 9064-9072.

Mojtaba Nayyeri||Sahar Vahdati||Can Aykul||Jens Lehmann. 5* Knowledge Graph Embeddings with Projective Transformations. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.9064-9072.

Mojtaba Nayyeri||Sahar Vahdati||Can Aykul||Jens Lehmann. 2021. 5* Knowledge Graph Embeddings with Projective Transformations. "Proceedings of the AAAI Conference on Artificial Intelligence". 9064-9072.

Mojtaba Nayyeri||Sahar Vahdati||Can Aykul||Jens Lehmann. (2021) "5* Knowledge Graph Embeddings with Projective Transformations", Proceedings of the AAAI Conference on Artificial Intelligence, p.9064-9072

Mojtaba Nayyeri||Sahar Vahdati||Can Aykul||Jens Lehmann, "5* Knowledge Graph Embeddings with Projective Transformations", AAAI, p.9064-9072, 2021.

Mojtaba Nayyeri||Sahar Vahdati||Can Aykul||Jens Lehmann. "5* Knowledge Graph Embeddings with Projective Transformations". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.9064-9072.

Mojtaba Nayyeri||Sahar Vahdati||Can Aykul||Jens Lehmann. "5* Knowledge Graph Embeddings with Projective Transformations". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 9064-9072.

Mojtaba Nayyeri||Sahar Vahdati||Can Aykul||Jens Lehmann. 5* Knowledge Graph Embeddings with Projective Transformations. AAAI[Internet]. 2021[cited 2023]; 9064-9072.


ISSN: 2374-3468


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