Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE-Reference-Cited by-同舟云学术

Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE

Published:2023-12-05 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 12th Knowledge Capture Conference 2023
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Author:

Hubert Nicolas¹^ORCID,Paulheim Heiko²^ORCID,Monnin Pierre³^ORCID,Brun Armelle⁴^ORCID,Monticolo Davy⁵^ORCID

Affiliation:

1. BIRD, Université de Lorraine, CNRS, LORIA, France and Université de Lorraine, ERPI, France

2. University of Mannheim, Germany

3. Université Côte d'Azur, Inria, CNRS, I3S, France

4. BIRD, Université de Lorraine, CNRS, LORIA, France

5. Université de Lorraine, ERPI, France

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3587259.3627550

Reference25 articles.

1. TuckER: Tensor Factorization for Knowledge Graph Completion

2. Poisoning Knowledge Graph Embeddings via Relation Inference Patterns

3. Antoine Bordes, Nicolas Usunier, Alberto García-Durán, Jason Weston, and Oksana Yakhnenko. 2013. Translating Embeddings for Modeling Multi-relational Data. In Conf. on Neural Information Processing Systems (NeurIPS). 2787–2795.

4. Summarizing semantic graphs: a survey

5. Haochen Chen Bryan Perozzi Yifan Hu and Steven Skiena. 2018. HARP: Hierarchical Representation Learning for Networks. In Proc. of the 32nd AAAI Conf. on Artificial Intelligence (AAAI-18) the 30th innovative Applications of Artificial Intelligence (IAAI-18) and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18) New Orleans Louisiana USA February 2-7 2018. AAAI Press 2127–2134.

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