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Cited by 95Advanced Graph Neural NetworksTopic ModelingSemantic Web and Ontologies

Incorporating Literals into Knowledge Graph Embeddings

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Kristiadi, Agustinus, Khan, Mohammad Asif, Lukovnikov, Denis, Lehmann, Jens, Fischer, Asja·2019·Lecture Notes in Computer Science
DOI 10.1007/978-3-030-30793-6_20
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  • For Bilinear+TR and LiteralE, adding entity type, text and other information helps to improve performance.

    “In terms of Bilinear+TR [78] and LiteralE [115], we can see that adding the entity type, text and other information helps to improve performance.”

    From A Survey on Knowledge Graph Embeddings for Link Prediction (2021) · cites this paper as [115]

    Section 6.1 (p. 7) reports that LiteralE improves link-prediction performance when it incorporates literal information, and Section 6.3 (p. 7) reports a further improvement from adding text literals. This source does not establish a performance gain from adding entity-type information. The separate Bilinear+TR citation is not being judged here. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • LiteralE introduces literature information, giving priority to numerical literature.

    “LiteralE [115] introduces the literature information, giving priority to numerical literature.”

    From A Survey on Knowledge Graph Embeddings for Link Prediction (2021) · cites this paper as [115]

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Research works printed but not linked: 21

  1. [Bollacker et al. , 2008] Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. Freebase: a collaboratively created graph database for structuring human knowledge. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data , pages 1247-1250. AcM, 2008.
  2. [Bordes et al. , 2013] Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. Translating embeddings for modeling multi-relational data. In Advances in neural information processing systems , pages 27872795, 2013.
  3. [Cho et al. , 2014] Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. Learning phrase representations using rnn encoder-decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) , pages 1724-1734, Doha, Qatar, October 2014. Association for Computational Linguistics.
  4. [Dettmers et al. , 2018] Tim Dettmers, Minervini Pasquale, Stenetorp Pontus, and Sebastian Riedel. Convolutional 2d knowledge graph embeddings. In Proceedings of the 32th AAAI Conference on Artificial Intelligence , February 2018.
  5. [Dong et al. , 2014] Xin Dong, Evgeniy Gabrilovich, Geremy Heitz, Wilko Horn, Ni Lao, Kevin Murphy, Thomas Strohmann, Shaohua Sun, and Wei Zhang. Knowledge vault: A web-scale approach to probabilistic knowledge fusion. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining , pages 601-610. ACM, 2014.
  6. [Garcia-Duran and Niepert, 2017] Alberto Garcia-Duran and Mathias Niepert. KBLRN: End-to-end learning of knowledge base representations with latent, relational, and numerical features. arXiv preprint arXiv:1709.04676 , 2017. · arXiv
  7. [Kingma and Ba, 2015] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In 3rd International Conference for Learning Representations . ICLR, 2015.
  8. [Le and Mikolov, 2014] Quoc Le and Tomas Mikolov. Distributed representations of sentences and documents. In International conference on machine learning , pages 1188-1196, 2014.
  9. [Lehmann et al. , 2015] Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick Van Kleef, S¨ oren Auer, et al. DBpedia-a large-scale, multilingual knowledge base extracted from wikipedia. Semantic Web , 6(2):167-195, 2015.
  10. [Mahdisoltani et al. , 2014] Farzaneh Mahdisoltani, Joanna Biega, and Fabian Suchanek. Yago3: A knowledge base from multilingual wikipedias. In 7th Biennial Conference on Innovative Data Systems Research . CIDR Conference, 2014.
  11. [Nickel et al. , 2016] Maximilian Nickel, Kevin Murphy, Volker Tresp, and Evgeniy Gabrilovich. A review of relational machine learning for knowledge graphs. Proceedings of the IEEE , 104(1):11-33, 2016.
  12. [Ruobing Xie, 2017] Huanbo Luan Maosong Sun Ruobing Xie, Zhiyuan Liu. Image-embodied knowledge representation learning. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17 , pages 3140-3146, 2017.
  13. [Sun et al. , 2017] Zequn Sun, Wei Hu, and Chengkai Li. Crosslingual entity alignment via joint attribute-preserving embedding. In International Semantic Web Conference , pages 628-644. Springer, 2017.
  14. [Tay et al. , 2017] Yi Tay, Luu Anh Tuan, Minh C Phan, and Siu Cheung Hui. Multi-task neural network for non-discrete attribute prediction in knowledge graphs. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management , pages 1029-1038. ACM, 2017.
  15. [Thoma et al. , 2017] Steffen Thoma, Achim Rettinger, and Fabian Both. Towards holistic concept representations: Embedding relational knowledge, visual attributes, and distributional word semantics. In International Semantic Web Conference , pages 694710. Springer, 2017.
  16. [Toutanova and Chen, 2015] Kristina Toutanova and Danqi Chen. Observed versus latent features for knowledge base and text inference. In Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality , pages 57-66, 2015.
  17. [Toutanova et al. , 2015] Kristina Toutanova, Danqi Chen, Patrick Pantel, Hoifung Poon, Pallavi Choudhury, and Michael Gamon. Representing text for joint embedding of text and knowledge bases. In EMNLP , volume 15, pages 1499-1509, 2015.
  18. [Tu et al. , 2017] Cunchao Tu, Han Liu, Zhiyuan Liu, and Maosong Sun. Cane: Context-aware network embedding for relation modeling. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , volume 1, pages 1722-1731, 2017.
  19. [Wu and Wang, 2018] Yanrong Wu and Zhichun Wang. Knowledge graph embedding with numeric attributes of entities. In Proceedings of The Third Workshop on Representation Learning for NLP , pages 132-136, 2018.
  20. [Xie et al. , 2016] Ruobing Xie, Zhiyuan Liu, Jia Jia, Huanbo Luan, and Maosong Sun. Representation learning of knowledge graphs with entity descriptions. In AAAI , pages 2659-2665, 2016.
  21. [Xu et al. , 2016] Jiacheng Xu, Kan Chen, Xipeng Qiu, and Xuanjing Huang. Knowledge graph representation with jointly structural and textual encoding. In IJCAI , 2016.

Cited by

Sorted by citations

Authors
Kristiadi, A., Khan, M. A., Lukovnikov, D., Lehmann, J., & Fischer, A.
Affiliations
  • University of Tübingen (DE)
  • University of Bonn (DE)
  • Ruhr University Bochum (DE)
Corresponding author
Kristiadi, Agustinus
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2019
Pages
347-363
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-030-30793-6_20
OpenAlex
W2786433705
Cited by
95
References
1
Citations by year
2026: 5 · 2025: 11 · 2024: 9 · 2023: 20 · 2022: 16 · 2021: 16 · 2020: 11 · 2019: 4 · 2018: 3
FWCI
4.50
Type
conference paper
Language
en
Open access
Licence
www.springer.com/tdm (tdm)
Locations
  • Lecture notes in computer science (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
Keywords
knowledge graph embedding, entity attributes, link prediction, latent feature models, entity embeddings

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