CoChat
Sign Up
Cited by 923Advanced Graph Neural NetworksTopic ModelingMultimodal Machine Learning Applications

A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network

Open access
Nguyen, Dai Quoc, Nguyen, Tu Dinh, Nguyen, Dat Quoc, Phung, Dinh·2018·Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
DOI 10.18653/v1/n18-2053
Open this paper in CoChat — free accountOpen PDF

Abstract

Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Phung. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). 2018.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.18653/v1/n18-2053

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • ConvKB uses 1D convolution to maintain the translation characteristics of TransE, which is sufficient for capturing global relationships and transitional characteristics between entities.

    “Removing the reshaping operation from ConvE, ConvKB [23] uses 1D convolution to maintain the translation characteristics of TransE, which is sufficient for capturing the global relationships and transitional characteristics between entities.”

    From A Survey on Knowledge Graph Embeddings for Link Prediction (2021) · cites this paper as [23]

  • The third group of KGE models are neural-network-based models, such as ConvE, ConvKB, HypER, CompGCN, SACN and CNN-BiLSTM.

    “The third group are neural-networkbased models, such as ConvE [22], ConvKB [23], HypER [24], CompGCN [25], SACN [26] and CNN-BiLSTM [27].”

    From A Survey on Knowledge Graph Embeddings for Link Prediction (2021) · cites this paper as [23]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 36

  1. Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. 2008. Freebase: A collab- oratively created graph database for structuring hu- man knowledge. In Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pages 1247-1250.
  2. Antoine Bordes, Nicolas Usunier, Alberto García- Durán, Jason Weston, and Oksana Yakhnenko. 2013. Translating Embeddings for Modeling Multi- relational Data. In Advances in Neural Information Processing Systems 26, pages 2787-2795.
  3. Antoine Bordes, Jason Weston, Ronan Collobert, and Yoshua Bengio. 2011. Learning Structured Embed- dings of Knowledge Bases. In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelli- gence, pages 301-306.
  4. Liwei Cai and William Yang Wang. 2018. KBGAN: Adversarial Learning for Knowledge Graph Embed- dings. In Proceedings of The 16th Annual Confer- ence of the North American Chapter of the Associ- ation for Computational Linguistics: Human Lan- guage Technologies, page to appear.
  5. Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. Journal of Machine Learning Research, 12:2493-2537.
  6. Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. 2018. Convolutional 2D Knowledge Graph Embeddings. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence, page to appear.
  7. Alberto García-Durán and Mathias Niepert. 2017. KBLRN: End-to-end learning of knowledge base representations with latent, relational, and numeri- cal features. arXiv preprint abs/1709.04676. · arXiv
  8. Kelvin Guu, John Miller, and Percy Liang. 2015. Traversing Knowledge Graphs in Vector Space. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 318-327.
  9. Yanchao Hao, Yuanzhe Zhang, Kang Liu, Shizhu He, Zhanyi Liu, Hua Wu, and Jun Zhao. 2017. An end- to-end model for question answering over knowl- edge base with cross-attention combining global knowledge. In Proceedings of the 55th Annual Meeting of the Association for Computational Lin- guistics (Volume 1: Long Papers), pages 221-231.
  10. Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, and Jun Zhao. 2015. Knowledge Graph Embedding via Dynamic Mapping Matrix. In Proceedings of the 53rd Annual Meeting of the Association for Compu- tational Linguistics and the 7th International Joint Conference on Natural Language Processing (Vol- ume 1: Long Papers), pages 687-696.
  11. Guoliang Ji, Kang Liu, Shizhu He, and Jun Zhao. 2016. Knowledge Graph Completion with Adap- tive Sparse Transfer Matrix. In Proceedings of the Thirtieth Conference on Artificial Intelligence, pages 985-991.
  12. Gjergji Kasneci, Fabian M Suchanek, Georgiana Ifrim, Maya Ramanath, and Gerhard Weikum. 2008. Naga: Searching and ranking knowledge. In Pro- ceedings of the 24th IEEE International Conference on Data Engineering, pages 953-962.
  13. Yoon Kim. 2014. Convolutional Neural Networks for Sentence Classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Lan- guage Processing, pages 1746-1751.
  14. Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86:2278-2324.
  15. Yankai Lin, Zhiyuan Liu, Huanbo Luan, Maosong Sun, Siwei Rao, and Song Liu. 2015a. Modeling Rela- tion Paths for Representation Learning of Knowl- edge Bases. In Proceedings of the 2015 Confer- ence on Empirical Methods in Natural Language Processing, pages 705-714.
  16. Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015b. Learning Entity and Re- lation Embeddings for Knowledge Graph Comple- tion. In Proceedings of the Twenty-Ninth AAAI Con- ference on Artificial Intelligence Learning, pages 2181-2187.
  17. Yuanfei Luo, Quan Wang, Bin Wang, and Li Guo. 2015. Context-Dependent Knowledge Graph Em- bedding. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Process- ing, pages 1656-1661.
  18. Dat Quoc Nguyen. 2017. An overview of embedding models of entities and relationships for knowledge base completion. arXiv preprint, arXiv:1703.08098. · arXiv
  19. Dat Quoc Nguyen, Kairit Sirts, Lizhen Qu, and Mark Johnson. 2016a. Neighborhood Mixture Model for Knowledge Base Completion. In Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning, pages 40-50.
  20. Maximilian Nickel, Kevin Murphy, Volker Tresp, and Evgeniy Gabrilovich. 2016a. A Review of Rela- tional Machine Learning for Knowledge Graphs. Proceedings of the IEEE, 104(1):11-33.
  21. Maximilian Nickel, Lorenzo Rosasco, and Tomaso Poggio. 2016b. Holographic Embeddings of Knowledge Graphs. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pages 1955-1961.
  22. Michael Schlichtkrull, Thomas Kipf, Peter Bloem, Ri- anne van den Berg, Ivan Titov, and Max Welling. 2017. Modeling relational data with graph convolu- tional networks. arXiv preprint arXiv:1703.06103. · arXiv
  23. Michael Schuhmacher and Simone Paolo Ponzetto. 2014. Knowledge-based graph document modeling. In Proceedings of the 7th ACM International Con- ference on Web Search and Data Mining, pages 543- 552.
  24. Yelong Shen, Po-Sen Huang, Ming-Wei Chang, and Jianfeng Gao. 2017. Traversing knowledge graph in vector space without symbolic space guidance. arXiv preprint arXiv:1611.04642v4. · arXiv
  25. Richard Socher, Danqi Chen, Christopher D Manning, and Andrew Ng. 2013. Reasoning With Neural Ten- sor Networks for Knowledge Base Completion. In Advances in Neural Information Processing Systems 26, pages 926-934.
  26. Fabian M. Suchanek, Gjergji Kasneci, and Gerhard Weikum. 2007. Yago: A core of semantic knowl- edge. In Proceedings of the 16th International Con- ference on World Wide Web, pages 697-706.
  27. Kristina Toutanova and Danqi Chen. 2015. Observed Versus Latent Features for Knowledge Base and Text Inference. In Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Com- positionality, pages 57-66.
  28. Kristina Toutanova, Xi Victoria Lin, Wen tau Yih, Hoifung Poon, and Chris Quirk. 2016. Composi- tional Learning of Embeddings for Relation Paths in Knowledge Bases and Text. In Proceedings of the 54th Annual Meeting of the Association for Compu- tational Linguistics, pages 1434-1444.
  29. Thanh Vu, Dat Quoc Nguyen, Mark Johnson, Dawei Song, and Alistair Willis. 2017. Search Person- alization with Embeddings. In Proceedings of the 39th European Conference on Information Re- trieval, pages 598-604.
  30. Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014. Knowledge Graph Embedding by Translating on Hyperplanes. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intel- ligence, pages 1112-1119.
  31. Robert West, Evgeniy Gabrilovich, Kevin Murphy, Shaohua Sun, Rahul Gupta, and Dekang Lin. 2014. Knowledge Base Completion via Search- based Question Answering. In Proceedings of the 23rd International Conference on World Wide Web, pages 515-526.
  32. Chenyan Xiong, Russell Power, and Jamie Callan. 2017. Explicit semantic ranking for academic search via knowledge graph embedding. In Pro- ceedings of the 26th International Conference on World Wide Web, pages 1271-1279.
  33. Bishan Yang and Tom Mitchell. 2017. Leveraging knowledge bases in lstms for improving machine reading. In Proceedings of the 55th Annual Meet- ing of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1436-1446.
  34. Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2015. Embedding Entities and Relations for Learning and Inference in Knowledge Bases. In Proceedings of the International Confer- ence on Learning Representations.
  35. Fan Yang, Zhilin Yang, and William W Cohen. 2017. Differentiable Learning of Logical Rules for Knowl- edge Base Reasoning. pages 2316-2325.
  36. Yuanzhe Zhang, Kang Liu, Shizhu He, Guoliang Ji, Zhanyi Liu, Hua Wu, and Jun Zhao. 2016. Ques- tion answering over knowledge base with neural at- tention combining global knowledge information. arXiv preprint arXiv:1606.00979. · arXiv

Cited by

Sorted by citations

Authors
Nguyen, D. Q., Nguyen, T. D., Nguyen, D. Q., & Phung, D.
Affiliations
Deakin University (AU)
ORCID
Venue
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
Publisher
Association for Computational Linguistics
Published
2018
Pages
327-333
DOI
10.18653/v1/n18-2053
OpenAlex
W2774837955
Cited by
923
References
3
Citations by year
2026: 27 · 2025: 105 · 2024: 154 · 2023: 167 · 2022: 170 · 2021: 146 · 2020: 103 · 2019: 46 · 2018: 5
FWCI
30.50
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • Own your potential (DEAKIN) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
  • arXiv (Cornell University)
Funders
Australian Research Council (DP160103934)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
Keywords
knowledge base completion, embedding models, convolutional neural networks, entity embeddings, relation embeddings

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review